Four factors that drive effective self-service analytics


When it comes to analytics, which usually includes the major categories of business intelligence (BI), predictive analytics and performance management, the idea of self-service isn’t something new. One could argue that the first true self-service analytical tools were spreadsheets, the rise of which started in the 1980s. The 1990s saw the emergence of business intelligence tools which became very popular, not just for IT departments struggling to build reports and OLAP (online analytical processing) capabilities, but also for many user-centric departments that were hungry to let people perform business analysis for themselves. In the 2000s, the growth of BI and the emergence of more advanced analytics have led to an enterprise requirement allowing for more crossfunctional insight and performance management. Today, we stand at the brink of an explosion of analytics, with big data and business demand driving the need for even more self-service capabilities. This white paper takes a look at the current challenges that many organizations face in addressing this growing need. It examines the different types of users and stakeholders who need or want more self-service, and lays out four factors that are critical to realizing the full potential of self-service analytics.

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