Thought Leaders in Big Data: Interview with Dr. Saed Syad, Chief Data Scientist at AdTheorent (Part 3)

Saed Syad: The second thing is that there are many issues related to those types of techniques, which is independence between variables. To come up with a solution to solve this issue, they found a remedy for it by creating enneagrams, just to find the correlation between different keywords. If I have a data set with 100 variables, I cannot just use 100 different enneagrams or a combination of the values I have. It is a huge amount of work, and there are endless combinations of those values. We needed something more, something that could support the majority of the predictive modeling we had, at least at a linear level.

The way I designed real-time learning machines is that I first cut the connection between data and model. Then there is another component called learner. Learner processes the data and updates a table, which we call basic element table (BET). One of the components of this basic element table is frequency. At this stage, basic element tables – if you just use then at count – are equal to the search engine structure. Besides count, we save some x, some y, some xy, some xy², some xy³ or some xy⁴, if we need it. But any component we add to the basic element table should support the core six features. We can not add minimum or maximum to that table. Those are scalable incrementally but not scalable decrementally. We can find a solution for this, but generally those types of parameters are not scalable.
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Thought Leaders in Big Data: Interview with Dr. Saed Syad, Chief Data Scientist at AdTheorent (Part 2)

Saed Syad: With big data, even without having any predictive modeling, we can answer some of those questions by looking into the database. What we call targeting or retargeting is some sort of a database query that makes it faster than you do it in memory. Just having the amount of data we have it is not an easy job. We need some sort of abstraction and predictive modeling. What type of predictive modeling can we use? All the traditional predictive modeling goes through three steps: data, modeling, score. It means I have the data, I build a model and I use the modeling to score. >>>

Thought Leaders in Big Data: Interview with Dr. Saed Syad, Chief Data Scientist at AdTheorent (Part 1)

Dr. Saed Sayad is the chief data scientist at AdTheorent, a platform that provides real-time analytics for advertisers. Saed is an adjunct professor at the University of Toronto and has more than 20 years of experience in data mining and statistics. In this interview he gives us detailed and technical information about the process of real-time analytics and how it is applied to website advertising. He also addresses the three challenges of this process: speed, accuracy, and scalability.

Sramana Mitra: Saed, let’s start with introducing our audience to you, your personal background, as well as the company AdTheorent. >>>

Thought Leaders in Big Data: Interview with Chris Carter, CEO of Approyo (Part 6)

Sramana Mitra: You became one of the major SAP Hana developers. You are part of the SAP Startup Focus program. Now you have core expertise in doing facility management and energy optimization for hospitals. When you go back to the SAP Startup Focus program, are they able to match you up with all the hospital energy optimization opportunities that SAP has in their customer base? >>>

Thought Leaders in Big Data: Interview with Chris Carter, CEO of Approyo (Part 5)

Sramana Mitra: How would that be followed through? Let’s say I am in the store, and I am looking for something that the store knows because of all the implementation you have done, integrating the POS System. How is that last mile bridged? Who is approaching me? Is there floor staff who gets a notification on his/her mobile phone and then go and talk to me?

Chris Carter: There are a couple of ways. One is considered to be the ultimate Big Brother watching over your shoulder. There are applications out there that can be integrated with SAP Hana that are on your iPhone or Android, that when you walk into a retailer or a grocery store it will grab the IP of your phone – because there are Wi-Fis within that organization – and that application will notify the store’s POSs, saying, “You have entered store number 362 in Palo Alto, California.” Based on your comments, it will figure out who you are, what you have done, etc. and will send you an electronic buzzer that says, “We have a sale on Columbia gear. You tweeted about this on July 25, 2:26 p.m. If you are interested, we have 25% off, and here is another coupon with an additional 5% off.” >>>

Thought Leaders in Big Data: Interview with Chris Carter, CEO of Approyo (Part 4)

Sramana Mitra: Can you talk about what kind of heuristics you are using in the wealth management scenario?

Chris Carter: When you look at wealth management, you have structured and unstructured data. You have all those nice Excel spreadsheets, documents, and plenty of data points about what you are doing when it comes to your financial loads or portfolios. Then you have to be able to marry that up to all of these different obligations they have. In the past you would have to bring up this document and that document, then see if they match up, if it is the right account number, etc. >>>

Thought Leaders in Big Data: Interview with Chris Carter, CEO of Approyo (Part 3)

Chris Carter: We are very fortunate to have very strong staff members who have focused on that within not only the SAP or big data ecosystem, but on the healthcare system. They know how to benchmark certain statistics. I didn’t even know the linen side or the cafeteria side to the story. They brought that in to be able to combine and paint a picture that everybody was able to see. To me, it was quite revolutionary because I didn’t think of it. They thought about it on a daily basis because they had dealt with it in the healthcare realm. >>>

Thought Leaders in Big Data: Interview with JR Reagan, Federal Chief Innovation Officer at Deloitte Services (Part 6)

Sramana Mitra: What is the source of the problems? Is it the customers who bring the problems to you, or do you bring the problems to the customers?

JR Reagan: We normally have “Art of the Possible” sessions – they are about two hours long. Sometimes [people] are very eager and want to launch off into something, and sometimes they don’t really know. >>>