Big Data and Big Decisions:
Structure a process to develop the questions and measure outcomes, and then go get the answers
It seems that everyone these days (including me) is standing on the soapbox of “big data”, and the need to go beyond simple dashboards to help executives and owners make the daily decisions which may ultimately result in great business success or total organizational failure. What many of us fail to discuss is how to manage the process of getting and using data, and why it is important to know what decisions the business should focus on making before the data is collected and analysis performed.
The whole point of “big data” is to assist in the development of more informed processes and people, which are elemental to supporting successful operations. Data becomes useful information which helps to bring understanding and insight, and which results with action (information = power). While this type of analysis was once oriented almost exclusively towards financial risk and fraud identification or detection, it is now being turned to the front lines where it is more focused on customers and supply chains, and where decisions made may be more visible (and volatile).
Decisions, questions posed in the business which are answered with action, are best made when based on complete and accurate information. To accomplish this, data must be collected from all available aspects of the business, including trapping detailed operational data not often collected for summary financial reporting. With this level of data, and with a structured and purposeful approach to management of the decision-making process, the business gains agility by being analytical and informed, and is better able to sustain performance by adapting to changing conditions.
The success of any decision-making effort is enabled by management practices which recognize the need to apply structure and standards, and know the value of actionable data over instinct. The application of performance monitoring and similar tools is also essential to measure the effectiveness of not just the decision, but also the processes which supported making it. Like asking a student to produce their work, this approach helps to identify potential flaws in the decision-making process, even as apparently successful conclusions may be reached.
Today’s big data push is fueled by cloud solutions and interconnected systems delivering more, and more detailed, data than ever before. Further, analysis tools have evolved beyond summary reports in graphs and charts and now offer advanced data mining and visualization, and introducing a predictive capability based on trends and condition sets. While the availability and access to business data increases, so does the responsibility of the organization to understand WHAT decisions it is looking to make improvements in, and to create a process to monitor the effectiveness of those decisions made and acted upon.
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