Leaders, managers, executives do not have a "level playing field" when entering into a decision-making process. Often without design, and often without really thinking about a structured process or approach, we participate in decision-making without being in a DMI functional environment. Increasing the quality, scope, and focusing the DMI environment can lead to improved success.

We are only functional when we are in a functioning environment. To use an extreme example, a baseball player without a bat will have a poor chance of getting a hit! While this is true throughout life’s journey, it has a particularly important meaning in the sphere of data driven decisionmaking.

To achieve functional decisionmaking, a key ingredient to successful decisionmaking, you must have a functional decisionmaking environment. For some rare individuals it suffices for the environment to be their maverick ability. More typically, the functional environment includes many intrapersonal and intra-organizational factors -- and the ability to ask the right questions and get the right answers.

How does one evaluate the adequacy of the functionality of their environment? Surely a trend of successful, or poor, decisions is one measure. But, there are more proactive steps that can be undertaken. You can very likely develop an environment that will have a better chance of supporting functional decisionmaking.

A functional decisionmaking environment (FDE) is based in part on a specific decisionmaker, set of people, and organization. There is a model involved. A semi-generic model must be re-specified for each individual and their environment.

You can help set your own destiny for insuring that you are in your functional environment by applying a model that has certain key parameters. Such a model for data driven decisionmaking environment is not simply based on the ability to “drill down” into a data warehouse to get the answers. This is a very small part of the DMI infrastructure. It would be very incorrect to think that by only having more data and the ability to access it more effectively, that the decisionmaker is in operating in an adequate FDE. These are perhaps necessary conditions, but they are not sufficient. Rather, it is critical to understand the dynamics of cause and effect which often transcend one’s personal ability to understand the required infrastructure to develop and maintain an optimal FDE.

Some key elements that go into the structural development of an FDME:

  • Component-oriented architecture of all DMI elements
  • Ability to interface and link key data components to models
  • Availability of data driven cause and effect models
  • Personal ability to optimally use today’s IT DMI tools