The current media hype around Big Data has clouded where the real opportunity is: software applications that exploit Big Data.
The infrastructure and platform plays that are grabbing the headlines are critical, but they won’t create the same long-term value as those entrepreneurs who figure out how to apply Big Data to the task of disrupting or accelerating a market.
Instead the market needs companies to provide Big Data applications that embed the insights for a particular market or business process, delivered at the right time, and to the widest possible audience. Why? Because there are simply too few skilled Big Data practitioners available for every organization that needs access to such insights to work it all out for themselves.
It’s not just the media that has been focused on Big Data infrastructure, much of the recent venture and growth equity investment activity has gravitated towards the tools and platforms needed to manage Big Data and deliver analytic insights. However the largest wave of Big Data value creation is still to come and it will focus on exploiting the infrastructure to create new applications that analytically optimize business processes.
My firm’s investment thesis focuses on software and information services companies who are bringing together three key capabilities: control or access to a business process; Big Data infrastructure and skills; and information rights to the broadest set of relevant data. Our term for this convergence is Applied Analytics and through our work with a wide range of companies we’ve developed the 11 Principles of Applied Analytics, a framework to assist strategic thinking around this opportunity.
In our experience the companies who are best positioned to be successful in exploiting Big Data are those who have focused on three key patterns.
The first is that they start with the answers their customers need rather than focusing on the data they currently have and what they think that data tells them. They work backward to figure out what is required (more data, tools, knowledge) to get to that answer.
A second trait is rather than delivering insights to a small number of business leaders; the insights are embedded directly into a business application or workflow. Getting the most out of Big Data means making sure insights are consumed, consciously or not, by the widest number of people and not just the data elite.
These companies all share a third characteristic: a strong understanding of Information Rights. In this brave new cloud-based, multi-tenant, third party data world they are methodical in their approach to acquiring, managing and using data.
Successful companies secure the appropriate rights to use key information and then proactively manage how that data is used to both maximize value creation and to ensure that the company stays on right side of the court of public opinion. They are not only compliant, they can prove compliance.
Leading companies such as Google and Amazon have blazed an applied analytics trail in display advertising and eCommerce. Those early innovations have now led to significant investments in Big Data infrastructure, the building blocks necessary to make Big Data mainstream. We look forward to the next wave of value creation that will come from the wide swath of software and Internet companies who exploit the emerging infrastructure and create new Big Data applications.
Justin LaFayette is a managing director of Georgian Partners, the growth-stage Private Equity firm he co-founded in 2008. Prior to Georgian Partners, Mr. LaFayette was Vice President of Strategy for Information Platform and Solutions with IBM Software Group.
Prior to IBM, Mr. LaFayette was a co-founder of DWL, an enterprise software company that led the Master Data Management (MDM) market. DWL was named one of the fastest growing technology companies for three consecutive year by Deloitte and Touche and was acquired by IBM in 2005. Mr. LaFayette was the recipient of Ernst & Young’s Entrepreneur of the Year award for Technology in 2001.
Follow Georgian Partners on Twitter @GeorgianPrtnrs.
Filed under: Big Data, Business, Enterprise
This article originally appeared on VentureBeat
I'd agree with this premise. The generally accepted observation, at this time, is that the 'big data' push of the moment may be great for delivering support for handling 'big data' but does not yet provide sufficient capability for those who buy in to the 'big data' premise to actually exploit their infrastructure investments. What we're looking for is the enablers to capability: the fusion, the pattern recognition, and the analytic algorithms; all packaged into easy-to-use, reliable components that can be incorporated into line of business applications.