Build it and they will come. That is the view many organizations maintain about their data lakes and data warehouses. Companies are rapidly investing in systems and processes to retain business data that they know is valuable but have no clue what to do with it. Even the government collects mass amounts of data without specific plans for using the information at the time of collection. This trend only accelerates as the amount of data being produced continues to escalate. Today, it is estimated that human knowledge is doubling every 12 to 13 months and IBM is estimating that with the build out of the “internet of things,” knowledge will double every 12 hours.
Most organizations search for value in their data by throwing teams of data scientists at the various stores of data collected hoping to find insights that are commercially viable. This approach typically results in endless hours of digging for insights and if any are found, they rarely see the light of day. In order to monetize your data, you need a different approach, one that starts by turning the process on its head. We recommend three approaches to help you monetize your data:
Understanding the decisions you would like to support drives the direction for the rest of the analytical exercise, including the type of data and analytics needed to support the decision. The decisions you focus on determine the analytics your team will undertake which can range from simple metrics like ROI or it may call for more sophisticated metrics such as a propensity or churn model.
It has been our experience that many analytical exercises start and end as an exploration of data. These explorations drive out interesting insights, but they tend not to be commercially viable and are usually a waste of time. By starting with the business objective and creating clear alignment of the insight to the objective, the relevancy of the insight is clear and the impact understood.
Decision theory is applied to help decision makers select the best choice to achieve their objectives. Structuring the decision criteria into a decision matrix laying out anticipated acts, events, outcomes, and payoffs helps managers see more clearly the full scope of their proposed actions and make more objective choices, guarding against hidden or implicit cognitive biases. Cognitive biases arise where an individual holds a view of a situation that is based on prior subjective experiences but may not be completely consistent with current reality. Confirmation bias, for example, occurs when the inclination is to look for information and analytics that support pre-existing beliefs or goals.
If you focus your analytics on your decision, you are already ahead of most analytical practitioners. Creating alignment from your decisions to your business drivers that achieve your corporate objectives makes your analytics actionable and relevant. Assessing economic value of your decision choices and employing decision theory to assist the decision maker with making the best possible choice will improve the value of your decisions. These three practices will drive up the value of your analytics and enable you to monetize your data.
By: Andrew Wells and Kathy Chiang
Andrew Roman Wells is the CEO of Aspirent, a management-consulting firm focused on analytics. Kathy Williams Chiang is VP, Business Insights, at Wunderman Data Management. They are the co-authors of Monetizing Your Data: A Guide to Turning Data into Profit-Driving Strategies and Solutions. For more information, please visit www.monetizingyourdata.com.