Thought Leaders in Big Data: Franz Aman, Chief Marketing Officer, Silicon Graphics (Part 2)

Sramana Mitra: I would like to talk in more detail about some of the customers and problems you discussed. You can pick whichever your favorites are. Let’s do three or four use cases. I would like to dive into the depths of what problems you are solving and how you are solving them. What role does SGI play in terms of what you need in order to solve those problems? What does a big data solution look like in various scenarios? >>>

Thought Leaders in Big Data: Franz Aman, Chief Marketing Officer, Silicon Graphics (Part 1)

Franz Aman is the chief marketing officer of SGI (Silicon Graphics), a leader in technical computing. Franz counts more than 20 years of leadership and innovation in global product marketing, brand strategy, and communications. In this interview, he talks about the development of SGI over the past two decades and the opportunities and possibilities that lie in big data in the coming years, especially with regards to real-time data processing.

Sramana Mitra: Franz, let’s start with an introduction to SGI. I have known the company for at least 20 years. We even used SGI machines when I was at university at MIT. We were designing chips using gap tools that were used in SGI or some workstations. Today SGI looks like a very different company. What would you like the world to see when it looks at SGI today? >>>

Thought Leaders in Big Data: David Bernstein, Vice President of eQuest’s Big Data Division (Part 6)

Sramana Mitra: I have seen this in other sectors, where a company is essentially offering a software as a service, but it is also offering a business process outsourcing. We can call it SaaS-enabled BPO. There is a company we have covered extensively, called Sabrix, that has a very refined ability to do tax calculations using different countries’ laws. Algorithmically it is very complex. But they don’t only offer it as software and as a service, they also offer the people on top of it to drive the program. >>>

Thought Leaders in Big Data: David Bernstein, Vice President of eQuest’s Big Data Division (Part 5)

Sramana Mitra: So, you are basically looking at your data and doing predictive modeling.

David Bernstein: Correct. There is predictive modeling, and then there is the branch fraction index. The benchmarking ability in a real-time comparison is not a level of insight or capability that has ever been within an HR person’s grasp. I have been in this space nearly 20 years. In my time I have had a variety of positions, from being a direct recruiter, a leader of global technology teams, and a director of talent acquisition. >>>

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.  >>>