By Evangelos Simoudis, Founder and Managing Director at Synapse Partners, on behalf of the KDD-2016 Conference
Big data’s day has come: it dominates headlines and business conversations alike. As industries coalesce around it, the conversations surrounding big data (benefits, issues, challenges) are changing and maturing. Venture investors who typically look over the horizon for opportunities are accelerating their investments in big data. Yet there are a few considerations that must be taken into account before investing in startups that develop big data technologies.
The set of opportunities and challenges startups face in developing big data technologies and solutions are varied including: The application of big data analytics for strategic problem solving, the combination of data science with other skill sets, and the pairing of infrastructures with applications. These topics form the basis of a high value panel discussion by experienced venture investors as part of the 22nd annual ACM KDD-2016 Conference in San Francisco. The panel titled, Big Data Needs Big Dreamers: Lessons from successful Big Data investors and will be held Tuesday, August 16 at 1:45 PM.
Invited panelists, who are well recognized names in the VC investing world, will analyze three primary considerations:
Consideration #1: Business is waking up to the value of data to solve strategic problems.
In recent years, publications have talked about data as “the new gold”. Corporations from many industries including consumer packaged goods, healthcare, financial services, and retail have been collecting data for years and have developed an interest in data/analytics. Few companies in these industries were able to apply such analyses effectively, addressing some of the strategic problems they we’re facing such as fraud detection around new payment methods, supply chain optimization, and yield optimization. More recently, additional industries like automotive, agriculture, pharmaceutical, and logistics have started waking up to the importance of big data analytics in effectively addressing some of their vexing strategic problems such as autonomous driving, crop optimization, and personalized medicine.
Consideration #2: Solutions require capabilities that blend data science with other technologies and skill sets.
Data science provides an important set of skills for addressing these problems, but it is not the only necessary set of skills for helping corporations take advantage of the data they collect. From this perspective, ACM KDD-2016 is an important conference venue for businesses, and other organizations, that want to take strategic advantage of their data and achieve superior returns on their investments. While the ACM KDD Conferences began as forums for discussions around advances in data mining, they were never exclusive for just data scientists. KDD conferences are typically attended by practitioners from academia and industry, with varied technology backgrounds, to come together to talk about the solutions they had developed, or techniques they had used to extract information from data, and to discuss critical problems that can be addressed through data exploration and mining. However, in order to address the problems that arise from that impact, you need more than just data scientists. In recent years, the ACM KDD conferences bring together diverse constituencies, including top academic researchers, data engineers, data stewards, application developers, business executives and even business users, to discuss and enhance our understanding of every aspect of the various complex problems the world is facing today ranging from autonomous driving to zika containment.
Consideration #3: Technology infrastructure must be paired with applications.
Corporations have increased their pace in hiring data scientists and have broadly adopted proprietary, open source software and hardware data infrastructures, many of which are provided by venture-backed companies. While these investments in people and technology help, they are not sufficient for addressing the strategic problems corporations face. They must be supplemented with applications that are accessible by a broader set of corporate users. The development of such applications will necessitate the need to address a new set of issues, including the utilization of industry-specific knowledge, as well as resolving critical issues such as data security and privacy. For example, certain industries such as healthcare, may need to analyze data to better understand consumer behavior in an effort to control costs while providing improved therapy outcomes.
As investors who intend to continually and actively fund the next generation of big data startups, we need to not only look for the next great company in which to invest, but we must also understand, and appreciate, the key issues that impact the use of big data analytics in every industry where our portfolio companies will be working. For example, if investing around big data applications used by autonomous vehicles, it is important to understand the issues and regulations that impact the use of analytics in autonomous driving.
Similarly, before investing in a company whose big data application will be used to detect fraud in healthcare related payments, VCs must understand the payment value chain and the privacy issues of the healthcare industry.
My fellow panelists and I will discuss these topics in greater depth at KDD-2016 and will try to help the attendees understand some of the considerations we use as we attempt to identify the next set of promising startups working on big data solutions. Learn more about the conference and register here: http://www.kdd.org/kdd2016/.