Gartner Symposium is one of the largest gatherings of CIOs, with events around the world throughout September, October, and November. The U.S. Event in Orlando, Florida was sold out with over 10,000 attendees.
At the U.S. Symposium, I facilitated a round table discussion on how to modernize BI and analytics portfolios, presented on this year’s BI and Analytics Magic Quadrant, and conversed with more than 40 CIOs and their reports in one-on-one sessions. Here’s what was top of mind for CIOs and Symposium attendees regarding BI and analytics:
Skills Gap and How to Organize:
CIOs are concerned about analytical talent both among technical staff and within business units. And they should be! Changes in technology mean IT can no longer be order-takers and coders. CIOs know that when it comes to BI and analytics, BI teams have to shift to being enablers and facilitators rather than sole do-ers. They welcome this shift, recognizing there is no way in a digital business that they can keep up with demand. And yet, some of the BI staff are more comfortable with the order-taking, build-to-requirements model.
On the business side, while many business users are excited about being more empowered, some companies have an aging workforce that is less analytically minded. There were a handful of companies that seem to be in recession-proof industries, but for the most part, the demand for talent will be a matter of survival of the best. The best employers will attract and retain those analytically savvy workers.
All the Data:
While I don’t normally talk about data lakes and logical data warehouse, leaving that to my colleagues Adam, Mark, or Roxane, the topic naturally arises as CIOs wonder if they can get to insights faster, on new data sources. They want sensor data, external data, and semi structured data. Where should they store it? How should they model it? Modern BI and analytic tools are a perfect fit with data lakes as many have self-service preparation capabilities baked into their products, as well as self-contained analytic engines that guarantee good performance.
Governance in a Self-Service World:
A traditional world of BI has governance built in from the start because the skills to load and model the data start with IT. Nobody gets access to the data, without a sanctioned meta data layer or strict authorization levels to particular reports. Self-service BI is a scarier world for CIOs. Access to data is more broadly granted, and for sure, there is a risk that data mashups may unintentionally reveal information about a customer or individual.
Worse, data may be incorrectly manipulated or misinterpretted. But these risks can be mitigated and IT has to get used to the idea of governing after the fact. It’s always been the business who has to decide what’s an acceptable risk level and for IT to enforce those policies. See
Modernizing and Which Vendor:
Of course there were lots of discussions about which product they should use – incumbent traditional BI vendor or augment with modern BI vendor?. Many of the discussions mirrored the shape of the Magic Quadrant this year – with lots of questions on Microsoft Power BI, Tableau, and Qlik – but conversations about almost every vendor in the market, some on the MQ and some not. There does seem to be a greater acceptance of using the right tool for the right user, rather than a one vendor fits all approach. It makes for a more complex portfolio to manage but focuses on achieving insights and business outcomes.
And I was very excited to see that Data and Analytic had its own flag this year!
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