Previously in this blog series (Leaders' Perspectives on Big Data Initiatives: Business Goals and Leaders' Perspectives on Big Data: Technology Goals), we looked at business and technology goals for big data and analytics initiatives as described by the leaders of these projects at a wide variety of businesses today. The aim is to understand the state of the market and how people seek to drive value from the data naturally occurring in their businesses. While some organizations are truly on the cutting edge, others are taking a more conservative approach or are just now implementing new solutions. Today I’d like to look at some of the ways people actually measure the value of big data and analytics to their business.
Value is usually defined as a relationship between cost and outcomes. In many areas of IT, the cost side of the equation is generally easier to see. Actual purchase price, support contracts, and other expenses related to building a solution are typically well-documented, not least for the finance and accounting part of the business. Yet the comparative cost to a solution not chosen or to past approaches may be harder to calculate. Many businesses contended that the introduction of Hadoop or an open source database running on commodity hardware was in itself a game changer for their cost base. The comparisons on almost any metric to existing data warehouse deployments or traditional enterprise database software licensing were seen as extremely favorable. The ability to negotiate lower pricing on server hardware with internal storage was also seen as a positive change, even if more server nodes would be required to support the workloads.
However, many recognized that this cost estimate often did not include the systems integration effort, the time and opportunity costs of building their own environment, or the often expensive expertise required for development of the technology stack. These hidden costs were sometimes seen as significant barriers. While there was a natural inclination to pursue new big data analytics initiatives for the reduced cost even in the face of increased volume, more skepticism was found than in the recent past. As such, many were thinking about how to make more formal justifications of the investments. While a few still had relatively free rein, others had to pursue detailed budgeting with multi-year roadmaps and prioritization to business outcomes, and then sell their concept proposal to senior management, sometimes even including the board of directors.
The most successful initiatives were those that were not built purely around reduced cost. These projects aligned tightly to a business process change, whether you want to call that reengineering or transformation. Here, a very wide range of possibilities emerged, each very specific not just to the industry but to the particular company, department, and even function. The better efforts were able to show a definitive jump in productivity, quality, or profitability related directly to the insights gained. Others were also able to demonstrate business process cost savings, not just IT infrastructure or operations cost savings. Comparisons to prior periods or prior modes of operation were often well-documented, if not required.
All together, most of those interviewed felt they could make a very defensible case for the outcomes of their new big data and analytics projects. Even where a specific result was hard to directly attribute to the new capabilities—such as a stronger sales quarter—the greater confidence in decision making was believed to be valuable. All of this suggests that the promised benefits are available for capture as long as initiative is well-managed. More often today, the onus on the vendor of a big data or analytics solution is to demonstrate why its offering is better than alternatives. Most business leaders already believe an new initiative will prove better than the way they have managed data in the past.