Data-Driven Decision Making - 'Was and Is'
In the early days of computing, it usually took a specialist with a strong background in technology to mine data for information because it was necessary for that person to understand how databases and data warehouses worked. If a manager on the business side of an organization wanted to view data at a granular level, she had to reach out to the information technology department (IT) and request a report. Someone from the IT department would then create the report and schedule it to run on a periodic basis. Because the process was complex, ad hoc reports, also known as one-off reports, were discouraged.
Today, business intelligence tools often require very little, if any, support from the IT department. Business managers can customize dashboards to display the data they want to see and run custom reports on the fly. The changes in how data can be mined and visualized allows business executives who have no technology backgrounds to be able to work with analytics tools and make data-driven decisions.
Data-driven decision management is usually undertaken as a way to gain a competitive advantage. A study from the MIT Center for Digital Business found that organizations driven most by data-based decision making had 4% higher productivity rates and 6% higher profits. However, integrating massive amounts of information from different areas of the business and combining it to derive actionable data in real time can be easier said than done. Errors can creep into data analytics processes at any stage of the endeavor, and serious issues can result when they do.
So here we go, this post is dedicated to look at the best process for integrating data in to your decision making. Distilled down into five simple steps.
Step 1: Choose the right metrics. Continue on next page.