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 U.S. government collects massive 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 estimates 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 for your practice’s clients to monetize data, they need a different approach — one that starts by turning the process on its head. We recommend three approaches to help:
1. It’s about the decision. A common approach when starting an analytics projects is to ask what questions you would like the analysis to answer. But if your client’s goal is to drive actionable analytics that monetize their data, they need to start at a different point. They need to understand the decisions they would like the analytics to support. This approach, termed Decision Architecture, is radically different from conventional methods.
Understanding the decisions your client would like to support drives their direction for the rest of the analytical exercise, including the type of data and analytics needed to support the decision. The decisions a business focuses on determines the analytics it will engage with, which can range from simple metrics like ROI, or more sophisticated metrics such as a propensity or churn model.
2. Align decisions to business objectives. Knowing the goal is to provide analytics to support value-driving decisions, you need to make sure the client’s goals align with their overall corporate objectives. Through mapping their decisions to key business drivers that achieve corporate objectives, they can charting a clear path to actionable analytics.
3. Economic value and decision theory. In order to monetize data, adding economic value to a client’s decisions through the use of data science and decision theory is a must. Whereas data science helps generate insights from data about possible actions, decision theory helps structure decisions for maximum impact and feasibility. Economic value captures both the quantitative and qualitative aspects of an action and can come in various forms including revenue and profitability, market growth or process efficiency. The goal of economic value analysis is to provide the decision maker with an understanding of the economic tradeoff amongst the set of decisions they have available to them.
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 analytics on decision, you are already ahead of most analytical practitioners. Creating alignment from decisions to business drivers that achieve corporate objectives makes analytics actionable and relevant. Assessing the economic value of your decisions, 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.