Becoming a Decision Centric Organization
By implementing decision intelligence and enterprise decision management, organizations can transform into decision-centric entities.
Organizations that utilize decision intelligence and enterprise decision management can become completely centered around decision-making.
A decision-centric organization is focused on the effectiveness of its decisions rather than aggregated, after-the-fact metrics. A decision-centric organization recognizes decision making as a competency and devotes resources to elevate, understand and continuously improve its decisions. Knows which decisions matter and why. Which processes depend on which decisions, for instance, and which decisions impact which key performance indicators (KPIs).
Knows who makes and who owns each decision. Who decides how a type of decision should be made and who makes the decision for a given transaction or customer. Uses the right technology to support or manage each decision. Because some decisions should be made by people and so require decision support, some should be automated and so managed in decision management systems.
Enterprise decision management and decision intelligence are fundamentally decision-centric approaches. Organizations wishing to use them to become decision-centric should take a number of steps.
Invest in Identifying and Categorizing Decisions
One of the biggest challenges in becoming decision-centric is finding the decisions that matter and classifying and describing them. Most organizations find that there are many decisions driving their business, and it is a challenge to determine what technology and approach are required to make each of them effectively. Aligning decisions with the right tools, information and approaches is critical; and this requires understanding the different categories of decisions.A first step is to define the essential characteristics or dimensions of a decision. In this way, each decision type can be evaluated against these dimensions and understood more clearly.
Esential dimensions include:
Time to Outcome
Upside vs. Downside
These dimensions combine in a number of common categories of decisions with specific values, or ranges of values, for each dimension. Examples include:
Strategic Decisions: Made once or very rarely following a one-off process and using data from a wide variety of data sources. Strategic decisions have long time horizons and a large impact as well as a great deal of uncertainty. Knowledge Worker DecisionsMade regularly and following a reasonably consistent process, these tactical decisions are based on a focused set of data but rely on the expertise and judgment of the individual.
Formulaic Decisions: Decisions made by following an extensive and precise set of guidelines or policies, often imposed by an outside regulatory body. Each category requires different approaches and a decision-centric organization must first understand and categorize its decisions. Without this understanding, no coherent approach to improving the decisions – to becoming more decision-centric – is possible.
Make Data Design and Analysis More Decision-Centric: A decision-centric organization inverts what many regard as the “normal” way to design systems. Instead of starting with the data to be managed and moving up from there, a decision-centric approach starts with the decisions and ends with the data to be managed. Once the decisions are understood, the next step should be to identify the kind of analytic insight required to make those decisions and to make them more effectively.
Would understanding hidden relationships help or do large volumes of data need to be summarized? Is it a system or a person who needs the insight? What information must that insight build on? The data that is available and the information that can be derived can then be matched to these needs. If, as is often the case, the readily available data is insufficient, then organizations must consider how to source new data. They need a mind-set that their role is to generate the information and insight required rather than to use the data that is available.
Decision-centric organizations must allow time and resources to test, validate and confirm the value and effectiveness of the insight being generated to ensure that they will actually help make better business decisions, that their accuracy is appropriate, that the data quality supports the use being made of the data and so on.
Finally, and perhaps most importantly, organizations must continuously monitor and improve their support for decision making. As data evolves and business needs/market conditions change, the requirements for a decision will change.
Decision-centric organizations establish a process for decision analysis – applying performance management techniques to decision-making results and processes – so that they can explicitly monitor and improve decision making.
Getting Started – First Steps Kurt Schlegel of Gartner released a report in 2008 – Deliver Business Value With a BICC (BI Competency Center) Focused on Decision Making. In it, he “identifies the steps required to evolve business intelligence (BI) beyond reporting measures, to making great decisions.” A great first step for becoming decision centric is embodied in this quote: “Tying BI to the decision made instead of the measure reported will deliver more tangible business value.” Giving existing business intelligence staff and projects a more decision-centric point of view is the focus of this report.
The focus of a BICC, even a decision-centric one, is still going to be on manual decisions – those made by people. Getting a top-to-bottom focus on decisions is going to require not only a more decision-centric BI approach, but also a focus on the identification, externalization and management of high-volume operational decisions.
Organizations will need to adopt a business rules approach to define how decisions are made in systems. Building business rules and decision management into systems development approaches and making decisions a standard architectural component alongside processes, events and systems will be essential in the long run. For all the investment organizations have made in business intelligence and other systems, there is little discussion about how, exactly, these systems facilitate decision making. Meanwhile decision making has ceased to be the carefully controlled approach of the past – things happen too quickly and relationships change continuously. Only by understanding the types of decisions an organization makes, where they are made and how they are made, will an organization be able to get ahead.