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.)
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. Time that would have been spent cleaning up all data can be redirected toward targeted data needs and specific process improvements identified by the insights, enabling iterations with increasing levels of value.
Companies that make data their overriding priority often lose momentum long before the first insight is delivered. By narrowing the scope of these tasks to the specific subject areas needed to answer key questions, value can be realized more quickly, while the insights are still relevant.
Organizations that start with the data or process change first often end up with unintended consequences — such as data that is not extensible or processes that are ultimately eliminated — that require rework and additional resources to solve.
Speeding insights into business operations Compared with other respondents, Transformed organizations are good at data capture. Additionally, Transformed organizations are much more adept at data management. In these areas, they outpaced Aspirational organizations up to 10-fold in their ability to execute. (see Figure 7.)