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.