The next BriefingsDirect Voice of the Customer big-data case study discussion focuses on the Tower of Babel problem for disparate data, and explores how Alation manages multiple data types by employing machine learning and crowdsourcing.
We’ll explore how Alation makes data more actionable via such innovative means as combining human experts and technology systems.
To learn more about how enterprises and small companies alike can access more data for better analytics, please join Stephanie McReynolds, Vice-President of Marketing at Alation in Redwood City, California. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.
Here are some excerpts:
Gardner: I’ve heard of crowdsourcing for many things, and machine learning is more-and-more prominent with big-data activities, but I haven’t necessarily seen them together. How did that come about? How do you, and why do you need to, employ both machine learning and experts in crowdsourcing?
McReynolds: Traditionally, we’ve looked at data as a technology problem. At least over the last 5-10 years, we’ve been pretty focused on new systems like Hadoop for storing and processing larger volumes of data at a lower cost than databases could traditionally support. But what we’ve overlooked in the focus on technology is the real challenge of how to help organizations use the data that they have to make decisions. If you look at what happens when organizations go to apply data, there’s often a gap between the data we have available and what decision-makers are actually using to make their decisions.
There was a study that came out within the last couple of years that showed that about 56 percent of managers have data available to them, but they’re not using it . So, there’s a human gap there. Data is available, but managers aren’t successfully applying data to business decisions, and that’s where real return on investment (ROI) always comes from. Storing the data, that’s just an insurance policy for future use.
The concept of crowdsourcing data, or tapping into experts around the data, gives us an opportunity to bring humans into the equation of establishing trust in data. Machine-learning techniques can be used to find patterns and clean the data. But to really trust data as a foundation for decision making human experts are needed to add business context and show how data can be used and applied to solving real business problems.
Gardner: Usually, when you’re employing people like that, it can be expensive and doesn’t scale very well. How do you manage the fit-for-purpose approach to crowdsourcing where you’re doing a service for them in terms of getting the information that they need and you want to evaluate that sort of thing? How do you balance that?
Using human experts
McReynolds: The term “crowdsourcing” can be interpreted in many ways. The approach that we’ve taken at Alation is that machine learning actually provides a foundation for tapping into human experts.
We go out and look at all of the log data in an organization. In particular, what queries are being used to access data and databases or Hadoop file structures. That creates a foundation of knowledge so that the machine can learn to identify what data would be useful to catalog or to enrich with human experts in the organization. That’s essentially a way to prioritize how to tap into the number of humans that you have available to help create context around that data.
That’s a great way to partner with machines, to use humans for what they’re good for, which is establishing a lot of context and business perspective, and use machines for what they’re good for, which is cataloging the raw bits and bytes and showing folks where to add value.
Gardner: What are some of the business trends that are driving your customers to seek you out to accomplish this? What’s happening in their environments that requires this unique approach of the best of machine and crowdsourcing and experts?
McReynolds: There are two broader industry trends that have converged and created a space for a company like Alation. The first is just the immense volume and variety of data that we have in our organizations. If it weren’t the case that we’re adding additional data storage systems into our enterprises, there wouldn’t be a good groundwork laid for Alation, but I think more interestingly perhaps is a second trend and that is around self-service business intelligence (BI).
So as we’re increasing the number of systems that we’re using to store and access data, we’re also putting more weight on typical business users to find value in that data and trying to make that as self-service a process as possible. That’s created this perfect storm for a system like Alation which helps catalog all the data in the organization and make it more accessible for humans to interpret in accurate ways.
Gardner: And we often hear in the big data space the need to scale up to massive amounts, but it appears that Alation is able to scale down. You can apply these benefits to quite small companies. How does that work when you’re able to help a very small organization with some typical use cases in that size organization?
McReynolds: Even smaller organizations, or younger organizations, are beginning to drive their business based on data. Take an organization like Square, which is a great brand name in the financial services industry, but it’s not a huge organization in and of itself, or Inflection or Invoice2go, which are also Alation customers.
We have many customers that have data analyst teams that maybe start with five people or 20 people. We also have customers like eBay that have closer to a thousand analysts on staff. What Alation provides to both of those very different sizes of organizations is a centralized place, where all of the information around their data is stored and made accessible.
Even if you’re only collaborating with three to five analysts, you need that ability to share your queries, to communicate on which queries addressed which business problems, which tables from your HPE Vertica database were appropriate for that, and maybe what Hive tables on your Hadoop implementation you could easily join to those Vertica tables. That type of conversation is just as relevant in a 5-person analytics team as it is in a 1000-person analytics team.
Gardner: Stephanie, if I understand it correctly, you have a fairly horizontal capability that could apply to almost any company and almost any industry. Is that fair, or is there more specialization or customization that you apply to make it more valuable, given the type of company or type of industry?
McReynolds: The technology itself is a generalized technology. Our founders come from backgrounds at Google and Apple, companies that have developed very generalized computing platforms to address big problems. So the way the technology is structured is general.
The organizations that are going to get the most value out of an Alation implementation are those that are data-driven organizations that have made a strategic investment to use analytics to make business decisions and incorporate that in the strategic vision for the company.
So even if we’re working with very small organizations, they are organizations that make data and the analysis of data a priority. Today, it’s not every organization out there. Not every mom-and-pop shop is going to have an Alation instance in their IT organization.
Gardner: Fair enough. Given those organizations that are data-driven, have a real benefit to gain by doing this well, they also, as I understand it, want to get as much data involved as possible, regardless of its repository, its type, the silo, the platform, and so forth. What is it that you’ve had to do to be able to satisfy that need for disparity and variety across these data types? What was the challenge for being able to get to all the types of data that you can then apply your value to?
McReynolds: At Alation, we see the variety of data as a huge asset, rather than a challenge. If you’re going to segment the customers in your organization, every event and every interaction with those customers becomes relevant to understanding who that individual is and how you might be able to personalize offerings, marketing campaigns, or product development to those individuals.
That does put some burden on our organization, as a technology organization, to be able to connect to lots of different types of databases, file structures, and places where data sits in an organization.
So we focus on being able to crawl those source systems, whether they’re places where data is stored or whether they’re BI applications that use that data to execute queries. A third important data source for us that may be a bit hidden in some organizations is all the human information that’s created, the metadata that’s often stored in Wiki pages, business glossaries, or other documents that describe the data that’s being stored in various locations.
We actually crawl all of those sources and provide an easy way for individuals to use that information on data within their daily interactions. Typically, our customers are analysts who are writing SQL queries. All of that context about how to use the data is surfaced to them automatically by Alation within their query-writing interface so that they can save anywhere from 20 percent to 50 percent of the time it takes them to write a new query during their day-to-day jobs.
Gardner: How is your solution architected? Do you take advantage of cloud when appropriate? Are you mostly on-premises, using your own data centers, some combination, and where might that head to in the future?
McReynolds: We’re a young company. We were founded about three years ago and we designed the system to be agnostic as to where you want to run Alation. We have customers who are running Alation in concert with Redshift in the public cloud. We have customers that are financial services organizations that have a lot of personally identifiable information (PII) data and privacy and security concerns, and they are typically running an on-premise Alation instance.
We architected the system to be able to operate in different environments and have an ability to catalog data that is both in the cloud and on-premise at the same time.
The way that we do that from an architectural perspective is that we don’t replicate or store data within Alation systems. We use metadata to point to the location of that data. For any analyst who’s going to run a query from our recommendations, that query is getting pushed down to the source systems to run on-premise or on the cloud, wherever that data is stored.
Gardner: And how did HPE Vertica come to play in that architecture? Did it play a role in the ability to be agnostic as you describe it?
McReynolds: We use HP Vertica in one portion of our product that allows us to provide essentially BI on the BI that’s happening. Vertica is used as a fundamental component of our reporting capability called Alation Forensics that is used by IT teams to find out how queries are actually being run on data source systems, which backend database tables are being hit most often, and what that says about the organization and those physical systems.
It gives the IT department insight. Day-to-day, Alation is typically more of a business person’s tool for interacting with data.
Gardner: We’ve heard from HPE that they expect a lot more of that IT department specific ops efficiency role and use case to grow. Do you have any sense of what some of the benefits have been from your IT organization to get that sort of analysis? What’s the ROI?
McReynolds: The benefits of an approach like Alation include getting insight into the behaviors of individuals in the organization. What we’ve seen at some of our larger customers is that they may have dedicated themselves to a data-governance program where they want to document every database and every table in their system, hundreds of millions of data elements.
Using the Alation system, they were able to identify within days the rank-order priority list of what they actually need to document, versus what they thought they had to document. The cost savings comes from taking a very data-driven realistic look at which projects are going to produce value to a majority of the business audience, and which projects maybe we could hold off on or spend our resources more wisely.
One team that we were working with found that about 80 percent of their tables hadn’t been used by more than one person in the last two years. In that case, if only one or two people are using those systems, you don’t really need to document those systems. That individual or those two individuals probably know what’s there. Spend your time documenting the 10 percent of the system that everybody’s using and that everyone is going to receive value from.
Where to go next
Gardner: Before we close out, any sense of where Alation could go next? Is there another use case or application for this combination of crowdsourcing and machine learning, tapping into all the disparate data that you can and information including the human and tribal knowledge? Where might you go next in terms of where this is applicable and useful?
McReynolds: If you look at what Alation is doing, it’s very similar to what Google did for the Internet in terms of being available to catalog all of the webpages that were available to individuals and service them in meaningful ways. That’s a huge vision for Alation, and we’re just in the early part of that journey to be honest. We’ll continue to move in that direction of being able to catalog data for an enterprise and make easily searchable, findable, and usable all of the information that is stored in that organization.
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