Three Ways to Generate Profit With the Data You Already Have

It’s About the Decision. A common approach when starting an analytics project is to ask what questions you would like the analysis to answer. But if your goal is to drive actionable analytics that monetize your data, you need to start at a different point. You need to understand the decisions you would like the analytics to support. This approach, termed Decision Architecture, is radically different from conventional methods. 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.

Align Decisions to Business Objectives. Knowing the goal is to provide analytics to support value driving decisions, you need to make sure the goals align with overall corporate objectives. Through mapping your decisions to key business drivers that achieve corporate objectives, you are charting a clear path to actionable analytics.

Economic Value and Decision Theory. In order to monetize your data, adding economic value to your decisions through the use of data science and decision theory is a must. Whereas data science helps you generate insights from your data about actions you can take, decision theory helps you structure your 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 trade-off among 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.