Power BI

The ability to view Power BI files directly in OneDrive and SharePoint preview requires Power BI Admins to opt-in to enable the preview. The ability to share links to Power BI reports saved in OneDrive and SharePoint from Power BI Desktop preview is on by default and requires Power BI Admins to opt-out to disable the preview.

outgrows its roots simply falls over. Wave 3 of business intelligence is about a balanced approach to insight generation and distribution. It is not focused on needless growth and does not derive its value from the sheer amount of charts created, but rather its veracity and total value added.

Circulatory: If sap flows in only one direction the data tree dies. Wave 3 must support bi-directional interaction with decision makers and downstream systems to create feedback loops to drive growth and change. This must be built into the DNA of the tool.

So what the heck does this actually mean? The biggest set of changes I see coming for Wave 3 is the backswing of the ‘centralization - distribution’ technology pendulum into a place of balance, where the BI tool is a self-service insight generation platform that easily feeds into other important data processes, instead of being a black-box end point for the data supply chain.

To support this the platform must grow beyond just presenting dashboards. It needs to have an open, headless metrics store to feed AI/ML and apps

Data quality is going to matter even more than it does today, because of how compelling ChatGPT’s answers sound to humans. If your data sucks, it will very confidently give you sucky responses.

There is going to be a major ‘trough of disillusionment’ with this tech when it gets widely implemented in BI and 3% of its answers

A lot of firms may have very poor training data that results in very poor performance and a very bad initial impression.

Rooted: Just as a data tree grows best in great soil, Wave 3 requires an accurate foundation of clearly defined, valuable metrics that can feed any upstream process - whether that’s traditional BI, AI/ML or analytic/operational apps. These metrics are the foundation of balanced self-service.

There has been a ton of interesting innovation in the BI space the last few years, but they are all point solutions attempting to solve specific, narrow-scope problems

They do it elegantly and create important new ways to tell stories and collaborate with data, but they aren’t trying to upend the enterprise BI market and it’s hard to see how they could evolve in that direction.

The first iteration of such an effort is usually a valuable discovery method and learning experience. Great… treat it as such; take notes, make note of the good parts and then throw it away and start over! In Fredrick Brooks’ “The Mythical Man Month“, he cites that for most engineering projects, the first six attempts should be abandoned before the team will be prepared to start over and complete the work successfully. Brooks was a chemical engineer before working for IBM; and hopefully, our methods in the data engineering business are more effective then his 6-to-1 rule. But, this makes the case the prototypes and proof-of-concept projects are a critical part of the learning path.

Everything correlates together: As you progress through each of the steps listed above, it's important that everything's correlated from the high-level strategic organizational objectives, all the way down to more detailed action items. That way, you'll know that you're working on the right things.

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I thought it would be interesting to highlight a few of the feature additions and enhancements present in the current preview version (version 0.1.7).

Microsoft has provided several online resources related to the new SDK: