Traditionally, organizations are tempted to start by gathering all available data before beginning their analysis. Too often, this leads to an all-encompassing focus on data management — collecting, cleansing and converting data — that leaves little time, energy or resources to understand its potential uses. Actions they do take, if any, might not be the most valuable ones. Instead, organizations should implement analytics by first defining the insights and questions needed to meet the big business objective and then identify those pieces of data needed for answers. (see Figure 6.)
Organizations should start by pinpointing the insights to be leveraged, then use readily available data to test the analytic models. Actions based on those insights will help define the next set of insights and data needed. The traditional approach of starting with a comprehensive data program creates too much lag time before insights can be put into action.
By defining the desired insights first, organizations can target specific subject areas, and use readily available data in the initial analytic models. The insights delivered through these initial models will illuminate gaps in the data infrastructure and business processes.