When applied to business, the application of analytics commonly associates with better decision making, increasing profits, reducing expenses, growing revenue, improving customer satisfaction, enabling compliance and reducing risk, or enhancing competitive positioning.
As challenges posed by external forces—especially economic ones—make many organizations get by with less, one would think they would target some or all of these goals for analytics. You probably think you know which one is the most prevalent and which one would help the bottom line the most. However, in terms of analytics success, a majority of organizations undertake only one of these goals, and that goal correlates most highly with BI efforts considered highly successful. The remaining goals correlate less often with the highest level of BI success, and fewer organizations report as much success achieving those goals.
All organizations should go back to the roots of decision support systems (DSS) theory. Although using analytics to help employees make better decisions in general is both laudable and understandable, data-driven decision making should focus first on lowering costs, increasing revenue, improving customer satisfaction, ensuring compliance and reducing risk, and cumulatively increasing profits. A successful BI strategy requires both core functionality (the application of which typically yields operating efficiencies) and Hyper-Decisive® enablement (that is, using analytics competitively to instantaneously process vast arrays of data and information, and deliver actionable insights to a growing community of data-driven decision makers).You do not have permission to access this document. Make sure you are logged in and/or please contact Danielle with further questions.