The devil is in the details. The details are in the data. The data defines the operational realities.

The process of combing through the enterprise, collecting, organizing and analyzing data to assess operational realities will make it possible to validate (or invalidate) compliance within regulatory, financial and agreed to business relationship boundaries. With an increased understanding of the operational realities made known by analysis of the data, unrecognized possibilities for improving the management of the organization will emerge. When operational realities are known, efficiencies can be introduced. Enhancements can be made. The possibilities can be expanded. But the data being analyzed must be qualitative as well as quantitative. So what data is qualitative and how can it be captured and used?

When the goal is to create or change operating policies, building data repositories that identify operational realities from which predictive policy and implementation decisions can be made is a place to start. It should be recognized that the various sources of data will be places from throughout the organization. Data comes from internal, external, inter-organizational relationships, and any stakeholder affected by the company’s operation. Data can be derived by examining the business architecture that supports (or interferes) with the company’s operation. There are other, unintended, very reliable sources of data that can be collected. It is available in the form of customers’ buying habits, competitors’ activities, their marketing materials and proposals to the company’s clients. It may be in the form of industry trends, economic news, changes affecting the supply chain and even the weather.

The collection, analysis and reporting of quantitative data will bring focus to sectors of qualitative data that will explain the reasons that the quantitative data is showing disappointing results. Think of this as a “blood test” to evaluate the company’s health. Recognition of these troublesome realities will afford the company the opportunity to implement a risk mitigation plan to correct the deficiency found by the analysis of the quantitative data.

Change management processes and considerations must move into play. Decisions have to be made about the level of change that is required and the probability and time frames for the change to be successfully implemented. Decisions about the risk tolerance that can be accommodated as change is implemented should be a considered. Provision and willingness to search for and recognize the unknown factors that will emerge as analyses of the qualitative issues is undertaken should be included in the decision making process.

Having full command of as much data as possible will assures intended consequences when decisions are made. Maximizing the company’s ability to assess and understand what is happening in every corner of the organization will mitigates risk and will keep the company on a path to maximum efficiency.

Information on gathering and using data to achieve predictive decision making is available.