This is part 4 of 11 from “Analytics: The New Path to Value,” a report on the findings of the 2010 New Intelligent Enterprise Global Executive Study and Research Project.
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.)
Enterprise processes have many points where analytic insights can boost business value. The operational challenge is to understand where to apply those insights in a particular industry and organization. When a bank customer stops automatic payroll deposits or remittance transfers, for example, who in the organization should be alerted and tasked with finding out whether the customer is changing jobs or planning to switch banks? Where customer satisfaction is low, what insights are needed and how should they be delivered to prevent defections?
To keep the three gears moving together — data, insights and timely actions — the overriding business purpose must always be in view. That way, as models, processes and data are tested, priorities for the next investigation become clear. Data and models get accepted, rejected or improved based on business need. New analytic insights — descriptive, predictive and prescriptive — are embedded into increasing numbers of applications and processes, and a virtuous cycle of feedback and improvement takes hold.