A recent IDG survey of IT executives underscores the growing role of data analytics in the enterprise. Asked about their tech investment plans for 2016, 50% of these IT pros said they intended to invest more in business intelligence and analytics, while 47% said they’d spend more on predictive analytics. Those were two of the top three categories (security applications was second at 49%).
But spending money on analytics and getting a good return on investment are two different things. That’s why a recent CompTIA survey shows 72% of enterprises receiving tangible benefits from their data analytics initiatives, and not 100%. As I noted a few weeks ago in this amazing run-on sentence:
Since it’s fair to assume that many, if not most, of the survey respondents who didn’t report positive results from their data projects may have been undermined by lack of direction, poor planning or execution, it’s reasonable to conclude that data analytics done right carries an even higher probability of success than indicated by the survey.
Data analytics done right: What does that mean?
First and foremost, it means harnessing your data initiatives to tangible business goals. Otherwise, you’re just letting the data geeks run amok with no productive focus. To get good ROI from an analytics program, enterprises must “link data analytics to business outcomes,” as Lisa Morgan writes over at InformationWeek.
Morgan also compiled a slideshow of 12 ways that enterprises can connect analytics to business goals. The good news: There’s a methodical process that enterprises can follow to keep their analytics efforts tied to business outcomes. The bad news: Defining business goals and unifying data strategy and business strategy are the easy parts. The real challenge comes from breaking down organizational barriers, aligning stakeholders, deciding where to put the data science team and changing processes to adapt to a data culture.
No one said managing was easy.
Are your enterprise’s analytics efforts aligned with business outcomes?