Data Strategies for Accelerating the ROI of Analytics
How does an effective data strategy increase the ROI of analytics?
Many businesses today are expanding their analytics programs because they know that analytics is an established way to achieve important business goals, such as revenue lift, increased competitiveness, customer retention, customer account growth, new product development, operational efficiency, and cost containment. To get the greatest return on investment (ROI) from each new analytics initiative or implementation, these same organizations are also extending the data integration infrastructure that provides data for analytics. After all, the old adage “garbage in, garbage out” still applies, even with modern analytics.
The outcome and effectiveness of any analytics application will be influenced by the volume, breadth, sources, models, quality, and semantics of the data provisioned for it. Furthermore, data requirements for analytics vary across the many forms of analytics, such as methods based on reporting, dashboards, statistics, data mining, text mining, natural language processing, graph, clustering, neural net, predictive modeling, machine learning, and so on. Hence, to achieve the fullest ROI and shortest time to use for analytics, organizations must employ data integration and other data management techniques and tools to provide just the right data, in just the right format and condition, on a per-use-case basis.
Analytics professionals must thoughtfully align their solutions with business goals, and data management professionals must provision data that is tailored to specific analytics solutions. Both of those demand some kind of strategy. Otherwise, analytics is weak, poorly targeted, and of minimal business value and ROI. To understand this better, let’s drill into strategy, data strategy, and analytics ROI.
WHAT IS DATA STRATEGY?
Data aside, Webster’s New World College Dictionary defines strategy as “the science of … maneuvering forces into the most advantageous position prior to engagement.”1 This definition was penned for a military context. Yet, it applies very well to data integration, which maneuvers and combines data assets into the most advantageous models and aggregations prior to using the repurposed data to engage with customers, partners, business processes, and analytics.
Strategy can also involve “a plan for action” and “skill in managing and planning.” In that spirit, a data strategy is usually documented in a plan
that will guide the integration and management of data for a specific purpose such as analytics. Furthermore, the best data strategies can flexibly
apply to individual analytics applications, as well as long-term, multiphase analytics programs
In short, you need a data strategy—with plans, people, skills, and tools to back it up—for each analytics application.