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.
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?
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.
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.
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.
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?
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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