As we covered in prior blog posts, on Tuesday, November 5, 2013, Foley & Lardner LLP hosted the ninth annual FoleyTech Summit in Boston. One of the breakout sessions at FoleyTech featured a panel discussion of leading innovators in the realm financing for “Big Data” processing companies, called “Big Data: Finding the Money.” The panel was moderated by Beth J. Felder, a Foley partner, and included the following panelists: Mohamad Ali, Chief Strategy Officer, Hewlett-Packard; Liam Donohue, Co-Founder and Managing Director, .406 Ventures; Scott Friend, Managing Director, Bain Capital Ventures; Chris Lynch, Partner, Atlas Ventures; and Steve Papa, Chairman, Infinio. The main question the panelists faced: If you’re a growing company looking for funding from sources interested in Big Data, how do you get it?
The panelists prefaced their conversation with a discussion of “What is Big Data?” because, as several noted, the term has become overused and can be difficult to parse when considering a prospective investment. In general, they agreed that Big Data entails three concepts: velocity, volume, and variety of data processing. To show it satisfies those three v’s, a company must be able to answer concretely: what value does your company extract from the data set and how is that extraction uniquely different from what we could do to the same set yesterday? If you’re going to call yourself a Big Data company, you need to show how you’re using the multiplied volume of data to create real value.
A number of the panelists agreed that the market for data processing has changed, too: big companies are not going to be the ones who learn how to distill value from Big Data, but the specialized start-ups will. Instead, large companies will be focused on stitching together the smaller partners necessary to process Big Data in a diversified way. With so many opportunities to stitch companies from disparate sources, larger companies and strategic partners will be able to solve problems previously identified but never resolved.
The panelists debated whether platform applications for Big Data processing would succeed or not, as many start-ups in the Big Data space are trying to market platforms for that processing in lieu of specific applications for their tools. The panelists agreed that, perhaps more than in other industries, Big Data processors may need to pivot more frequently than peers in other industries, in part because value can almost certainly be extracted from any data set, but the value must be responsive to a market need for that value. To do that, you need to find out what customers’ problems are. Remember that investors can introduce you to the problems of prospective customers, too, before spending too long focusing on a problem set that may not reap value if solved. More than anything, they emphasized, focus on making an application that really works before turning too soon to platform creation.