Data analysis and traditional IT functions operate in separate silos in too many organizations. That means those organizations aren’t as effective as they could be, according to Krishna Venkatraman and Pamela Rice of online business lender OnDeck. Venkatraman is SVP of analytics, and Rice is SVP of technology. In part one of this two-part interview, they explained how OnDeck uses data and software to make better lending decisions than humans could. In part two, they talk about how to break down the divisions between data and IT.
The Enterprisers Project (TEP): You talk about breaking down the silos between data and technology. Since the collection and analysis of data are typically technological functions, can you tell me about the silos you see between data management and IT?
Venkatraman: Data collection can easily become a mechanical exercise, unless it is animated by a purpose. With open-air desks within just a few feet of one another, OnDeck’s data and technology teams work together in every part of the business. Together, we identify opportunities to improve decisions and operations through data insights. We then use these learnings to drive our strategy to collect, aggregate, and organize our data. The data team focuses on machine learning and analytics software services that provide intelligence at every decision point of our customer funnel, collaborating with teams from marketing to customer service. The technology team builds the underlying transactional systems, decision workflows and user experiences that carry customers through our product.
As a company, we focus on the respective strengths of the data team and the technology team that complement one another. For example, data teams need to test and iterate rapidly to refine their initial hypotheses, whereas tech teams work best with a well-documented and validated road map. Finding where the two teams meet in the middle is what makes our jobs exciting. Combining seemingly opposing aspects of a workplace environment can sometimes make for interesting times, but we find that this results in a healthier, more open culture that spreads to every division of the company. TEP: More data of all kinds is available today than ever before, but not all of it is valuable. Some companies are struggling to determine which data they should pay attention to, store, and analyze, and which data is not that useful. Has OnDeck faced challenges like these? How do you deal with the threat of data overload?
Venkatraman: In the future, we will look back and wonder, “How did we get by with so little data?” I think we are just getting started in thinking about architectures for learning systems that “automagically” get better with more data. Most companies, save for the select few web-scale companies, truly don’t have a challenge with collecting and storing data; they just have a challenge in quantifying the value of the data and as such in justifying the investment.
This is partially due to organizational silos. Different functions often make investment decisions in isolation and, when it comes to data, this can hurt. For instance, data that is useful for marketing decisions is often useful for sales and customer service as well. Taken together, the case for making the investment may be very strong, but it’s easy for each of the siloed functions to come to the opposite conclusion.
Another reason why data can seem overwhelming for companies is that many of them have been trained to believe that if they simply start collecting data, insights and value will automatically follow in the not too distant future. In fact, the opposite is true. It is very important for the company to have a set of specific objectives – be they strategic or tactical – that inform the data strategy and investment decisions.