By Andrew Wells and Kathy Chiang
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:
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 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.
2. 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.
3. 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 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 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.