Thought Leaders in Big Data: David Bernstein, Vice President of eQuest’s Big Data Division (Part 4)

Sramana Mitra: So, you are basically optimizing job advertising campaigns for your clients based on your extensive knowledge [of] data that is flowing through your system and of the behavior of different clients. Let me ask you about trends because you are doing big data on a very large scale. What trends do you see in terms of jobs? Are there specific kinds of jobs that are easier to fill than others?

David Bernstein: I can’t answer that question in only one way. We can start answering exact questions like this by having a particular function or recruitment market in mind. From there we would begin to be able to talk about what we see in that market and which jobs are most frequently requested – if that market is the entire U.S., or if we would we narrow it down to cities or regions in a part of the country. >>>

Thought Leaders in Big Data: David Bernstein, Vice President of eQuest’s Big Data Division (Part 3)

Sramana Mitra: When you make that recommendation, what is happening in the back end?

David Bernstein: We bring forward a recommendation of the job or outlet to be used by our client – at which point in the year and for which positions [they should use certain outlets]. To get to that, we study the markets they are recruiting in. We look at recentness, frequency, response rates, and the level of the position and the way they describe the position in a posting title against the function the job encompasses. Then we look at the response patterns of candidates to help our customers understand which boards produce the most candidate flow or which boards are most appealing to our customers. >>>

Thought Leaders in Big Data: David Bernstein, Vice President of eQuest’s Big Data Division (Part 2)

Sramana Mitra: Let’s discuss what kind of big data–related work you are doing.

Dave Bernstein: Probably you are looking to know more about where big data is born – the concept of the transaction itself and the residual information. The transaction it creates has become a core base of a lot of the big data. This is the genesis of our big data. We are bringing together the information from the transaction, the results of the transaction, and the residue or the “data exhaust,” as it is often referred to. >>>

Thought Leaders in Big Data: David Bernstein, Vice President of eQuest’s Big Data Division (Part 1)

David Bernstein is the vice president of the big data division at eQuest, a major job posting distribution company. Each year, more than 250 million job postings are delivered through eQuest. In this interview, David talks about how eQuest handles big data, how information is being gathered to provide customers with accurate consulting, and trends for the future of the field of human resources.ss

Sramana Mitra: David, let’s start with some context about eQuest and you. Our audience loves niches, but those niches need to be introduced before.

David Bernstein: eQuest is 18 years old and has been primarily serving as an infrastructure for productivity support tools [as] a component of talent acquisition, which [includes] job board posting services.  >>>

Thought Leaders in Big Data: Omer Artun, CEO of AgilOne (Part 5)

Sramana Mitra: What kind of competition are you seeing on the market? I know there is a lot of activity in the data space right now, and everybody wants to be categorized on their big data these days. It sounds as though you have relatively mature capabilities since you have been in business for seven years working with real customers. What do you consider as real competition? >>>

Thought Leaders in Big Data: Omer Artun, CEO of AgilOne (Part 3)

Sramana Mitra: The clustering itself is dynamic? Are you coming up with algorithmic clusters?

Omer Artun: That is right. The platform we use runs on a Hadoop-based framework, and it is cascading frameworking. It also has algebraic contruction tecniques (ART) integrated. This way, we can run these algorithms in a scalable, multi-tenant way. Basically, each customer gets its own clusters. Or each customer gets its own propensity model.  The whole process of pattern recognition, feature generation, feature selection, classifier design, and system design is implemented into cascading in a fully configurable way. Out of the box the marketer gets a bunch of things to use automatically. For example, if they want to predict people with green eyes, we can do that, too. >>>

Thought Leaders in Big Data: Omer Artun, CEO of AgilOne (Part 2)

Sramana Mitra: What kind of customers were you working with before you raised the money? You mentioned that you built your business in a bootstrap mode, and then demand for your company or products went up. That is when you decided to move to Silicon Valley. Tell me a bit about what happened in the pre-funding incarnation of the company. Who were the early adapters of this kind of technology? >>>

Thought Leaders in Big Data: Omer Artun, CEO of AgilOne (Part 1)

Omer Artun is the founder of AgilOne, a company which provides cloud-based predictive customer analytics. He studied at Brown University and holds a PhD in computational neuroscience/machine learning and physics. He previously worked for McKinsey & Company and for the marketing division of Best Buy. Seven years ago he decided to found his own company to apply his expertise to medium-sized businesses.

Sramana Mitra: Omer, let’s start with some context.  Tell us a little bit about your company. How long has it been in business? I know you are moving from stealth mode to a more public launch, so please give us some context.

Omer Artun: I started the company about seven years ago. I bootstrapped it from no revenue to having about 40 employees when I received the first funding.  I started the company out of firsthand frustration that I had as a marketer. I used to run marketing for a division of Best Buy – Best Buy for Business – and before that I was VP of marketing at Microwarehouse, which was a direct marketer of computers and related products. Before that I did strategic consultant for McKinsey, and I have a PhD in machine learning. When I was running these marketing departments, which had millions of customers, millions of transactions, and billions of clicks, there was so much information in this data which could be utilized [to make] better marketing decisions. >>>