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. I have played all these roles and have seen what HR has access to. It is very exciting for us to be able to a group of people who have been challenged with being analytic, who had great wins in their history in terms of knocking down administration, getting components under control, establishing operational effectiveness and getting houses in order. But now we need to step up in terms of evidence-based decision making and recommendations.
SM: When did you release this product?
DB: We have been performing a version of this service for the past four years. We just announced the official release of our big data for HR program within the past month.
SM: In what format have you been doing this? What is the difference between what you have been doing for the past four years and what you are doing now?
DB: The implementation of metrics and the predictive algorithms in place, versus being more loosely analytical. In our big data program we bring together data from numerous sources.
SM: So, you were not doing it in a big data way before?
DB: It was more of an informal approach. We have a grouping of customers whom we call our media customers. Those are the customers who use eQuest services to secure their contracts, negotiate the best price, and manage their billing. Our media consultants have been advising our customers over the past four years to deliver this kind of service, based on the collection of information we had. But what we have done recently is pull it together in a more formal and aggregated sense, then applied analytics and Big Data architecture to bolster what we have already been doing.
SM: Is there anything else you would like to add?
DB: Big data is one of the things that is unique about our model. I haven’t found anybody who has coined a phrase about what we are doing yet. I refer to it as big data analysis as a service. We are not selling tools or infrastructure. It is a service model, it is people, and it is a true subscription to a service delivered through people, but it is fueled and driven through our big data capabilities. We provide people with the power to not just amass all the data, but to analyze it and create their recommendations. It is a truly unique approach.
SM: You have the algorithms and infrastructure to do large-scale data analysis, and you also have the people doing it on behalf of you?
DB: Yes. That is the way we deliver the service. Our customers subscribe to the service and with it they get a team member who joins them throughout the year, starting with the discovery, then goes through quarterly reviews, with touch points in between. There is a tool they have access to for the in-between meetings and conversations, but the primary emphasis of the service is the delivery of recommendations, consultations, and advice, all based on the evidence we extract. We do all the heavy lifting: crunching, analyzing, and then recommending it back to leave our customers with a firm understanding. We give them that evidence. There is a deliverable set that they get. By the end of the day, they are getting the recommendations we are making for them.