How Globe Testing helps startups make the leap to cloud- and mobile-first development

This latest BriefingsDirect mobile development innovation discussion examines how Globe Testing, based in Madrid, helps startups make the leap to cloud-first and mobile-first software development.

We’ll explore how Globe Testing pushes the envelope on Agile development and applications development management using HP tools and platforms.
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To learn more about modern software testing as a service we’re joined by Jose Aracil, CEO of Globe Testing, based in the company’s Berlin office. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Tell us about Globe Testing. Are you strictly a testing organization? Do you do anything else? And how long have you been in existence?


Aracil: We’re a testing organization, and our services are around the Application Development Management (ADM) portfolio for HP Software. We work with tools such as HP LoadRunner, HP Quality Center, HP Diagnostics, and so on. We’ve been around for four years now, although most of our employees actually come from either HP Software or, back in the day, from Mercury Interactive. So, you could say that we’re the real experts in this arena.

Gardner: Jose, what are the big issues facing software developers today? Obviously, speed has always been an issue and working quality into the process from start to finish has always been important, but is there anything particularly new or pressing about today’s market when it comes to software development?

Scalability is key

Aracil: Scalability is a big issue. These days, most of the cloud providers would say that they can easily scale your instances, but for startups there are some hidden costs. If you’re not coding properly, if your code is not properly optimized, the app might be able to scale — but that’s going to have a huge impact on your books.

Therefore, the return on investment (ROI) when you’re looking at HP Software is very clear. You work with the toolset. You have proper services, such as Globe Testing. You optimize your applications. And that’s going to make them cheaper to run in the long term.

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There are also things such as response time. Customers are very impatient. The old rule was that websites shouldn’t take more than three seconds to load, but these days it’s one second. If it’s not instant, you just go and look for a different website. So response time is also something that is very worrying for our customers.

Gardner: So it sounds like cloud-first. We’re talking about high scale, availability, and performance, but not being able to anticipate what that high scale might be in any given time. Therefore, creating a test environment, where you can make the assumption that cloud performance is going to be required and test against it, becomes all more important.

Aracil: Definitely. You need to look at performance in two ways. The first one is before the app goes into production in your environment. You need to be able to optimize the code there and make sure that your code is working properly and that the performance is up to your standard. Then, you need to run a number of simulations to see how the application is going to scale.

You might not reach the final numbers, and obviously it’s very expensive to have those staging environments. You might not want to test with large numbers of users, but at least you need to know how the app behaves whenever you increase the load by 20 percent, 50 percent, and so on.

The second aspect that you need to be looking at is when the app is in production. You can’t just go into production and forget about the app. You need to carry on monitoring that app, make sure that you anticipate problems, and know about those problems before your end users call to tell you that your app is not up and running.

For both situations HP Software has different tools. You can count on HP Performance Center and HP Diagnostics when you’re in preproduction in your staging environment. Once you go live, you have different toolsets such as AppPulse, for example, which can monitor your application constantly. It’s available as software as a service (SaaS). So it’s very well-suited for new startups that are coming out every day with very interesting pricing models.

Gardner: You’re based in Berlin, and that’s a hotbed of startup activity in Europe. Tell us what else is important to startups. I have to imagine that mobile and being ready to produce an application that can run in a variety of mobile environments is important, too.

Mobile is hot

Aracil: Definitely. Mobile is very hot right now in Berlin. Most of the startups we talk to are facing the same issue, which is compatibility. They all want to support every single platform available. We’re not only talking about mobile and tablet devices, but we’re also talking about the smart TVs and the wide array of systems that now should support the different applications that they’re developing.

So being able to test on multiple operating systems and platforms and being able to automate as much as possible is very important for them. They need the tools that are very flexible and that can handle any given protocol. Again, HP Software, with things such as Unified Functional Testing (UFT), can help them.

Mobile Center, which was just released from HP Software, is also very interesting for startups and large enterprise as well, because we’re seeing the same need there. Banking, for example, an industry which is usually very stable and very slow paced is also adopting mobile very quickly. Everyone wants to check their bank accounts online using their iPad, iPhone, or Android tablets and phones, and it needs to work on all of those.

Gardner: Now going to those enterprise customers, they’re concerned about mobile of course, but they’re also now more-and-more concerned about DevOps and being able to tighten the relationship between their operating environment and their test and development organizations. How do some of these tools and approaches, particularly using testing as a service, come to bear on helping organizations become better at DevOps?

Aracil: DevOps is a very hot word these days. HP has come a long way. They’re producing lots of innovation, especially with the latest releases. They not only need to take care of the testers like in the old days with manual testing, automation, and test management. Now, you need to make sure that whatever assets you’re developing on pre-production can then be reused when you go in production.

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Just to give you an example, with HP LoadRunner, the same scripts can be run in production to make sure that the system is still up and running. That also tightens the relationship between your Dev team and your Operations team. They work together much more than they used to.

Gardner: Okay, looking increasingly at performance and testing and development in general as a service, how are these organizations, both the startups and the enterprises, adapting to that? A lot of times cloud was attractive early to developers, they could fire up environments, virtualize environments, use them, shut them down, and be flexible. But what about the testing for your organization? Do you rely on the cloud entirely and how do you see that progressing?

Aracil: To give you an example, customers want their applications tested in the same way as real users would access them, which means they are accessing them from the Internet. So it’s not valid to test their applications from inside the data center. You need to use the cloud. You need to access them from multiple locations. The old testing strategy isn’t valid any more.

For us, Globe Testing as a Service is very important. Right now, we’re providing customers with teams that are geographically distributed. They can do things such as test automation remotely, and that can then be sent to the customers so they are tested locally, and things such as performance testing, which is run directly from the cloud in the same way as users will do.

And you can choose multiple locations, even simulating the kind of connections that these users are using. So you can simulate a 3G connection, a Wi-Fi connection, and the like.

Other trends

Gardner: I suppose other trends we’re seeing are rapid iterations and microservices. The use of  application programming interfaces (APIs) is increasing. All of these, I think, are conducive to to a cloud testing environment, so that you could be rapid and bring in services. How is that working? How do you see your customers, and maybe you can provide some examples to illustrate this, working toward cloud-first, mobile-first and these more rapid innovations; even microservices?

Aracil: In the old days, most of the testing was done from an end-to-end perspective. You would run a test case that was heavily focused on the front end, and that would run the end-to-end case. These days, for these kinds of customers that you mentioned we’re focusing on these services. We need to be able to develop some of the scripts before the end services are up and running, in which case things such as Service Virtualization from HP Software are very useful as well.

For example, one of our customers is Ticketmaster, a large online retailer. They sell tickets for concerts. Whenever there’s a big gig happening in town, whenever one of these large bands is showing up, tickets run out extremely quickly.

Their platform goes from an average of hundreds of users a day to all of a sudden thousands of users in a very short period of time. They need to be able to scale very quickly to cope with that load. For that, we need to test from the cloud and we need to test constantly on each one of those little microservices to make sure that everything is going to scale properly. For that, HP LoadRunner is the tool that we chose.

Gardner: Do you have any examples of companies that are doing Application Development Management (ADM), that is to say more of an inclusive complete application lifecycle approach? Are they thinking about this holistically, making it a core competency for them? How does that help them? Is there an economic benefit, in addition to some of these technical benefits, when you adopt a full lifecycle approach to development, test, and deployment?

Aracil: To give you an example of economic benefit, we did a project for a very large startup, where all their systems were cloud-based. We basically used HP LoadRunner and HP Diagnostics to look at the code and try to optimize it in conjunction with their development team. By optimizing that code, they reduced the amount of cloud instances required by one-third, which means a 33 percent savings on their monthly bill. That’s straight savings, very important.

Another example is large telecommunication company in Switzerland. Sometimes we focus not only on the benefits for IT, but also the people that they are actually using those services. For example those guys that go to their retail shops to get a new iPhone or to activate a new contract.

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If the systems are not fast enough, sometimes you will see queues of people, which turns into lower sales. If you optimize those systems, that means that the agents are going to be able to process contracts much quicker. This specific example will reduce to one-fifth of the time by using Performance Center. That means that the following Christmas, queues literally disappear from all those retail shops. That turns into higher sales for the customer.

Gardner: Jose, what about the future? What is of interest to you as a HP partner? You mentioned the mobile test products and services. Is there anything else particularly of interest, or anything on the big data side that you can bring to bear on development or help developers make better use of analytics?

Big data

Aracil: There are a number of innovations that are coming out this year that  are extremely interesting to us. These are things such as HP AppPulse Mobile, StormRunner, both are new tools and they are very innovative.

When it comes to big data, I’m very excited to see the next releases in the ALM suite from HP, because I think they will make a very big use of big data, and obviously they will try to get all the information, all the data that testers are entering into the application from requirements. The predictive test and the traceability will be much better handled by this kind of big data system. I think we will need to wait a few more months, but there are some new innovations coming out in that area as well.
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Big data detects and eradicates slavery risks from across B2B supply chains

Business networks and big data are joining to give companies new means to identify and eliminate slavery and other labor risks from across their complex global supply chains.

Data-savvy B2B participants in these networks can now newly uncover unsavory and illegal labor practices that may be hidden deep inside multi-level supplier ecosystems.

The next BriefingsDirect best business practices panel discussion focuses on how Made in a Free World, a nonprofit group in San Francisco, is partnering with Ariba, an SAP company, to shine more light across the supply chain networks to not only stem these labor practices, but also reduce the risks that companies may unwittingly incur from within their own pool of buying.

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To explain practical and effective approaches to forced-labor risk determination and mitigation, we’re joined by Tim Minahan, Senior Vice President of Ariba, and Justin Dillon, CEO and Founder at Made In A Free World in San Francisco. The panel is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Dillon: I learned about this slavery issue about 10 years ago, just reading an article in The New York Times, and really wanted to figure out a way to get more people involved. That led to Made In A Free World, a nonprofit organization that was started just few years ago.


We began with consumers, just everyday individuals getting involved and being able to leverage their own network, their personal networks. That’s now graduated into figuring out how to get businesses to leverage their own networks.

Gardner: What is the problem we are talking about? Is this just slavery? Is this breaking the law in terms of other labor regulations?

Dillon: We define slavery as anyone who is being forced to work without pay under threat of violence, being economically exploited and unable to walk away. There are over 30 million people who fall under that definition today. In some cases, these people find themselves in the sex industry, but in most cases, they’re in informal sectors, agricultural or service industries, much of which is finding its way into supply chains.

Part of what we believe is that we have to find a way to be able to connect the dots and figure out how we can use the systems that currently exist to protect the world’s most vulnerable resource.

Gardner: Tim, why is this becoming such an important issue now for nearly any business?

Minahan: Over the past decade, as companies begin to outsource more processes and manufacturing and assembly to low-cost regions, they’ve really looked to drive costs down. Unfortunately, what they haven’t done is really take a close look at their sub-tier supply chain.


So they might have outsourced a process, but they didn’t outsource accountability for the fact that there may be forced labor in their suppliers’ suppliers. That’s a real issue, getting that transparency into that problem, understanding whether there’s a potential risk for the threat of forced labor in a sub-tier supply chain.

Gardner: How big of a problem is this in terms of scope, depth and prevalence? Is this something that happens from time to time in rare instances or is this perhaps something that’s quite a bit more common than most people think?

Minahan: Dana, this is far more pervasive than most people think. Slavery really has no boundaries. There are incidences of forced labor in all industries, from conflict minerals in the Congo to fishing in Malaysia to, unfortunately, migrant workers right here in the United States.

Palm oil problem

Dillon: It’s really not a problem that “exists over there.” Just one statistic alone: By 2020, half of all the products you can purchase in a grocery store will have a commodity, palm oil, which has a huge incidence of forced labor, particularly around the Malaysia region and Indonesia.

This small commodity, mostly because it’s so easy to pick and harvest, is now finding its way into a myriad of products — and that’s just in agriculture.

Gardner: Why should companies be concerned? Isn’t that someone else’s issue, if it’s below the level of their immediate reach?

Minahan: You can certainly outsource process or manufacturing, but you can’t really outsource accountability. Secondly, there is a big movement afoot from regulators, both here in the United States, Federal laws, California state laws, as well as overseas in the UK to hold companies accountable, not just for their first-tier supply chain, but for their sub-tier supply chain.

Gardner: And, Justin, of course visibility being so prominent now — a camera around every corner, social media — people can easily react and irreparable damage to a company’s reputation. Do you have any examples of where this has come to bear, where not knowing what’s going on within your supply chain can be such an issue economically and otherwise?

Dillon: Well, people love to hate the ones they love. So everyone is complaining about Apple products, while they’re using their Apple iPhone. But in a recent article in The New York Times, one of the supply-chain folks for Apple said that the days of sloppy globalization is over and they’re taking this quite seriously. I would argue that some Apple’s work is some of the most innovative human rights work that we’ve seen.

They’ve dug deeper down into the sub-tier suppliers and, if their reputation as innovators has anything to do with their products, it certainly has to do with their supply chain as well. They are a great example of a company who sees these challenges, sees the connection to not only their brand, but also to their products and they’re taking some remediation work against it.

Gardner: Okay, we understand the depth of the issue and why it’s important. Now, now how do we do get the tools to combat this effectively? What have you done? I understand you have a database, and it’s ongoing. Tell me a little bit about the tools that you’re bringing to bear to solve this problem and to help companies take better accountability.

Dillon: The most important thing to do first is to realize that we aren’t operating in the 1990s anymore, in the sweatshop era. That was an era that where we found problems in supply chains and we started to build solutions around that, auditing, monitoring, all the rest of that. That was 25 years ago.

Era of big data

We’re now in the 21st Century, in the era of big data, and we need to be able to use those tools to be able to combat 21st Century problems. For us as an organization, we’ve realized this was a space where we could be helpful to business. Our mission is to use the free market to free people. That’s what we do as an organization.

We’ve realized that there is a lot of data missing, and there is a lot of synthesis of data missing. So we, as an organization, decided to pull together the bible of databases, when it comes to supply chains, the UNSPSC. Then, we built off of that taxonomy a risk analysis on every single thing good, service, or commodity that can be bought or sold. That becomes the lingua franca, so to speak, when it comes to supply chain, risk management.

Gardner: How does it work? When a company comes in and gives you information about who their suppliers are, how do you come up with inference, insight and some sort of a predictive capability that identifies the risk or the probability that they could be in trouble?

Dillon: We are using all the best databases that currently exist on the issue. Everything from forced-labor databases to child-labor databases to rule-of-law, governance, migration, trade flows. All of that is synthesized into an algorithm that can be applied to any individual’s spend data. You’re able to get a dashboard on all that spend, which gives you some optics into your sub-tier suppliers, which is where we need the optics. It’s not a crystal ball, but it’s the next best thing.

That database and analysis are now available to anyone, any size, any sector, to leverage their influence to the extent that they can. We’ve recognized that you can’t be everywhere at all times but you’re somewhere at some times and that’s the place we feel like any company can make the greatest influence.

Gardner: Now, Tim, the labor issues that we’re talking about are certainly a risk issue, but at the same time, we’re looking to find more ways to automate and make visible other types of risk, any kind of risk, in the supply chain in procurement, buy-and-sell environment.

Tell us a little bit about how this particular risk fits into a larger risk category, and then how the databases for each of them, or many of them, come together for a whole greater than the sum of the parts.

Minahan: As Justin said, the information here is shining a light on a particular issue, being able to mass, for the very first time, data points from hundreds of different data sources around the world to predict and project the threat of forced labor.

Extending that further, that’s one risk indicator that companies need to manage. Companies are managing a whole host of sustainability issues around social and eco-responsibility, but also financial risk, and threat of disruption risk.

Being able to pull all those together into a common risk factor is what we’re attempting to do through the power of business networks. By connecting the world’s businesses and connecting their supply chains and automating their processes, that was phase one.

Phase two is harnessing all the information from all of those interactions to provide a better level of visibility that raises that transparency for everyone. Then, you can predict future risk. Being able to tap into what Made In A Free World has done in this particular area, pull that into the network, and expose that as another risk factor that companies can evaluate is a very powerful opportunity for us together.

Gardner: As we’ve seen in other aspects of the networked economy, the better the data, the more influence, the more action as a result of that data, that encourages more people to see value and add more data back into the pool and so on and so forth. Tell us about the news you made in April at the Ariba LIVE conference and how that works into this notion of a virtuous adoption cycle.

New partnership

Minahan: Ariba and Made In A Free World announced a new partnership to bring the power of the Made In A Free World database together for predictive analytics of forced labor with the power of the Ariba toolset and the Ariba Network to help the Global 2000 and beyond be able to have the transparency they need to identify potential risk, in this case, of forced labor and their supply chain, and be able to take action and rectify it.

Gardner: Justin, how do you see this announcement benefiting your cause?

Dillon: Well, it moves us past the point of saying there is nothing that you can do as a company. That is no longer an option on the table. What you’re going to do is the next question. We have removed the word “if” in this conversation — and we couldn’t have done that without Ariba.

And we can see how the Ariba Network is going to be helpful, because companies are coming to us and coming at the federal government saying, “What are we supposed to do? How do we move forward? This seems like a huge problem.” They’re right, and the solution is right in front of them.

Gardner: Tim, for organizations that are now more aware of this problem, and of the general risk issue across supply chains, how do they start? Where do they go to begin the process for getting better control that allows them to have better accountability?

Minahan: That’s exactly what Ariba and Made In A Free World are trying to do —  provide the tools necessary for companies to get started. We mentioned together the technical solution that we ‘re bringing to bear to allow folks within the Ariba community to be able to access the Made In A Free World input and be able to project potential issues of forced labor in their supply chain.

Together, through that effort, we’ve developed a playbook that provides suggested guidelines for folks to get started. You can’t fix what you don’t know about. You can’t improve without having some understanding of where you fit in the mix. We believe that data and doing an analysis on the freedom platform is the best way for a company to get started.

We think it’s the best synthesis of practices, data, influence, and the network effect that anyone can begin to take. We suggest that they start to look at their own risk based on what they are buying and based on their own exposure. Every company that comes to us, we’re able to do this risk analysis with them, and they’re are getting new insights.

In addition, there is a whole ecosystem of companies that are really looking at this issue hard. The thing that’s most exciting about it is that everyone is very transparent and they’re willing to share. So you have companies from Patagonia to Apple and the like that are sharing their practices freely, because this is an issue that we want to address. The business world has the opportunity to address probably the most serious issue facing us today, and that is modern slavery.

Learning more

Gardner: It strikes me that this is a game of numbers. If enough companies examine their supply chain sufficiently and then eradicate the areas where there is trouble, the almighty Dollar, Peso, or what currency it may be, comes to bear, and things like corruption don’t work and bad labor practices don’t get supported. Is this a rolling-thunder sort of thing. If so, what’s the timeframe? Is this something that can be solved in a fairly short amount of time if people actually come together and work on it?

Dillon: To me, it’s the greatest story ever told. This is the thing that we all appreciate, and frankly we all benefit from it. We’re all benefiting from a free market and the way that we look at change in our organization is that we say that change is more about judo than karate. How can we use the force against itself to actually change it, and the marketplace is the way to do that.

So yes, this can move quite quickly. In my opinion, it’s much quicker than governments can move. We think that they’re going to be followers in this case and we highly expect them to be followers.

Gardner: Tim, if companies do this right, if they leverage big data, if they examine risk properly, what do they get, other than of course the direct reduction of that particular risk? It strikes me that we are not just tackling risk, but we’re putting into place systematic ability to manage risk over time, which is way more powerful. Is that what we’re going to get?

Looking at risk

Minahan: Absolutely. Companies are looking at risk holistically, and this is one key vital input into that. As they continue to address that and, as Justin said, we’re using free markets as a powerful lever to free people. They can, through their buying patterns and their buying policies, begin to adjust the market, shine a light not just on their own supply chain but change the sub-tier supply chain practices to make sure that there is fair labor. It becomes unappealing to have forced labor and it becomes a detriment to their ability to win new business by doing that.

That’s really what the power of data and the power of business networks can deliver in this scenario.

Dillon: Businesses are the heroes in the story. It’s not the non-profit, and it’s not government. Anytime you associate business with human rights, businesses are often qualified as Darth Vader who actually think that they are Luke Skywalker in the story. And for us an Ariba, I don’t need to put that on them, but we are just Yoda.

We’re giving them the tools that they need to fight the evil empire and we are going to do it together and celebrate it together. But this is one thing that we’ll have to do in concert. We all have individual roles to play, and business has a very clear, distinct role to play.

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ECommerce portal Avito uses big data to master rapid fraud detection

This BriefingsDirect big data innovation discussion examines how Avito, a Russian eCommerce site and portal, uses big data technology to improve fraud detection, as well as better understand how their users adapt to new advertising approaches.

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To learn more about how big data offers new insights to the eCommerce portal user experience, BriefingsDirect sat down with Nikolay Golov, Chief Data Warehousing Architect at Avito in Moscow. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: It sounds like Avito is the Craigslist of Russia. Tell us a little bit about your site and your business.

Golov: Yes, Avito is a Russian Craigslist. It’s a big site and also the biggest search engine for some goods. We at Avito have more searches, for example, from iPhones than Google or Yandex. Yandex is a Russian Google.

Gardner: Does Avito cover all types of goods, services, business-to-business commerce?

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Golov: You can sell almost anything that can be bought in the market on Avito. You can sell cars, you can sell houses, or rent them, for example. You can even find boats or business jets. We right now have about three business jets listed.

Size advantage

The main advantages of Avito is, firstly, its size. Everybody in Russia knows that if you want to buy or sell something, the best place for it is Avito. It’s first.


Second is speed. It is very easy to use it. We have a very easy interface. So we must keep these two advantages.

But there are also some people who want to use Avito to sell weapons, drugs, and prohibited medicines. It’s absolutely critical for Avito to keep it all clean, to prevent such items from appearing in the queries of our visitors.

We’re growing very fast, and if we use moderators we’ll have to increase our expense on moderation in a linear progressions as we grow. So, the only solution to avoid a linear increase in expenses is to use automation.

Gardner: In order to rapidly decide which should or should not be appearing on your site, you’ve decided to use a data warehouse that provides a streaming real-time data automation effect. What your requirements are for that technology?

Golov: We need to be able to perform fast fraud detection. The warehouse has to have very little delay.

Second, we have to have data for long periods of time to learn our data mining algorithms — to create reports, and to analyze trends. So our data warehouse has to be very big. It has to store months, possibly years, of data. It has to be fast, or only slightly delayed, and it has to be big.

Third, we’re developing very quickly. We’re adding some new services, and we’re integrating with partners. Not long ago, for example, we added information from Google AdWords to optimize banners. So the warehouse must be very flexible. It must be able to grow in all three ways.

Gardner: How long have you been using HP Vertica and how did you come to choose that particular platform?

Golov: Well over a year now. We chose Vertica for two two main advantages. First, speed of load and data. The I/O speed provided by Vertica is awesome.

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Second is its ability to upgrade, thanks to the commodity hardware. So if you have some new requirements that require you to increase performance, you can just buy new hardware — commodity hardware — and its power just increases.

It’s great and it can be done really fast. Vertica was the winner.

Measuring the impact

Gardner: Do you have a sense of what the performance and characteristics of Vertica and your data warehouse have gotten for you? Do you have a sense of reduced fraud by X percent or better analytics that have given you a business advantage of some sort? Are there ways to measure the true impact?

Golov: Avito grew really fast over the past year. We have a moderation team of about 250 people at the beginning of this process. Now, we have the same moderation team, but the number of items has increased two-fold. I suppose that’s one of the best measures that can be used.

Gardner: Fair enough. Now, looking to the future, when you’re working in a business where your margins, your business, your revenue comes from the ability to provide advertisement placements, improving the performance and the value on the actual distribution of ads and the costs associated is critical.

In addition to rapid fraud detection and protection, is there a value from your analytics that refines the business algorithms and therefore the retail value to your customers?

Golov: We’re creating more products. The main aim of them is to create our own tool for optimizing the directions of advertising. We have banners, marketing campaigns, and SMS. So we’ve achieved some results in our reporting and in fraud prevention. We’ll continue to work in that direction, and we are planning to add some new types of functionality to our data warehouse.

Gardner: It certainly seems that a data warehouse delivers a tactical benefit but then over time moves to a strategic benefit. The more data, inference, and understanding you have of your processes, the more powerful you can become as a total business.

Golov: Yes. One of my teachers in data warehouses explained the role of data warehouses in an enterprise. It’s like a diesel engine inside a ship. It just works, works, and works, and it’s hot around it. You can create various tools to increase it, to make it better.

But there must always be something deep inside that continuously provides all of the associated tools with power and strong data services from all sides of the business.

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Gardner: I wonder for others who are listening to you and saying, “We really need to have that core platform in order to build out these other values over time.” Do you have any lessons that you have learned that you might share. That is to say, if you’re starting out to develop your own data warehouse and your own business intelligence and analytics capabilities, do you have any advice?

Be flexible

Golov: First, you have to be flexible. If you ask a business about changing, they’ll tell you that they can’t. It will be absolutely this, every time. And in two months, it will still change. If you’re not ready to change using your data warehouse to get needed data and analytics, it would be a disaster. That’s first.

Second, there always will be errors in data, there will be gaps, and it’s absolutely critical to start building a data warehouse together with an automated data quality system that will automatically control and monitor the quality of all the data. This will help you to see the problems when they occur.

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Enterprise Architecture pioneer John Zachman on gaining synergies among the major EA frameworks

What is the relationship between the major Enterprise Architecture (EA) frameworks? Do they overlap, compete, support each other? How? And what should organizations do as they seek a best approach to operating with multiple EA frameworks?

These questions were addressed during a February panel discussion at The Open Group San Diego 2015 conference. Led by moderator Allen Brown, President and Chief Executive Officer, The Open Group, the main speaker John Zachman, Chairman and CEO of Zachman International, and originator of the Zachman Framework, examined the role and benefits of how EA frameworks can co-exist well. He was joined by Steve Nunn, vice president and chief operating officer of The Open Group.

Download a copy of the full transcript. [Disclosure: The Open Group is a sponsor of BriefingsDirect podcasts.]

Here are some excerpts:

Zachman: A friend of mine recently did a survey of 108 CEOs, around mostly North America. I was shocked when I saw that the survey said that the biggest problem facing the enterprise is change.


And when I heard that, my reaction was, well, if the CEO thinks the biggest problem facing the enterprise is change, where is the “executive vice president in-charge of change management”? If nobody is in-charge, the high probability is low to zero that you are going to be able to accommodate change.

There are two reasons why I do architecture. One is complexity, and the other one is change.

Create the architecture

If you want to change something that already exists, you are going to have to have architecture — one way or another. And you have to create that architecture.


Now, the reason that I am saying this is if 108 out of 108 CEOs — of course those were high visibility CEOs — said the biggest problem facing the architecture is change, who ought to own the Enterprise Architecture? I would submit it has to be a general management responsibility.

It needs to be an executive vice president. If the CEO thinks the biggest problem facing the enterprise is change, the CEO ought to be in-charge of Enterprise Architecture. If he or she is not going to be in-charge, then it ought to be the person they see every morning when they come in the office … “Hey, Ralph, how is it going on the architecture?” So it probably should be a general management responsibility. That’s where I would take it.

I put TOGAF® together with my Zachman framework and, in fact, I kind of integrate them. In fact, I have known Allen Brown for a number of years, and he was in Johannesburg a number of years ago. He introduced me to a TOGAF conference and he said, “For a lot of years, I thought it was either Zachman or TOGAF.” He said that’s incorrect. “Actually it’s Zachman and TOGAF.”

That basically is where I am going to take this: It’s Zachman and TOGAF. How then would you integrate TOGAF and The Zachman Framework, which I obviously think is where we want to go to?

The first question turns out to be what is architecture? What is it? Some people think this is architecture: the Roman Colosseum. Some people think that is architecture.

Now, notice that is a common misconception. This is not architecture. This same misconception about enterprise is what leads people to misconstrue Enterprise Architecture as being big, monolithic, static, inflexible, and unachievable and it takes too long and costs too much.

If you think that is architecture, I am going to tell you, that’s big and monolithic, and static. It took a long time and it cost a lot of money. How long do you think it took them to build that thing? Not a day, not a week, not a year, not a decade. It was a couple of decades to build it.

In fact, the architecture had to be done long before they ever created the Roman Colosseum. They couldn’t have even ordered up the stones to stack on top of each other until somebody did the architecture. The architecture had to be done long before they ever created the Roman Colosseum.

Result of architecture

Now, that is the result of architecture. In the result, you can see the architect’s architecture. The result is an implementation and instance. That is one instance of the architecture. Now, they could have built a hundred of these things, but they only built one.

I was in New Zealand a few years ago and I said that they could have built a hundred of these things, but they only built one. Some guy in the back of the room said they actually built three. I said I didn’t know that. He even knew where they were. I was really impressed.


I was in Rome last June and I am talking to these guys in Rome. I said, you guys could have built a hundred of these things, you only built three. And the guys in Rome said, “We built three; I thought we only built one.” Actually I felt a lot better. I mean, you can build as many as you want, but this just happens to be one instantiation. And in fact, that is not architecture. That’s just the result of architecture.

Architecture is a set, it’s not one thing, it’s a set of descriptive representations relevant for describing a complex object (actually, any object) such that an instance of the object can be created and such that the descriptive representations serve as the baseline for changing an object instance (assuming that the descriptive representations are maintained consistent with the instantiation). If you change the instantiation and don’t change the descriptive representations, they would no longer serve as a baseline for ensuing change of that instantiation. In any case, architecture is a set of descriptive representations.

Now, you can classify those descriptive representations in two dimensions. One dimension is what I call it Abstractions. I don’t want to digress and say why I happened to choose that word. But if you look at architecture for airplanes, buildings, locomotives, battleships, computers, tables or chairs, or XYZ, they are all going to have Bills of Materials that describes what the thing is made out of.

You have the Functional Specs that describe how the thing works. You have the Drawings or the Geometry that describes where the compound is relative to another. You have the Operating Instructions that describes who is responsible for doing what. You have the Timing Diagram that describes when thing happens, and the Design Objectives that describes why they happen.

So it doesn’t make any difference what object you are looking at. They are going to have bills of material, Functional Specs, the Drawings or Geometry or Operating Instructions and so on. You are going to have that set of descriptive representations.

Now, they didn’t happen to stumble across that by accident. Basically, they are answering six primitive interrogatives: what, how, where, who, when and why. That’s been known since the origins of language about 7,000 years ago. And the linguists would observe for you that’s the total set of questions you need to answer to have a complete description of whatever you want to describe; a subject, an object, or whatever.

It’s okay if you don’t answer them all, but any one of those questions that you don’t answer, you are authorizing anybody and everybody to make assumptions about what the answers are that you don’t make explicit. So if don’t make those answers explicit, people are going to make assumptions. You are authorizing everybody to make assumptions.

The good news is, if the assumptions are correct, it saves you time and money. On the other hand, if the assumptions are incorrect, that could be horrendous, because incorrect assumptions are the source of defects. That’s where defects are, or miscommunications, or discontinuity. That’s where you have the defects come from. So you don’t have to answer all the questions, but there is a risk associated with not answering all the questions.

And I did not invent that, by the way. That’s a classification that humanity has used for 7,000 years. Actually, it’s the origins of language basically. I did not invent that. I just happened to see the pattern.

Parts and part structures

Now, there is one other thing I have to tell you, in a Bill of Materials, you have descriptions of parts and part structures. There is no expression of functional specification in the Bill of Materials, there is no expression of Drawings in the Bill of Materials, nor expression of Operating Instructions. There is no expression of Time or Design Objectives. There are parts and part structures.

In the Functional Specs there is Functional Specs. There is no expression of parts or part structures. There is no expression of Geometry or Drawings. There is no expression of operating responsibility, time, or Design Objectives. There are Functional Specs.

In the Geometry, there is no expression of parts and part structures, there is no expression of Functional Specs, operating responsibility, time, or Design Objective. There are the Drawings or the Geometry.

I am not going to do it anymore; you get the idea. If you are trying to do engineering kind of work, you want one, and only one, kind of thing in the picture. You start putting more and more kinds of thing in the picture, and that picture is going to get so complicated that you will never get your brain around it actually.

And if you are going to do engineering work, what you want to do is normalize everything. You want to minimize any potential discontinuity, any kind of disorder. You want to normalize everything. In order to normalize everything you have to see all the parts relative to the whole object. You have to see them all, so that you can get rid of any re-occurrence or any kind of redundancy.

You want to normalize, get the minimal possible set. You only want to look at all the Functional Specs, but you want to look at it for the whole object. Get it down to minimize, minimize the complexity. You want to minimize the redundancy. You don’t want any redundancy showing up and so on. You want to minimize everything. Minimum possible set of components to create the object.

You don’t want extraneous anything in that object, whatever that object happens to be,  airplane, building, or whatever. So I just made that observation.

Now I am going to digress. I am going to leap forward into the logic a little bit for you. There is the engineering view. If you want to do engineering work, you want to see the whole. You only want to see one type of fact, but you want to see the whole set for the whole object, for the whole product.

So when you are doing engineering work, you want to see the whole thing, because you have to see how all the parts fit together basically.

Now, if you are doing manufacturing work, however, that’s not what you need. You want to take one part, you want to decompose the object down to as small parts as possible and then you want to see the characteristics. You take one part and you need to know the part or part structure. You have to know the functionality for that part, you have to know the Geometry for that part, the operating responsibility, for that part, the Timing Diagram for that part, and the Design Objective for that part. So if you are doing manufacturing, you want to see all the variables relative to one part.

Different models

There are two different kinds of models that are required here. You want the engineering models, which are, in fact, a normalized set. You want to see one fact for the whole object. And the manufacturing model, you want to see for one part. You want to see all the variables for one part. So there are two different kinds of descriptive representations that are relevant for describing the object.

Now, I would just observe this, engineering versus manufacturing. Engineering work requires single-variable, ontologically defined descriptions of the whole of the object, which I would call a primitive.

In contrast, manufacturing work requires multi-variable, holistic descriptions of parts of the object, what I would call a composite. That’s the implementation; that’s the composite.

The interesting phenomenon is — and somebody talked about this yesterday too — in manufacturing, this is analysis. You break it down into smaller and smaller pieces. In fact,  it’s a good approach if you want to classify, you want to deal with complexity. The way humanity deals with complexity is through classification.

A one-dimensional classification for manufacturing is to decompose it down to various small parts. The reason why that becomes useful, is that it’s cheaper and faster to manufacture the part. The smaller the part, the faster and cheaper it is to manufacture it.

So basically if you go back to The Wealth of Nations by Adam Smith, the idea was to break it down into smaller parts so you can manage the parts, but in doing that, basically what you are doing is you are disintegrating the object, you are disintegrating it.

In contrast, in engineering work, you need to look at synthesis. It you take a one-dimensional classification, you are disintegrating the object. The same content can show up in more than one category, the bottom of the tree. If you want to do engineering work, you want to see how all the parts fit together. That’s a synthesis idea.

So if you just do analysis, and you are doing manufacturing or implementation work, you are going to get disintegration. If you are doing engineering work, you want to deal with the issue of synthesis.

So it’s not an either-or thing; it’s an “and” kind of a thing. And the significant issue is that this is radically different. In fact, it was Fred Brooks who said, programming is manufacturing, not engineering. So those of us who come from the IT community have been doing manufacturing for the last 65 or 70 rears basically. In contrast, this is different; this is a standard. This stuff appears radically different.

So the reason why we build implementations and we get frustration on the part of the enterprise is because the implementations are not integrated, not flexible, not interoperable, not reusable, and not aligned. They are not meeting expectations. Fundamentally, if we use a one-dimensional classification, you’re going to end up with disintegrating the thing. It’s not engineering. It’s implemented, but not engineered.

Two-dimensional classification

If you want the thing to be engineered, you have to have a two-dimensional classification. You have to have a schema, a two-dimensional classification, because you have to have two-dimensional in order to normalize things.

I don’t want to digress into that, but Ted Codd was floating around with the relational model. Before Ted Codd and the relational model, we didn’t even have the word normalization at that point in time. But to try to manage the asset you are trying to manage, you have to have a normalized structure.

If you want to manage money, you have to have chartered accountants. If you want to manage an organization, you have to have allocation responsibilities. If you want to manage whatever you want to mange, you have to have a normalized structure.

So if you want the thing to be engineered, integrated, flexible, interoperable, reusable, and so on, then you have to do the engineering work. Those are engineering derived characteristics.

You don’t get flexibility, integration and so on from implementation. Implementation is what you get, which is really good. I am not arguing; that’s really good, but on the other hand, if you need integration, flexibility and so on, then you have to do engineering work. So it takes you beyond merely the manufacturing and the implementation.

I gave you one dimension of the classification of descriptive representations, which I called abstractions; the other dimension I call perspectives. Typically, I would take a few minutes to describe this for you, but I’m just going to kind of net this out for you.

Back in the late 1960s time frame, we had methodologically defined how to transcribe the strategy of the enterprise, but we had to transcribe it. We knew at the time we had to transcribe it in such a fashion that you can do engineering work with it.

It’s not adequate to transcribe the strategy in such fashion to say make money or save money or do good or don’t do, whatever, or feel good, or feel bad, or go west or go east. Those are all relevant, but you have to take those apart to create the descriptive representations in such a fashion that we can do engineering work with it.

These are in the late 1960s time frame. We knew how to transcribe this strategy in such a fashion that we could do engineering work. What we didn’t know is how to transform that strategy into an instantiation such that the instantiation bears any resemblance to what the strategy was fundamentally.

So the problem is that in those days, we tended to describe this in a somewhat abstract fashion: make money or save money, whatever, but down here, you’re telling a person how to put a piece of metal in a lathe and then how to turn it to get whatever you’re trying to create. Or it could be telling a machine what to do, in which case you’re going to have a descriptive representation, like 1,100 and 11,000. So it’s a long way from “make money” to “11,000.” We didn’t know how to make the transformation of the strategy to the instantiation, such that the instantiation bears any resemblance to the strategy.

We knew architecture had something to do with this, but, if you go back to the late 1960s time frame, we didn’t know what architecture was.

Radical idea

I had a radical idea one day. I said, “What you ought to do is ask somebody who does architecture for something else, like a building, an airplane, a computer, an automobile, or XYZ. Ask them what they think architecture is.” If we could figure out what they think architecture is, maybe we can figure out what architecture is for enterprises. That was my radical idea back in those days.

A friend of mine was an architect who built buildings actually. So I went to see my friend Gary, Gary Larson, the architect, and I said, “Gary, talk to me about architecture.” He said, “What do you want to know?” I said, “I don’t know what I want to know; just talk to me and maybe I’ll learn something.”

He said, “Somebody came in my office, and said I want to build a building.” I said, well, what kind of building do you have in mind? Do you want to work in it? Do you want to sell things in it? Do you want to sleep in it? Is it a house? What are you going to do with it? Are you going to manufacture things in it? What’s the structure of it: steel structure, wood structure, stucco, glass, or whatever?

I have to know something about the footprint. Where are you going to put this thing? What’s the footprint? I have got to know what the workflow is. If you’re going to eat in the thing and sleep in the thing, you put the eating and the cooking near each other, you put the sleeping someplace else. You have to know something about the workflows.

I have to know something about the Timing Diagrams. Am I going to design a building that has elevators. It has an up button, you go up, and the down button, you go down. I have to know something about the Design Objectives.

Do you want to change this building, you want flexibility. If you want to change this building after I get a bill, then don’t hard bind the wall to the floor. Separate the independent variables. If you want flexibility, you separate the independent variables.

By the way, we learned about that a long time ago, those of us who are in IT; separate the independent variables. I haven’t heard this for 30 or 40 years, but it’s like binding. You don’t want to bind anything together.

You want to bind independent variables together so you collect relationship knowledge. That’s why you bind them together, because as soon as you fix two things together, independent variables, if you want to change one, you have to change them all — throw the whole thing and you have to start over again.

So if you want to change things, you separate the independent variables. How do you like this for an idea by the way? You have the data division and a procedure division? That’s pretty interesting. You can change one data element and all the instructions. You want to change one data element and all the instructions. So you separate the independent variables if you want to change them.


Now, for manufacturing purpose, you want to hard bind them together. That’s the implementation.

So Gary says, “I have to know whether they want flexibility or whatever. I have to know the Design Objectives. sketch up my bubble charts. I have to understand what the boundaries are here, so I don’t get blindsided in effect.”

“If I’m going to build a 100-story building, a huge building, then I’ll live with the owners for a couple of years, so I find their aesthetic values, what they’re thinking about, what their constraints are, what they really want to do, how much money they have, what their purpose is. I have to understand what the concept of that building is.”

“I transcribe the concepts of the building. And this is really important. I can take this down to an excruciating level of detail. Actually, I have to build the scale model. It has light bulbs that go on or off. I have water that runs through the pipes. I can build a scale model, so that the owners can look at this and say, ‘That is really great; it’s exactly what I had in mind’, or ‘Whoa, it’s not what I had in mind.'”

“It’s really important because if the owner. If this is what they have in mind and they say, ‘Okay, chief, sign here, press hard on the dotted line, you have got to go through three copies.'”

“I have an architect friend right now, who’s in the middle of a massive lawsuit. The owners of the building did not want to sit down and define these models up here. They said, ‘You know what we have in mind so go ahead and define it. We don’t have the time to think about this or whatever.'”

“So the architect defined these models, then they transformed it into the instantiation. He built the building, but it’s not what the owner had in mind. And it’s a massive lawsuit.

“I said to my architect friend, ‘I went out to your website and I figured out, I found out why you’re having this lawsuit. They were not involved in defining what the concepts are.”

Now, Gary would say, “Once I get the concepts, I have to transform those concepts into design logic for the buildings, because I haven’t got the building design, I only have the concepts transcribed. Now I have to deal with pressure per square inch, metallurgical strength, weight of water to move the water around. I have to deal with earthquakes. I have got to deal with the whole bunch of other stuff.”

“I may have some engineering specialization to help me transform the requirement concepts into the engineering design logic.” In manufacturing, they call this the as-designed representation. Gary called that the architect’s plans. He called these architect’s Drawings. He called these the architect’s plans.

“Now, I have to get the architect’s plans. “I have to negotiate with the general contractor, because the general contractor may or may not have the technology to build what I have designed. So I have to transform the logic of the design into the physical design. I have got the schematics here, but I have to have the blueprints.”

Making transformations

“So we have to negotiate and make the transformations, have some manufacturing engineers help me make that transformation. And in manufacturing they would call this as designed and this as planned.”

“I make the transformation, so the implementation. They have the technology to implement, the design. Then, this contractor goes to the subcontractors who have the tooling, and they have to configure the tools or express precisely what they want somebody to do in order to create it and then you build the building.”

That’s pretty interesting. You notice, by the way, there are some familiar words here: concepts, logic, and physics in effect. So you have the owner’s view thinking about the concept; the designer’s view thinking about the logic; and the builder’s view thinking about the physics in effect. You have the concepts, the schematics, and the blueprints. Then you have the configuration and the instantiation. That’s the other dimension of a classification.

Now, there is a two-dimensional classification structure. That’s an important idea. It’s a really important idea. If you want to normalize anything, you have to be looking at one fact at a time. You want to normalize every fact. You don’t want anything in there that’s extraneous. You want to normalize everything.

So it’s a two-dimensional schema, not a one-dimensional schema, not a taxonomy or a hierarchy or a decomposition; this is a two-dimensional schema.

If you folks go back to the origins in the IT community, the original databases typically were either flat files or hierarchical databases. They’re not any good for managing data; they’re good for building systems. You break it down, decompose it onto small parts, and they’re good for building systems. They’re not good for managing data.

So then you had to have a two-dimensional classification and normalization. Ted Codd showed up, and so on. I don’t want to digress into that, but you get the idea here. It’s a two-dimensional classification.

And I was in Houston at one time, talking about the other dimensional classifications. Some guy in the back of the room said, “Oh, that’s reification.” I asked what that was. Reification? I never heard the word before. It turns out it comes out of philosophy.

Aristotle, Plato, and those guys knew the ideas that you can think about are one thing but the instantiation of the ideas is a completely different thing. If you want the instantiation to bear any resemblance to what the idea is, that idea has to go through a well-known set of transformations.

You have to identify it and name it. So you’re going to have to dialogue about it. Then you define it and you have the semantic structures. They have to have their representations — all the interior designs are done with representations — and then you have to specify it based upon the implementation technology. Then, you configure it based upon the tooling and then you instantiate it. And if it goes through that set of well-known transformations, then the end result will bear some resemblance to the outset.

Set of transformations

If you don’t go through that, you may or may not look out, and say, “A blind thing finds a solution every now and then.” Well that’s pretty good, but on the other hand, you won’t have any degree of assurance that whatever you’re going to end up with bears any resemblance to what you had in mind of the outset. It has to go through that set of transformations.

By the way, I didn’t define those; those came out about a couple of thousand years ago as reification. The etymology of the word “re” is Latin; it means thing. So you’re taking an idea and transforming it into a thing. That’s the other dimension of classification in any case.

This is the framework for Anything Architecture, okay? They are going to bills of material, the Functional Specs, the Geometry, or Drawing, Operating Instructions, Timing Diagrams, Design Objectives. That’s one dimension. For the other dimension, you have the scoping representation, the boundaries, requirement concepts, the design logic, the plan physics, the tooling configurations, and then you get the instantiations. So that’s the framework for Anything Architecture.

And I don’t care whether you’re talking about airplanes, buildings, locomotives, battleships, tables, chairs, or whatever. It’s anything in effect. That’s just a framework for Anything Architecture.

Now all I did was I put enterprise names on the same descriptive representation relevant prescribing anything.

Okay, we produced a Bill of Materials, too. We would call these the Inventory Models, actually that’s the business name for them. The technical name would be Entity Models. Now what’s an entity, what’s a set? What’s important about sets? Well how many members are in the set? Are they all present or not? It is actually that the business cares less about entity. They don’t care about entity; they care about inventories.

So let’s call them by their business name. It’s the Inventory Model. The technical name is be Entity Model, but there is Inventory Model. Now the system Entity Model would be the logic entity. In fact, we would call it a Logical Model, but that would be sitting right there. But the Bill of Materials we would call them Inventory Models.

The Functional Specs we call it the Process Models, those are processes. It takes on something different, or the input process output.

The Drawings or the Geometry we would call the Geography, the distribution models, the locations where you store things and transport things around. That would be the distribution models or the Geometry of the enterprise. Maybe Geography would be our name.

The Operating Instructions, we call the Responsibility Models, the workflow models. You know what responsibilities are going to assign to various roles within the enterprise; responsibility of workflow.

The Timing Diagrams, we would call Timing Models. Some people say the Dynamics Models. Jay Forrester at MIT basically wrote the book Industrial Dynamics in 1959. They were tracing resource flows in the enterprise. They were using manufacturing concepts in human systems and so they call the Dynamics Model, but a lot of times we will call them Timing Models.

Motivation models

The Design Objectives we might call motivation models. So all I was doing was putting enterprise names on the same concepts. By the same token the Scope Contexts we would call Scope Lists. We are just scoping out. Give me a list of inventory, give me a list of processes.

The Requirement Concept, we would call Business Models; those are models of the business. And the design logic, we call system models. Those are the Logic Models, they are the System Models and we call systems.

The plan physics we call technology models, the technology constraint. The part configuration, we call tooling models and then product instances we call the enterprise implementation.

The enterprise is sitting down here. Actually all this is architecture, but the instantiation is down here.

Allen Brown made some really good observations about business architecture. I have a whole other observation about business architecture. Now the question is when you say business architecture, what do you mean?

I was talking at a big business architecture conference. They were having animated discussions and they were getting real passionate about it, but the fact of the matter is they weren’t defining business architecture the same way; they were all over the board.

I said this yesterday. I calculated 176 different plausible definitions for business architecture. For those guys, you could be talking about any one of those, but if you don’t define which one you are talking about, whoever you’re talking to may be hearing any one of the other 175. So you have to get definitive about it, or else you are like freight trains passing in the night.

I will tell you, there are various combinations of these models up here that somebody can articulate as business architecture. Which one are you talking about when you say business architecture. Are you talking about the business process? Are you talking about the objectives and strategies. Or are you talking about the infrastructure distribution structure?

Or are you talking about some combination? You have to talk about the inventories and the processes and see those together. You can put together whatever combinations you want. There are 176 possibilities basically.

I would have what I would call the primitive components defined and then, depending upon what you want to talk about, I would construct whatever definition you want to construct.

Enterprise names

Now, I just put the enterprise names on it again. So here is The Framework for Enterprise Architecture and I populated this. Here is the Bill of Material, here are the functional specs, here is the Geometry or the Geography, here are the Operating Responsibilities, here are the Timing Diagrams and here is the Design Objectives, and here are the Scoping Representations, here are the Concepts Models, the Requirement Concepts, here are the Design Logic, here is the Building Physics in effect, the as planned, here are the Tool Configuration, and there is the Instantiation. So that’s The Framework for Enterprise Architecture.

I just put the enterprise names on it, You obviously saw what I was doing. You can read The Framework for Anything, you can read The Framework for Enterprise, but I was telling you The Framework for Anything. So it’s all basically the same thing. This is Enterprise Architecture.

Now, I have some of these framework graphics. For anybody who wants to go to the workshop this afternoon, we will make sure you have a copy of it, and anybody who doesn’t go to the workshop, we will have them out at the table. So you can pick up a copy.

I wrote a little article on the back of  that John Zachman’s Concise Definition of Zachman Framework.

Actually somebody asked me if I had ever read what Wikipedia said about my Framework? I said no, I had never read it. I don’t need to read Wikipedia to find out the definition of The Zachman Framework. So they said, “You better read it, because whoever wrote it has no idea what you’re talking about.”

So I read it, and they were right. They had no idea what I was talking about. So I fixed it. I wrote the article and put it out there. A couple of months later some friend of mine said, “Did you ever read what they wrote on Wikipedia about your Framework?” I said I wrote it. He said, “What? You wrote it? I don’t believe it. It’s not what you talk about.”

So I read it and some yo-yo had changed it back. So I changed it back. And a couple of months later, guess what? They changed it. So I said I’d forget these people. The fact is I wrote my own definition of The Zachman Framework, so that’s on the back there, with the little audio.

Now, you understand what I am telling you. This is Enterprise Architecture. It’s architecture for every other object known to humankind. It’s architecture for airplanes, buildings, locomotives, computers, for XYZ. It doesn’t make any difference. I just put enterprise names on it.

By the way, for those of you technical aficionados, the meta-entity names are at the bottom of every cell, and there are only two meta-entities on every cell. But the names are very carefully selected to make sure they are precisely unique and single variable. You only have one and only one thing in each one of these — one type effect in any one of these cells. So in any case, this is Enterprise Architecture.

Friends of mine wanted me to change the name of this to Zachman Ontology, because if you recognize this, this is not a methodology; this is an ontology. This does not say anything about how you do Enterprise Architecture — top-down, bottom-up, left to right, right to left, where it starts. It says nothing about how you create it. This just says this is a total set of descriptive representations that are relevant for describing a complex object. d I happen to have enterprise names on them, but it doesn’t tell you anything about how to do this.

Not either/or

For a lot of years, people didn’t know what to do with this. They were saying, “I don’t know what to do with it. How do you do Enterprise Architecture?” Now you understand where I am going to take you with this. This is an ontology, and you need a methodology. It is neither a methodology or an ontology. It’s an ontology and a methodology. It’s not either/or.

However, this is an ontology. It’s classifying. It has unique categories of every set of facts that are relevant for describing a complex object basically.

Now, by the way, there is another graphic in this and the reason I put this is that my name is on a number of websites, but I am excluded from those websites, I have nothing to do with those websites, even though they have my name on them. There is only one website that I have any access to, and that’s That’s why I put that slide in there and there’s some other stuff in there.

Now, you understand what I basically am saying here. Architecture is architecture is architecture. I simply put enterprise names on the same descriptive representations relevant for describing everything. Why would anyone think that the descriptions of an enterprise are going to be any different from the descriptions of anything else your manager has ever described? I don’t believe it.

Now, you could argue enterprises are different. Hey, airplanes are different than buildings too, and buildings are different than computers, and computers are different than tables, and tables are different than chairs, and everything is different, they are all different, but they all have Bills of Material, Functional Specs, Geometry. They all have Concepts, Logic, Physics, so this is basically architecture is architecture is architecture. That’s my observation.

I am trying to do this in a very short period of time and I haven’t had half a day or a day to soften all you guys up, but get ready, here you go. I don’t think Enterprise Architecture is arbitrary… and it is not negotiable. My opinion is, we ought to accept the definitions of architecture that the older disciplines of architecture, construction, engineering, and manufacturing have already established and focus our energy on learning how to use them to actually engineer enterprises. I think that’s what we ought to be doing.

So I don’t think it’s debatable. Architecture is architecture is architecture.

I have to tell you another thing, Depth and Width. For every cell, you could have a cell that’s enterprise wide and it’s an excruciating level of detail. That would be the whole cell basically.

Or you could have a model that is enterprise wide and only a medium level of detail. That would be half of it. You could have a model that’s enterprise wide at a high level of detail. So there is nothing that says that you have to have an excruciating level of detail. You can just say that’s another variable.

By the way, you could have a model that’s less enterprise wide. It’s an excruciating level of detail. It’s half of the enterprise excruciating or it could be the whole enterprise excruciating level of detail. So you have those two other variables. You have to be able to represent them in some fashion.

The implication is that anything that is white space here, if you don’t make it explicit, it’s implicit, which basically says that you’re allowing anybody and everybody to make way.

Risk of defects

It may be fine. You may be willing to accept the risk of making erroneous assumptions. You’re going to accept the risk of defects. In fact, in manufacturing airplanes they will accept some degree of risk of defects. When the parts don’t start to fit together in the scrap, the work cost starts to go up. Now, then they will say, wait a minute, you can’t complete the implementations until you have a complete engineering design release.

So that other variable you have to read into this as well. There are two different things here in ontology. I didn’t even know what an ontology was till fairly recently.

I’m going to give you my John Zachman layman’s definition of ontology. Some of you guys may be ontological wizards. I don’t know, but the probability in a group this big is that somebody really is familiar with ontology.

The Zachman Framework scheme technically is an ontology. Ontologies they are a theory of existence. Ontologies have to do with what exists, a theory of the existence of a structured set. That says a classification, a schema, that is rational, logical, and structured —  it’s not arbitrary — of essential components of an object. Those essential components that says the end object is dependent for its existence on the components and the components exist as well.

So you have a kind of existence of the object — it just isn’t the components — for which explicit expression is necessary. Probably it’s mandatory for designing, operating, and changing the object — the object being an enterprise, a department of an enterprise, a value chain, many enterprises, a sliver, a solution, a project, an airplane, a building, a bathtub or whatever — it doesn’t make too much difference what it is. It’s whatever that object is.

A framework is a structure. A structure defines something. In contrast, a methodology is a process, a process to transform something. And a structure is not a process, and a process is not a structure. You have two different things going on here.

Now, this is really an important idea too. Here is a comparison between ontology and methodology. An ontology is the classification of the total set of primitive elemental components that exist and are relevant to the existence of an object. A methodology produces composite compound implementations of the primitives.

All the implementation, the instantiations, are derivative of the methodology. The methodology produces the implementation. The implementations are compounds, and primitives, elements, are timeless and the compounds are temporal.

Now, that’s an important point, and I’ll try to give you an illustration of that.

Here is an ontology. I learned a lot from this metaphor by the way. This is a classification of all the elements in the universe actually. It’s a two-dimensional schema. It’s normalized; one factor in one place. You are classifying the elements of the universe in terms of neutrons and protons — the number of neutrons and protons by the number of electron. That is not a process.

This tells you nothing about how do you do this: top-down, bottom-up, left to right, right to left, or what compound that you might want to create out of this thing. This just says here is the total set of elements from which you can create whatever you want to create.

And once again, I didn’t say this yet, but until an ontology exists, nothing is repeatable and nothing is predictable. There is no discipline.

Best practices

Before Mendeleev published the periodic table, there were chemists. They weren’t chemists actually; they were alchemists, and they were very clever by the way, really competent, very clever. They could produce implementation, produce compounds, but it was based upon their life experience. It was a best practice kind of a thing, not based upon a theoretical construct.

And elements — these elements are timeless. If you have an element that has six neutrons and protons and two electrons, that’s carbon. The rest of the world calls it carbon. Do yourself a favor and call it carbon. You can call it whatever you want to, but if you want to communicate with anybody else, just call it by the name that is recognizable by the rest of the universe.

Now, in any case, those are the elements and they are timeless. They are just forever.

Here are compounds. This is a process. A process transforms, creates something. This is a process. Take a bowl of bleach and add it to a bowl of alkali. It has to get transformed into saltwater. This is not an ontology; this is a process. Take this, add it to that, and it’s going to produce whatever you want to produce.

Now, the compounds are temporal. You produce saltwater for some reason, something good for some whatever, whatever it happens to be that you are trying to create.

Here are some examples of other compounds. This is an acid and a base, or a base or an alkali, and again, sodium chloride on water. It’s a balanced compound. Here is hydrogen, there is hydrogen, there are the two hydrogen. Here is chlorine, there is chlorine, here is the sodium, there is a sodium, here is the oxygen, there is oxygen.

We could not have written this down like that until Mendeleev published the periodic table. We didn’t have any notation to produce that.

So here are some other compounds: here is salt, that’s sodium chloride, here is aspirin. C9H8O4, Vicodin is C18H21NO3, Naproxen is C14H14O3, Ibuprofen, Viagra, sulphuric acid and water and so on and so on.

How many of these can you create out of the periodic table? The answer is infinite. It would go infinite. I don’t want to take the time to elaborate, but it’s infinite. And these are temporal. These are specifically defined to do specific things.

Here is an ontology. How many different enterprises could you create out of this ontology? And the answer again is going to be infinite. Until an ontology exists, nothing is repeatable, nothing is predictable. There is no discipline. Everything is basically best practice. The perimeters are timeless.

Now, here are some compounds. The elements are what I would call a primitive component. The compounds are implementations, instantiations. COBOL Programs, you can read Java 2 or Smalltalk or whatever you want to read; Objects, BPMN, Swimlanes, Business Architecture, Capabilities, Mobility, Applications, Data Models, Security Architecture, Services, COTS, Technology Architecture, Big Data, Missions/Visions, Agile Code, Business Processes, DoDAF Models, Balanced Scorecard, Clouds, I.B. Watson, TOFAF Artifacts, and so on. How many of these are there? It’s infinite.

Specific reasons

How long will it be until we can add one to the list? What time is it? People get really creative. They create a lot of these things. And these are temporal. They are for specific reasons at a specific point in time.

Here is alchemy. It’s a practice. It’s a mythology without an ontology. Process is down in the basement with a chemistry set, trying things out. If it works and it doesn’t blow the house up, write that one down; that’s a good one. If it blows up, you probably have to write that one down too; don’t do that one again.

So a process with no ontological structure is ad hoc, fixed, and dependent on practitioner skills. It’s not a science; it is alchemy; it’s a practice.

I’ve got to tell you, the alchemists were really clever. Man, they figured out how to create gunpowder long before they ever had the periodic table. So these people were really creative. However, few hundred years later, Mendeleev published the periodic table.

I don’t know whether you guys realize this or not, but we tend to think the periodic table has been around forever, because the elements have been around forever. Basically we learn that in chemistry or whatever. Periodic table was only published in the 1880-1890 time frame.

If you think about this, within 50 years of the publication of the periodic table, the physicists and chemists basically were splitting atoms. Think about this. Once you have order, now research actually works. Things become predictable and repeatable. We don’t have to learn everything by experience. We can hypothetically define other possibilities and get really creative.

Like I say, in a very short period of time, friction goes to zero, and you can get really creative and really sophisticated in very short periods of time, so I just throw that one away.

So ontology versus process, engineering versus manufacturing, architecture versus implementation. It’s not “either/or;” it is “and.” And the question is, how did you get your composite manufacturing implementation? Did you reuse components of primitive, ontological, engineering constructs, or did you just manufacture the composite ad hoc to some problem or some system requirement?

Actually the enterprise is the total aggregate sum of composite implementations.

Now, the question is, how did you get your composite? Were you just building systems or did you have the periodic table of primitive components from which you assembled the implementation?

If you just built them to get the code to run, they’re not going to be integrated, not flexible, not interoperable, not reusable. They are not aligned; they are not meeting expectations.

So the question is, how did you get the composite, the compounds? Did you have the periodic table? Now, obviously I am taking it to a point where I am saying, it’s not an “or;” it’s an “and.”

Allen and I were talking about this yesterday. I don’t want to take a lot of time to develop this, but this came from Roger Greer, who was the Dean of the School of Library and Information Management USC years ago, and I just happened to run across some notes I had taken at an IBM GUIDE Conference in 1991.

Professional vs. trade

Roger was talking about the difference between a profession and a trade. He basically didn’t make any differentiation. This is the Professional Service Cycle. The professional starts with a diagnosis, analysis of need, and diagnoses the problem. Then you prescribe the solution. Then the technician applies the solution. He evaluates the application and, depending upon the evaluation, enters into the cycle again.

So what differentiates the professional from the trade or labor is the diagnosis and a prescription, where the trade or labor is involved with the implementation and any evaluation.

My observation is that this is where the engineering has taken place. That’s where you need the ontology to do the diagnosis and the prescription. And then, you need the methodology to do the implementation basically — the manufacturing. The engineering work is going on over here; the manufacturing work is going on over there.

So what differentiates the professional from the trade? Well, if you start with the diagnosis of the problem and the prescription, that’s what the doctor does. The x-ray technician shoots the x-ray, takes the picture, and then evaluates whatever the result is.

Leon Kappelman is a friend of mine. He’s an academic guy. He traces the CEO surveys for years and years — 20, 30 years. In 20 or 30 years, one of the top ten issues that the CEOs of the world say those of us who come from the information community need to deal with turns out to be alignment.

They’re basically saying, “I don’t know what you guys are doing. You’re spending a lot of money down there in IT. Whatever you’re doing with it does not align with what I think the enterprise is about.”

So there’s an alignment problem. I would submit to you, if you are starting over here, you are going to always be a solution in search of a problem.

So we want to change it. Allen and I really feel strongly about this. Those of us who come from the architecture domain, need to begin to develop the characteristics of a profession. This is a profession. Well, that presumes a discipline, and the implication is that we need to change our whole concept to diagnose the enterprise problem. In fact, that’s the one last slide I would use.

The end object is not to build the system. The end object is to diagnose the enterprise problem. Then, you can prescribe. The enterprise really complicates it. You can probably prescribe three, four, or a dozen different possible solutions that they could pursue. Okay chief, here are a set of things that you can do.

Somebody, I think it was Steve Jobs in his book, said that you had to go in with two recommendations to Steve Jobs, but you have a third one in your pocket, because he would tear them up. So, you have to go in and have a third one.

How many do you want chief? We can construct however many you want to, and you can evaluate them or analyze them for whatever the implications are. What are the capital expense implications, or cultural? You can analyze them and let them understand what the alternatives are, what the implications are, or the alternatives. and you can pick one and you can do the implementation, then you evaluate and so on.

Lessons to be learned

This is what differentiates the profession from the trade. This is important. The more I think about it, there is really lessons to be learned here.

Here are the research lessons that we’ve learned. It is possible to solve general management problems very quickly with a small subset of primary components — simply lists and their interdependencies short of the complete primitive models.

You don’t have to do a lot of architecture to begin. You have enough that you can do the diagnosis of the problem. Then, different complex, composite constructs can be created dynamically, virtually cost-free, from the inventory of primitive lists for addressing subsequent general management problems.

Once you begin to populate the inventory of the primitive components, they are reusable to analyze or diagnose other problems. This is not just limited to whatever precipitated the first population of the primitives.

And many scenarios can be evaluated to test strategy alternatives before making commitments. You can analyze the implications and make recommendations around those implications before you actually spend money or actually create infrastructure kinds of things.

These are really important issues. So here are my conclusions.

Here is what I would propose to TOGAF. There is a TOGAF development strategy, and I would evolve TOGAF to become an engineering methodology as well as a manufacturing methodology.

Those of us who come from the IT community, for the last 75 years we’ve been building it to run a system. Technically that’s what people say. If you ask somebody from IT what they do for a living, we would say we’re building to run systems.

So all of us are very manufacturing dominant. That’s the way we tend to think. We tend to think in terms of composite contracts. Every artifact, if it has more than one variable in it, is a manufacturing artifact; it’s not an engineering artifact. I can tell pretty quickly by looking at the descriptive representation, looking at the model.

If you have more than one variable in that model, I know you’re using it for manufacturing, for implementation purposes, probably manufacturing, I would say the implementation purposes.

I would just broaden TOGAF to dig in to deal with the engineering issues. The way I would do that in Phase I. I’ll tell you what I think phase two and three might be as well. I’m just getting creative here. Allen may say, “That’s interesting, Zachman, but you’re out of here. We don’t need a lot of help. We already got enough things to do here.”

Existing process

But, first of all, I would use the existing data-gathering process to populate the inventory of single-variable, primitive podels. We’re already doing the gathering, and I would just factor out what are the primitive components and begin to populate the inventory.

We have a little workshop this afternoon to show you how to do that. It is not that hard. Anybody who goes to the workshop will see. The workshop is created in order to just show you that it’s not too complicated. You can do this.

I would just use the existing data-gathering, purchase methodology, begin to populate. Then I would reuse the primitive components in the creations of present TOGAF artifacts. You’ve got to create the artifacts anyway, you might as well just reuse the primitive components.

Now that presumes another thing for those of you who are into the tooling domain, but you’d have to map the primitive metamodel against the TOGAF metamodel. So there is a metamodel issue here.

But what that would tell you is that you have to look at the metamodel, the TOGAF artifacts, and see if there is a composite construct in the Metamodel and just factor out what the primitive components are.

That’s the way you would map the composite you’re trying to create from the primitive implementations. That’s what I would do. That would be just looking at right where we are today. So, here is the set of primitives and here is the methodology. Let’s just use the methodology to do engineering work, and it will still end up creating the same implementation composites.

I was getting creative, here is what I would do. Here is what we were doing. I probably have to say it that way, and I encourage you to do this by the way. I would extend the methodology for enterprise problem diagnosis and solution prescriptions –single-variable, primitive components, binary relationships, and impact analysis.

What you need in order to do the diagnosis is the single-variable, primitive construct, and only binary models, because what you’re going to do with this is to impact analysis. You touch one thing, and here are all the other things that are impacted by it.

That application has been around for a long time. I’m not telling you something that nobody knows how to do. But there are single-variable models and binary models. Building a binary model, is this related to that, is this related to this, there are two things at a time. The models are pretty simple. I’m not going to take more time to do that but — then I would segue to the current TOGAF methodology.

I would come out of here and go into the current methodology, making enhancements incrementally as practical. You have been improving TOGAF forever, from the very beginning. I would just start to begin to improve it based upon what we’ve learned with the diagnostic and the prescription enhancement.

The transformation

Then, in Phase III, I would orchestrate the transformation from the TOGAF artifacts to implementation, lower rows. I would orchestrate that transformation. So you have the transformation from the strategy up here and to the concept, the logic and physics, totally a configuration.

I would orchestrate that and I would extend the TOGAF governance process. With a governance process, and TOGAF’s is really strong, I would just take a hard look at that and elaborate that to manage the entire reification set of transformation. That’s where I would take it to.

Some of you may say, “We will not take so long or cost too much, whatever.” The argument might be a guess, but I think that’s where I would go. Principally, I think that’s where I go because of the implication of changing the fundamental concept of the Enterprise Architecture. The profound significance is this. It alters the concept of Enterprise Architecture from one of building models to one of solving general management problems.

Man, that would be really interesting. It buys the time for the experts to build out the complete Enterprise Architecture — thing-relationship-thing — primitive models iteratively and incrementally. You don’t have to do it all at once, but general management problem, my problem, my problem, my problem, iteratively and incrementally start building out little more adding to the primitives over time.

Then, it builds significant creditability for the information-technology community, and I would submit, we need all help we can get. If we begin to be perceived to be the enterprise doctors, we would be perceived to be direct not indirect. It wouldn’t be an optional, but mandatory, kind of a responsibility. Most importantly, it would position Enterprise Architecture to become a general management operational process, not simply an IT exercise. I think that’s where you have to go.

If we could change the perception of Enterprise Architecture to be one of solving general management problems, we would have no problem getting the resources and the time to do whatever Enterprise Architecture we want to do. That valuation issue will tend to go away. I saw a presentation yesterday about the valuation. It was talking about the Internet of Things, and it was really a creative presentation. I really appreciated it a lot.

But if we can solve general management problems, you don’t have to worry about valuation. I will say one more thing about valuation. The fundamental problem with architecture is that it doesn’t save money in the current accounting period. It’s not an expense. You don’t make money or save money in the current accounting period. You are building an inventory of assets. What make an asset different than an expense? How many times you use it. Use it more than once, it’s as an asset. So you build the inventory of assets.

The assets don’t save you money in the current accounting period, but they enable you to make money or save money in many accounting periods in the future. The problem with asset valuation is the accounting people in the US are precluded from putting values on assets. That’s probably because there’s not an absolute way to value the asset, because the value of it is derived from how many times you use it, or from what the market will pay for at some point in time.

It’s difficult to value assets and it’s really difficult to value intellectual assets. I would submit Enterprise Architecture as intellectual asset, and we’re just beginning to learn some things about that. But that issue turns out to go away. If you just solve general management, you don’t have to worry about valuing the value proposition.

I think I made it in an hour, but actually an hour and three minutes. I owe Allen three minutes now and that’s not too bad on my part, but  there will be a panel. We will have some discussion, answer any questions, and then also there is a workshop of anybody who cares about trying to work with some of these things. I would be glad to do it. Thank you, Allen, and thank you guys for taking the time to listen, I appreciate it a lot.


Brown: We have some questions. We talked about professionalizing Enterprise Architecture. We both feel passionately about it, and having these professionals as the enterprise doctors, as you say. The person to ask the questions is actually the CEO of the AEA, Steve Nunn. The AEA is now 44,000 members. And they are actually active, as well, which is great. So, Steve, what have you got?

Steve Nunn: Unsurprisingly no shortage of questions and compliments on the presentation, John.

Here’s a long question, but bear with it. Given a composite of an enterprise, a methodology existed for its construction. Today, I have a million assets with individual change records associated with them. The EA methodology did not govern the maintenance after construction. What do you suggest to correct the EA to the Ontology?

Zachman: This not atypical, by the way. I think that’s where most of us live. I normally make the case that those of us who come from IT have been manufacturing the enterprise for last 60 to 70 years. The enterprise was never engineering. We are manufacturers. So we are manufacturing parts. We don’t manufacture the enterprise, and the parts don’t fit together.

So what do you do if you manufacture parts that don’t fit together? We will actually scrap and rework. There is no way to fix that problem after the fact. If you want the parts to fit together, you have to engineer them to fit together before you manufacture them. After you get them manufactured and then try to fit them together, you can’t get them to fit together.

We’re all sitting in kind of the same position. Somebody has to break the pattern. If you just keep on writing code, you’re just going to get more parts. You’re not going to change anything. You have to have a different paradigm. I was describing for you a different paradigm, and I was describing for you an engineering paradigm.

I would do just exactly what I said. I’ll start taking TOGAF. We already have this methodology — very, very widely respected, very widely used. I would take the methodology, the data gathering methodology portion of it, and I would begin to populate the inventory of primitive assets. You don’t have to have them all, but you have to begin. So you have to have solve whatever you can out of the TOGAF activity that you have at your disposal.

Once you do that, then you’re going to populate them with the primitives that are required to create the TOGAF composites right now, so we can produce whatever we are producing out of TOGAF right now. I would just start with something I know, something I have my hands on. I can start with TOGAF. I would start to populate the primitive artifacts and then create by reusing the primitives to create the composites.

So I would start with there. And then I begin to enhance that over time. I have to begin to enhance the methodology to elaborate. I gave you some thoughts about how I would enhance it, but in the meantime what you can do is, once you start creating the architectural constructs, you have to orchestrate your way out of where you’re at.

Migrating what exists

We don’t have a blank sheet of paper when we start with our Enterprise Architecture. We already have an enterprise out there. You have to figure out a way to migrate out of the existing environment into the architectural environment. am not just going to tell you what I think the solution is without elaborating how I would use it, but I would use it, the data warehouse kind of a concept. I create the architecture. I extract and transform the database out of the existing application to populate the architectural environment.

I didn’t learn this. I didn’t figure out this all myself. People from Codec and I were sitting in the Codec Theater one time. They were saying, once we have the architected data, we know what the data is, and now we’re going to rebuild all the transaction processing systems to update the data warehouse. Then, after we update the data warehouse, we’re going to turn off the legacy system.

Over some period of time, however long you want to take, you just move it out little by little, transaction by transaction by transaction to the architectural environment. If you’re going to rebuild the transaction processing systems to populate the data warehouse, then I would add the process specification. I would add the distribution. I would add the other characteristics of architecture. That’s the way it would orchestrate my migration out of the existing environment.

Brown: There is also a sense that came out of that question. The architecture, once it was done, it was done. And then things changed afterwards. So there was no concept that the architecture should be referenced any time you make a change to the instantiation and to update the architecture accordingly.

Zachman: Allen, that’s really an important point. I’ll tell you where I learned how to manage change. I set that up at Boeing. It took me about 10 years to figure this out. How do you manage the changes in the Boeing 747’s? Very carefully, right?

Brown: Yes.

Zachman: You don’t walk around a Boeing 747 with a hammer and screwdriver making the changes. Forget that. You have an engineering administration. Who are they? They’re managing the drawings, the functional space, the building materials, and so on. You pull out the one you want to change. You change the artifact and then you figure out the impact on the adjoining artifacts.

When you figure out the impact, you make changes to all those other artifacts. You don’t throw away the old version, but you keep the old version. It is regulated in the airplanes. You have to trace the artifact back to the last time we had an airplane that flew successfully.

But once you change the artifact, then you go to the shop for a particular change kit. You take the change kit out of the Boeing 747 and you put the change in the Boeing 747. If you manage change in that fashion, you will minimize the time, disruption, and cost of change in the Boeing 747.

Every artifact precisely represents the Boeing 747 as it existed in this moment. And one thing that people would not tend to know, every Boeing 747 is unique. They are all different and they have a set of these artifacts for every Boeing 747. You can trace it back to the origin, whatever they changed and flew since the last time. The Boeing 747 is not complicated enough. These artifact were on paper.

The first electronically designed airplane was the 777. Now you understand the reason I’m telling you this. If you really want to change the Enterprise Architecture before you change the enterprise, if you have a general management responsibility for Enterprise Architecture, this a piece of cake.

Making changes

So by the way Ms. Vice President of Marketing, before you change your allocation responsibility, your organization responsibility, come-up and see me. We’ll change the repository first, and then you can change the allocation responsibility.

Oh, by the way Ms. Programmer, before you change a line of code in that program, you come up and see what changes are in the repository first. Then, you can change the line of code on the program. Before you change anything, you change the architecture first, and then you change it.

And, by the way, it’s dynamic because you can continuously solve problems, you can then populate, put more primitive components into the architecture. That’s why this becomes really important. It becomes an operating responsibility for the general management.

If they really understood what they are, that’s a knowledge base, everything they can possibly know about the enterprise. They can change anything or have a great deal of creativity to do lots of things they haven’t even dreamed about doing before. That’s really important idea that that becomes a general management who is integrated into the enterprise operation.

Nunn: Next question. Have you considered what changes might be required to your framework to accommodate an ecosystem of multiple enterprises?

Zachman: That’s what I would call federated architecture. You know some things you want in common with more than one enterprise and some things you want to be provincial, if you will. Some things you want to make federal, some things you want to leave provincial. The problem we have now is that when you try to make things common or federal, when they are not common, that’s where you get the hate and discontent. The framework is really helpful for thinking through what you what to make common or you want to leave a provincial artifact.

That’s the way you need to deal with that and that would be any complex environment. In  most every enterprise these days, there is more than one framework that you might possibly want to populate, but then you have to understand what you want to be the same and what you want to leave. That’s the way we would handle that.

Brown: So if you are an architect, you pull out the drawing for the entire urban landscape. You pull out the drawing for specific buildings. You pull out the drawing for functions within that building, a different framework.

Zachman: Actually this was implemented. I learned it from the Province of Ontario. There was a premier about 20 years ago who was pretty creative. We sorted all of the departments in the Province of Ontario into categories. You can call them clusters.

You had the social services cluster, the land cluster, the finance cluster. Then, he put a super minister in-charge of each cluster, and their role was to integrate — get rid of all the redundancy and make it as integrated as possible.

That was the process we were using. You have a federation at each cluster, but then you have the federation, a second level up at the province level as well.
Common connector

Nunn: Do you envision a common connector from a given architecture development method, like TOGAF, DoDAF, FEA to the Zachman Framework?

Zachman: When we talk in the workshop, we’ll get into that a little bit. If you have the primitive components and say which set you want to change, if you want the TOGAF components, click, there is TOGAF. Oh. You want DoDAF, oh no problem; click, there is the DoDAF. Oh you want balanced scorecard, no problem; click, there is a balanced scorecard. Oh, you want your profit and loss statement; click, there is a profit and loss.

What you’re doing is you are creating a composite dynamically. Whatever the composite is, you want to take a look at. I was really impressed with Don’s presentation yesterday. I was at Raytheon last week and there was a presentation I had seen recently about the hardware — the price performance improvements and the capabilities in hardware. What it basically was saying is that you’ll be able to put big data, all the structured data, all this data on a chip. And that chip will go into your processor. The guys at Raytheon said that it’s not when you can do it; you can do it now.

Because you have big data on a chip, and you can analyze that big data and find a threat, an opportunity, or something external or even internal, the immediate question is going to become what are you going to change in the enterprise. Are you going to increase or decrease the inventory, increase or decrease the process transformation, going to increase or decrease the storage capacity of the node? What are you going to do to your enterprise?

So if you have big data on a chip, you get dynamically identified threats and opportunities. What do you think that’s going to do to the decision cycle? It’s going to make that decision cycle very short.

So you have to make up your mind. What you are going to do real quickly. So you like several alternatives? Okay, chief, here are the three or four alternatives. Which one do you want to pick?

It’s going to shorten the decision cycle dramatically. Dawn was frightening me yesterday. We’re not talking about the sweet by and by. She was talking about stuff that is here now, and that’s what the guys at Raytheon were telling me. This is here now.

I talked about big data before, and the fundamental question is, once you figure out something external or even internal, what are you going to do to your enterprise? Where is your Enterprise Architecture? What are you going to change?

The next question is, who is working on your architecture? Somebody might be working on this. I’ll tell you about that. I don’t think too many people have an idea of the sense of urgency we have here. You’re not going to do this today. You have to start working on this, and you’ve got to eat this elephant — bite, bite, bite. It’s not going to happen overnight.

Nunn: How can the Zachman Framework be applied to create an architecture description that can be implemented later on, without falling into a complex design that could be difficult to construct and maintain. Following on from that, how do you avoid those descriptions becoming out of date, since organizations change so quickly?

Manufacturing perspective

Zachman: Whoever posed that question is thinking about this from a manufacturing perspective. They’re thinking about it as a composite construct. And if you separate the independent variables and populate the primitive components, don’t bind anything together until you click the mouse, and you can change any primitive component anytime you want to, okay.

You’re not locked into anything. You can change with minimum time. You can change one variable without changing everything. The question is couched in terms of our classic understanding of Enterprise Architecture, the big monolithic, static, it takes long time and costs lot of money. That’s a wrong idea. Basically, you build this iteratively and incrementally, primitive, by primitive, by primitive, and then you can create the composites on-the-fly.

Basically, that would be the approach that I would take. You’re not creating fixed implementation, extreme complex implementations. That’s probably not the way that you want to do it.

Nunn: Short question. Is business architecture part of Enterprise Architecture or something different?

Zachman: Well, out of the context in my framework, I started to suggest that some people say, well, the business processes are as architecture, it would be column 2, row 2. Some people say, well, no, it’s actually column 6, row 1. Some people will say, well it’s actually the composite of column 1, and column 2 at row 2.

The Chief Operating Officer of a utility I work with, this is years ago now, he basically said, “My DP people know this. My DP people want to talk to me about data, process, and network, and I don’t care about data, process and network. I want to talk about allocation, responsibility, business cycles, and strategy. So I don’t want to talk about column 1, 2, and 3. I only care about column 4, 5, and 6.”

I couldn’t believe this guy said that. I knew the guy. You don’t care about the inventories, the process transformations, and the distribution structure? Are you kidding me — in a utility? Come on. It is just unfathomable.

At some point of time you probably are going to wish, you’d have more and more and more of these primitives. Build them up iteratively and incrementally for some long period of time. There’s not one way to do it, there are n different ways to do it, and some work better than others. The fact that you’ve got a tested methodology, you had to use that, why not.

Brown: WI think it depends on which one of the 176 different definitions of business architecture you use.

Zachman: Yes.

Business architects

Brown: In my definition, the people I spoke to in Australia and New Zealand, had the title of business architects and they quite clearly felt that they were part of Enterprise Architect. But the other side of things is that some of the greatest business architects would be Bill Gates, Michael Dell, Steve Jobs, Jack Roush.

Zachman: I was pontificating around the architectural idea and I lost sight of the business architecture question. The question turns out to be, which primitives you want to consider. If you want to say you want to open up new markets, then we’ve got to figure out whatever choice you are going to need, what process, what location, and that would create the composite that you need for addressing whatever the issues you have —

Brown: And it is too tough, Enterprise Architecture.

Zachman: Yeah, right exactly.

Nunn: TOGAF’s primary, short- and long-term guidance is achieved through principles or achieved with principles. How would you propose to reconcile that with the idea of extending TOGAF’s framework and method with the Zachman Framework?

Zachman: The principles don’t go away. One thing is that when you define principles, they have a lifetime. Somebody was making that case yesterday at the presentation. He defined the principle, and I think there is a set of architectural principles. One thing is, if you want flexibility to separate the independent variables, that’s a good principle, you have a single point of control, you have single sort of truth. Those tend to be principles that people would establish, and then take whatever the principles are that anybody has in mind, and you could figure out how that manifests itself in the architectural structure, in the framework structure and, in fact, the ontological construct, and you manage that. I mean the governance system has to enforce the principle.

There is another principle. I would not change the enterprise without changing the artifact first. I would change the architecture before I change the enterprise. Here is another one. I wouldn’t allow any programmer to just spuriously create any line or code or data element or any kind of technology aggregation. You reuse what’s in the primitive. And if it’s not, if you need something that’s not in that primitive, then fix the primitive. Don’t create something spurious just to get the code to run. That’s another principle.

There was probably an array thing, and off the top of my head there is a couple that I would be interested in, but those tend to be deriving from these ideas about architecture. I learned it all of this. I didn’t invent any of this. I learned it by looking to other people and, I saw the patterns. All I do is I put enterprise names and the same architectural constructs in any other object and then I learned about migration. I learned about federation. I learned about all these other things by managing change and by looking at what other people did.

This has been a special BriefingsDirect presentation and panel discussion from The Open Group San Diego 2015. Download a copy of the transcript. This follows an earlier discussion on cybersecurity standards for safer supply chains. And another earlier discussion from the event focused on synergies among major Enterprise Architecture frameworks.

Copyright The Open Group and Interarbor Solutions, LLC, 2005-2015. All rights reserved.

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GoodData analytics developers on what they look for in a big data platform

This BriefingsDirect big data innovation discussion examines how GoodData created a business intelligence (BI)-as-a-service capability across multiple industries that enables users to take advantage of both big-data performance as well as cloud delivery efficiencies. They had to choose the best data warehouse infrastructure to fit their scale and cloud requirements, and they ended up partnering with HP Vertica.

Listen to the podcast. Find it on iTunes. Get the mobile app for iOS or Android. Read a full transcript or download a copy.

To learn more about their choice process for best big data in the cloud platform, we’re joined by Tomas Jirotka, Product Manager of GoodData; Eamon O’Neill, the Director of Product Management at HP Vertica, and Karel Jakubec, Software Engineer at GoodData. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Tell us a bit about GoodData and why you’ve decided that the cloud model, data warehouses, and BI as a service are the right fit for this marketplace?

Jirotka: GoodData was founded eight years ago, and from the beginning, it’s been developed as a cloud company. We provide software as a service (SaaS). We allow our customers to leverage their data and not worry about hardware/software installations. We just provide them a great service. Their experience is seamless, and our customers can simply enjoy the product.


We provide a platform — and the platform is very flexible. So it’s possible to have any type of data, and create insights. You can analyze data coming from marketing, sales, or manufacturing divisions — no matter in which industry you are.

Gardner: If I’m an enterprise and I want to do BI, why should I use your services rather than build my own data center? What’s the advantage?

Cheaper solution

Jirotka: First of all, our solution is cheaper. We have a multi-tenant environment. So the customers effectively share the resources we provide them. And, of course, we have experience and knowledge of the industry. This is very helpful when you’re a beginner in BI.

Gardner: What have been some of the top requirements you’ve had as you’ve gone about creating your BI services in the cloud?


Jakubec: The priority was to be able to scale, as our customers are coming in with bigger and bigger datasets. That’s the reason we need technologies like HP Vertica, which scales very well by just adding nodes to cluster. Without this ability, you realize you cannot implement solutions for the biggest customers. Even if you’re running the biggest machines on the market, they’re still not able to finish computation in a reasonable time.

Gardner: In addition to scale and cost, you need to also be very adept at a variety of different connection capabilities, APIs, different data sets, native data, and that sort of thing.

Jirotka: Exactly. Agility, in this sense, is really curial.

Gardner: How long you have been using Vertica and how long have you been using BI through Vertica for a variety of these platform services?

Working with Vertica

Gardner: What were some of the driving requirements for changing from where you were before?

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Jirotka: We began moving some of our customers with the largest data marts to Vertica in 2013. The most important factor was performance. It’s no secret that we also have Postgres in our platform. Postgres simply doesn’t support big data. So we chose Vertica to have a solution that is scalable up to terabytes of data.

Gardner: What else is creating excitement about Vertica?


O’Neill: Far and away, the most exciting is about real-time personalized analytics. This allows GoodData to show a new kind of BI in the cloud. A new feature we released in our 7.1 release is called Live Aggregate Projections. It’s for telling you about what’s going on in your electric smart meter, that FitBit that you’re wearing on your wrist, or even your cell-phone plan or personal finances.

A few years ago, Vertica was blazing fast, telling you what a million people are doing right now and looking for patterns in the data, but it wasn’t as fast in telling you about my data. So we’ve changed that.

With this new feature, Live Aggregate Projections, you can actually get blazing fast analytics on discrete data. That discrete data is data about one individual or one device. It could be that a cell phone company wants to do analytics on one particular cell phone tower or one meter.

That’s very new and is going to open up a whole new kind of dashboarding for GoodData in the cloud. People are going to now get the sub-second response to see changes in their power consumption, what was the longest phone call they made this week, the shortest phone call they made today, or how often do they go over their data roaming charges. They’ll get real-time alerts about these kinds of things.

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When that was introduced, it was standing room only. They were showing some great stats from power meters and then from houses in Europe. They were fed into Vertica and they showed queries that last year we were taking Vertica one-and-half seconds. We’re now taking 0.2 seconds. They were looking at 25 million meters in the space for a few minutes. This is going to open up a whole new kind of dashboard for GoodData and new kinds of customers.

Gardner: Tomas, does this sound like something your customers are interested in, maybe retail? The Internet of Things is also becoming prominent, machine to machine, data interactions. How do you view what we’ve just heard Eamon describe, how interesting is it?

More important

Jirotka: It sounds really good. Real-time, or near real-time, analytics is becoming a more-and-more important topic. We hear it also from our customers. So we should definitely think about this feature or how to integrate it into the platform.

Jakubec: Once we introduce Vertica 7.1 to our platform, it will be definitely one of features we will focus on. We have developed a quite complex caching mechanism for intermediate results and it works like a charm for Postgres SQL, but unfortunately it doesn’t perform so well for Vertica. We believe that features like Live Aggregate Projection will improve this performance.

Gardner: So it’s interesting. As HP Vertica comes out with new features, that’s something that you can productize, take out to the market, and then find new needs that you could then take back to Vertica. Is there a feedback loop? Do you feel like this is a partnership where you’re displaying your knowledge from the market that helps them technically create new requirements?

Jakubec: Definitely, it’s a partnership and I would say a complex circle. A new feature is released, we provide feedback, and you have a direction to do another feature or improve the current one. It works very similarly with some of our customers.

O’Neill: It happens at a deeper level too. Karel’s coworkers flew over from Brno last year, to our office in Cambridge, Massachusetts and hung out for a couple of days, exchanging design ideas. So we learned from them as well.

They had done some things around multi-tenancy where they were ahead of us and they were able to tell us how Vertica performed when they put extra schemers on a catalog. We learned from that and we could give them advice about it. Engineer-to-engineer exchanges happen pretty often in the conference rooms.

Gardner: Eamon, were there any other specific features that are popping out in terms of interest?

O’Neil: Definitely our SQL on Hadoop enhancements. For a couple of years now we’ve been enabling people to do BI on top of Hadoop. We had various connectors, but we have made it even faster and cheaper now. In this most recent 7.1 release, you can now install Vertica on your Hadoop cluster. So you no longer have to maintain dedicated hardware for Vertica and you don’t have to make copies of the data.

The message is that you can now analyze your data, where it is and as it is, without converting from the Hadoop format or a duplication. That’s going to save companies a lot of money. Now, what we’ve done is brought the most sophisticated SQL on Hadoop to people without duplication of data.

Using Hadoop

Jirotka: We employ Hadoop in our platform, too. There are some ETL scripts, but we’ve used it in a traditional form of MapReduce jobs for a long time. This is really costly and inefficient approach because it takes much time to develop and debug it. So we may think about using Vertica directly with Hadoop. This would dramatically decrease the time to deliver it to the customer and also the running time of the scripts.

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Gardner: Eamon, any other issues that come to mind in terms of prominence among developers?

O’Neill: Last year, we had our Customer Advisory Board, where I got to ask them about those things. Security came to the forefront again and again. Our new release has new features around data-access control.

We now make it easy for them to say that, for example, Karel can access all the columns in a table, but I can only access a subset of them. Previously, the developers could do this with Vertica, but they had to maintain SQL views and they didn’t like that. Now it’s done centrally.

They like the data-access control improvements, and they’re saying to just keep it up. They want more encryption at rest, and they want more integration. They particularly stress that they want integration with the security policies in their other applications outside the database. They don’t want have to maintain security in 15 places. They’d like Vertica to help them pull that together.

Gardner: Any thoughts about security, governance and granularity of access control?

Jakubec: Any simplification of security and access controls is a great new. Restriction of access for some users to just subset of values or some columns is very common use case for many customers. We already have a mechanism to do it, but as Eamon said it involves maintenance of views or complex filtering. If it is supported by Vertica directly, it’s great. I didn’t know that before and I hope we can use it.

Listen to the podcast. Find it on iTunes. Get the mobile app for iOS or Android. Read a full transcript or download a copy. Sponsor: HP.

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Source Refrigeration selects agile mobile platform Kony for its large in-field workforce

The next BriefingsDirect enterprise mobile strategy discussion comes to you directly from the recent Kony World 2015 Conference in Orlando.

This series of penetrating discussions on the latest in enterprise mobility explores advancements in applications design and deployment technologies across the full spectrum of edge devices and operating environments.

Our next innovator interview focuses on how Source Refrigeration and HVAC has been extending the productivity of its workforce, much of it in the field, through the use of innovative mobile applications and services.

Listen to the podcast. Find it on iTunes. Get the mobile app for iOS or Android. Read a full transcript or download a copy.

We’ll delve in to how Source Refrigeration has created a boundaryless enterprise and reaped the rewards of Agile processes and the ability to extend data and intelligence to where it’s needed most.

To learn how their successful mobile journey has unfolded, we welcome Hal Kolp, Vice President of Information Technology at Source Refrigeration and HVAC in Anaheim, California. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: It’s my understanding that you have something on the order of several hundred field-based service and installation experts serving the needs of 2,500 or more customers nationwide. Tell us a little bit about why mobility is essential for you and how this has created better efficiency and innovation for you?

Kolp: Source started to explore mobility back in 2006. I was tasked with a project to figure out if it made sense to take our service organization, which was driven by paper, and convert it to an electronic form of a service ticket.


After looking at the market itself and at the technology for cellular telephones back in 2006, as well as data plans and what was available, we came to the conclusion that it did make sense. So we started a project to make life easier for our service technicians and our back office billers, so that we would have information in real time and we’d speed up our billing process.

At that time, the goals were pretty simple. They were to eliminate the paper in the field, shorten our billing cycle from 28 days to 3 days, and take all of the material, labor, and asset information and put it into the system as quickly as possible, so we could give our customers better information about the equipment, how they are performing, and total cost of ownership (TCO).

But over time, things change. In our service organization then, we had 275 guys. Today, we have 600. So we’ve grown substantially, and our data is quite a bit better. We also use mobility on the construction side of our business, where we’re installing new refrigeration equipment or HVAC equipment into large supermarket chains around the country.

Our construction managers and foremen live their lives on their tablets. They know the status of their job, they know their cost, they’re looking at labor, they’re doing safety reports and daily turnover reports. Anyone in our office can see pictures from any job site. They can look at the current status of a job, and this is all done over the cellular network. The business has really evolved.

Gardner: It’s interesting that you had the foresight to get your systems of record into paperless mode and were ready to extend that information to the edge, but then also be able to accept data and information from the edge to then augment and improve on the systems of record. One benefits the other, or there is a symbiosis or virtuous adoption cycle. What have been some of the business benefits of doing it that way?

Kolp: There are simple benefits on the service side. First of all, the billing cycle changed dramatically, and that generated a huge amount of cash. It’s a one-time win, whatever you would bill between 3 days and 28 days. All of that revenue came in, and there was this huge influx of cash in the beginning. That actually paid for the entire project. Just the generation of that cash was enough to more than compensate for all the software development and all the devices. So that was a big win.

But then we streamlined billing. Instead of a biller looking at a piece of paper and entering a time ticket, it was done automatically. Instead of looking at a piece of paper, then doing an inventory transfer to put it on a job, that was eliminated. Technician’s comments never made it into our system or on paper. They just sent a photocopy of the document to the customer.

Today, within 30 seconds of the person completing a work order, it’s uploaded to the system. It’s been generated into PDF documents where necessary. All the purchase order and work order information has entered into the system automatically, and an acknowledgement of the work order is sent to our customer without any human intervention. It just happens, just part of our daily business.

That’s a huge win for the business. It also gives you data for things that you can start to measure yourself on. We have a whole series of key performance indicators (KPIs) and dashboards that are built to help our service managers and regional directors understand what’s going on their business.

Technician efficiency

Do we have customers where we’re spending a lot of time in their store-servicing them? That means there is something wrong. Let’s see if we can solve our customer’s problems. We look at the efficiency of our technicians.

We look at the efficiency of drive times. That electronic data even led us into automatic dispatching systems. We have computers that look at the urgency of the call, the location of the call, and the skills necessary to do that service request. It automatically decides which technician to send and when to send them. It takes a work order and dispatches a specific technician on it.

Gardner: So you’ve become data-driven and then far more intelligent, responsive, and agile as a result. Tell me how you’ve been able to achieve that, but at the same time, not get bogged down in an application development cycle that can take a long time, or even find yourself in waterfall-type of affair, where the requirement shift rapidly, and by the time you finish a product, it’s obsolete.

How have you been able to change your application development for your mobile applications in a way that keeps up with these business requirements?

Kolp: We’ve worked on three different mobile platforms. The claim in the beginning was to develop once and just update and move forward. That didn’t really work out so well on the first couple of platforms. The platforms became obsolete, and we essentially had to rewrite the application on to a new platform for which the claim was that it was going to survive.

This last year, we converted to the Kony platform, and all indications so far are that that platform is going to be great for us, because we’ve done a whole bunch of upgrades in the last 12 months on the platform. We’re moving, and our application is migrating very quickly.

So things are very good on that side and in our development process. When we were building our new application initially, we were doing two builds a week. So every couple of days we do a little sprint up. We don’t really call them sprints, but essentially, it was a sprint to add functionality. We go into a quick testing cycle, and while we’re testing, we have folks adding new functionality and fixing bugs. Then, we do another release.

At the current stage where we are in production really depends on the needs of the business. Last week, we had a new release and this week, we’re having another release as we fix some small bugs or did enhancements to the products that came up during our initial rollout where we are making changes. It’s not that difficult to roll out a new version.

We send an alert. The text says that they have got a new version. They complete the work order that they’re on, they perform an update, and they’re back in business again. So it’s pretty simple.

Field input

Gardner: So it’s a very agile, iterative, easily adaptive type of development infrastructure. What about the input from those people in the field. Another aspect of agile development isn’t just the processes for the development itself, but being able to get more people involved with deciding features, functions, and not necessarily forcing the developers to read minds.

Has that crept into your process? Are you able to take either a business analyst or practitioner in the field and allow them to have the input that then creates better apps and better processes?

Kolp: In our latest generation application, we made tremendous changes in the user interface to make it easier for the technicians to do their job and for them to not have to think about anything. If they needed to do something, they knew what they had to do. It was kind of in their face, in other words. We use cues on screens by colors. It’s something that’s required for them to do, it’s always red. If there is an input field that is optional, then it’s in blue. We have those kinds of cues.

We also built a little mini application, a web app, that’s used by technicians for frequently asked questions (FAQs). If they have got some questions about how this application works, they can look at the FAQs. They can also submit a request for enhancements directly from the page. So we’re getting requests from the field.

If they have a question about the application, we can take that question and turn it into a new FAQ page, response, or new question that people can click on and learn. We’re trying to make the application to be more driven by the field and less by managers in the back office.

Gardner: Are there any metrics yet that would indicate an improvement in the use of the apps, based on this improved user interface and user experience. Is there any way to say the better we make it, the more they use it; the more they use it, the better the business results?

Kolp: We’re in early stages of our rollout. In a couple of weeks we’ll have about 200 of our 600 guys on the new application, and the guys noticed a few things. Number one, they believe the application is much more responsive to them. It’s just fast. Our application happens to be on iOS. Things happen quickly because of the processor and memory. So that’s really good for them.

The other thing they notice, is that if they’re looking at assets and they need to find something in the asset, need to look up a part, or need to do anything, we’ve added search capability that just makes it brain-dead simple to find stuff that they need to look for. They can use their camera as a barcode scanner within our application. It’s easy to attach pictures.

What they find is that we’ve made it easier for them to add information and document their call. They have a much greater tendency to add information than they did before. For example, if they’re in their work order notes, which for us is a summary, they can just talk. We use voice to text, and that will convert it. If they choose to type, they can type, but many of the guys really like the voice to text, because they have big fingers and typing on the screen is a little bit harder for them.

What’s of interest?

Gardner: We are here at Kony World, Hal. Did anything jump out at you that’s particularly interesting? We’ve heard about solution ecosystems and vertical industries, Visualizer update, some cloud interactions for developers? Did anything really jump out at you that might be of interest for the coming year?

Kolp: I’m very interested in Visualizer 2.0. It appears to be a huge improvement over the original version. We use third-party development. In our case, we used somebody else’s front-end design tool for our project, but I really like the ability to be able to take our project and then use it with Visualizer 2.0, so that we can develop the screens and the flow that we want and hand it off to the developers. They can hook it up to the back end and go.

I just like having the ability to have that control, and now we’ve done the heavy lifting. For the most part, understanding your data, data flow or the flow of the application is usually where you spend quite a bit more time. For us to be able to do that ourselves is much better than writing on napkins or using PowerPoint or Visio to generate screens or some other application.

It’s nice because ultimately we will be able to go use Visualizer, push it into the application, take the application, push it back into Visualizer, make more changes, and go back and forth. I see that as a huge advantage. That’s one thing I took from the show.

Gardner: With this journey that you’ve been on since 2006, you’ve gone quite a way. Is there anything you could advise others who are perhaps just beginning in extending their enterprise to that mobile edge, finding the ways to engage with the people in the field that will get them to be adding information, taking more intelligence back from the apps into their work? What might you do with 20-20 hindsight and then relate that to people just starting?

Kolp: There are a couple of things that I’ll point out. There was a large reluctance for people to say that this would actually work. When your business says that you can’t mobilize some process, it’s probably not true. There’s this resistance to change that’s natural to everyone.

Our technicians today, who have been on mobile applications, hate to be on paper. They don’t want to have anything to do with paper, because it’s harder for them. They have more work to do. They have to collect the paper, shove the paper in an envelope, or hand it off to someone to do things. So they don’t like it.

The other thing you should consider is what happens when that device breaks? All devices will break at some point for some reason. Look at how those devices are going to get replaced. We operate in 20 states. You can’t depend upon the home office to be able to rush out a replacement device for your suppliers in real time. We looked pretty hard at using all kinds of different methods to reduce the downtime for guys in the field.

You should look at that. That’s really important if the device is being used all day, every day for a field worker. That’s their primary communication method.

Simpler is better

The other thing I could say is, “simpler is better.” Don’t make an application where you have to type-in a tremendous amount of data. Make data entry as easy as possible via taps or predefined fields.

Think about your entire process front to back and don’t hesitate to change the way that you gather information today, as opposed to the way you want to in the future. Don’t take a paper form and automate it, because that isn’t the way your field worker thinks. You need to generate the new flow of information so that it fits on whatever size screen you want. It can’t be a spreadsheet or it can’t be a bunch of checkboxes and stuff, because that doesn’t necessarily suit the tool that you are using to drive the information gathering.

Spend a lot of time upfront designing screens and figuring out how the process should work. If you do that, you’ll meet with very little pushback from the field once they get it and actually use it. I would communicate with the field regularly if you’re developing and tell them what’s going on, so that they are not blind-sided by something new.

I’d work closely with the field in designing the application. I’d also be involved with anybody that touches that data. In our case, it’s service managers. We work with builders, inventory control, purchasing people, and timecards. All of those were pieces that our applications touch. So people from the business were involved, even people from finance, because we’re making financial transactions in the enterprise resource planning (ERP) system.

So get all those people involved and make sure that they’re in agreement with what you’re doing. Make sure that you test thoroughly and that everybody signs off together at the end. The simpler you can make your application, the faster you can roll it out, and then just enhance, enhance, enhance.

Add a new feature if you’re starting something new. If you’re replacing an existing application, it’s much harder to do that. You’ll have to create all of the functionality because the business typically doesn’t want to lose functionally.

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Ariba elevates business user experience with improved mobile apps and interfaces

LAS VEGAS — Ariba today announced innovations to its cloud-based applications and global business network designed to deliver a personal, contextual and increasingly mobile-first user experience for business applications consumers.

Among the announcements are a newly designed user interface (UI) and a mobile app based on the SAP Fiori approach, both due to be available this summer, for Ariba’s core procurement, spend management, and other line of business apps. Ariba, an SAP company, made the announcements here at the Ariba LIVE conference in Las Vegas.

“This gives us a cleaner approach, more tiles, and a Fiori-like user experience,” said

Chris Haydon, Ariba Senior Vice President of Product Management. “Users see all the business network elements in one dashboard, with a native UI for a common, simple experience for Ariba apps upstream and down.”

The new UI offers an intuitive experience that simplifies task and process execution for all users – from frontline requisitioners to strategic sourcing professionals, said Ariba. [Disclosure: Ariba is a sponsor of BriefingsDirect podcasts.]

The goal is to make the business users want to conduct their B2B processes and transactions using these new interfaces and experiences, in effect having them demand them of their employers. User-driven productivity is therefore a new go-to market for Ariba, with simplicity and everywhere-enabled interactions with processes a growing differentiator.

“We want to make it easier to use the central procurement department, rather than to go around it,” said Cirque du Soleil Supply Chain Senior Director Nadia Malek. Ariba Sourcing, she said, helps Montreal-based Cirque standardize to keep all of its many shows going around the world. And by having all the Ariba apps funnel into SAP, they can keep costs managed, on a pay-as-you-go basis and still keep all the end users’ needs met and controlled.

In another nod to consumer-level ease in business, B2B enterprise buying on Ariba increasingly looks like an Amazon consumer online shopping experience, with one-click to procure from approved catalogs of items from approved suppliers. Search-driven navigation also makes the experience powerful while simple, said Haydon.

Behind the pretty pictures is an increasingly powerful network and platform, built on SAP HANA. Network-centric and activity-context-aware apps allow Ariba to simplify processes across the cloud, on-premises, and partner ecosystem continuum for its global users. The interface serves up only what the users need at that time, and is context aware to the user’s role and needs.

“The future of commerce means processes across multiple business networks, on any device, with shared insights by all,” said Tim Minahan, Senior Vice President at Ariba.

Scale and reach

The Ariba Network supports these apps and services in new terms of scale and reach as well. Every minute the Ariba Network adds a new supplier, and every day, it fields 1.5 million catalog searches while generating $1.8 billion in commerce, said Ariba.

“Business strong, consumer simple” is the concept guiding Ariba’s user experience development, said new incoming Ariba President Alex Atzberger. And SAP is increasingly focusing on far-reaching processes among and between companies to better control total spend and innovate around the analytics of such activities, he said.

Some 1.7 million companies are now connected by Ariba, more than for Alibaba, eBay and Amazon combined, said Atzberger.

Ariba is also in the process of integrating with SAP HANA and other recent SAP acquisitions, including Concur and Fieldglass. Haydon emphasized that Ariba is striving to make those integrations invisible to users.

“Ariba is leveraging SAP HANA to integrate Concur, S4HANA, and Fieldglass, with no middleware needed,” said Haydon.

The scale and sophistication of the Network and its data is also enabling new levels of automated risk management to Ariba and SAP users. The Hackett Group says more than half of the companies surveyed recently see reducing supply risk as a key initiative.

The scale and sophistication of the Network and its data is also enabling new levels of automated risk management to Ariba and SAP users. ata-driven risk management is increasingly being embedded across supply chain and procurement apps and processes, says Ariba. Now, the risk of businesses having forced labor or other illegal labor practices inside their supply chain is being uncovered.

Made in a Free World

Also at the conference here, Ariba announced a alliance with San Francisco-based non-profit Made in a Free World to help eliminate modern day slavery from the supply chain. Ariba has donated part of the Ariba LIVE registrations to help fund further development of Made in a Free World’s supply chain risk analysis tool, which improve a business’s visibility of their supply chain.

Also at Ariba LIVE, the company presented T-Mobile with its 2015 Network Innovation Award. T-Mobile was an early adopter of Ariba Pay, a  cloud-based solution designed to digitize the “pay” in “procedure-to- pay.” Prior to Ariba Pay, the payment process was largely paper based, making it costly and inefficient.

“Emerging payment technologies are certainly disruptive,” said Atzberger. “But as T-Mobile proves, disruption fuels innovation. And innovation drives advantage. And for this, we are pleased to recognize the company with the 2015 Network Innovation Award.”

But the mobile enablement and so-called “total user experience” improvements seemed the most welcomed by the audience. The experience factor may well prove to be where Ariba and SAP can outpace their competitors and fundamentally change how enterprises use business services.

For example, for all Ariba users — from technicians on the plant floor to sales people in client meetings — the company now provides a mobile app, Ariba Mobile, which is available from the Apple App Store or Google Play. With the app, users can quickly and easily view, track, and act on requisitions from their mobile devices, transforming the speed and efficiency with which work gets done. Ariba’s approach is not just about replicating existing user interactions onto mobile devices, but providing continuity between desktop interactions and working outside the office.

Easy access

“The Ariba Mobile app provides our requisition approvers with easy access to requisitions anywhere, anytime and gives them the information they need to make spend decisions while keeping up with global demand,” said Alice Hillary, P2P Enablement Lead at John Wiley and Sons. “Using the app, approvers can view their requests in a sleek interface and access details in an easier-to-read format than general emails. The feature has also helped us to address security concerns with approvals done through email.”

And as part of planned innovations, Ariba will provide access to templates, best practices, videos and more within many of its applications, beginning with its Seller solutions. Users will also be able tap directly into the Ariba Exchange Community, enabling collaboration within the context of their apps with professionals who have the expertise, experience, and knowledge to help them execute a process such as responding to an RFP or creating an auction.

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