DAX Pareto Calculation - Phil Seamark on DAX

Power BI
The Future of Business Intelligence Part 2: Dismantling the Supply Chain and Planting the Forest.
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.
On object | Public Preview (Opt-in) | Microsoft Power BI Blog | Microsoft Power BI
Data Quality: The Missing Link in Your Cloud Data Migration
What is Snowpark — and Why Does it Matter? A phData Perspective
Power BI adoption roadmap conclusion - Power BI
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.
e
Icon Maker by Raycast
Power BI Ideas site improvement updates
PowerBi.tips tools
When can I use OFFSET DAX function in Power BI
GitHub - tirnovar/Power_BI_REST_API_PQ: Power BI REST API custom functions
Chris Webb's BI Blog: Finding The Tables, Columns And Measures Used By A DAX Query In Power BI
Using Azure Log Analytics in Power BI (Preview) - Power BI
Why you should care about Power BI Deployment Pipelines
Effect of dataset changes while using deployment pipelines in Power BI
Blogs
Major Update to Power Pivot
Microsoft Ignite 2022: Do more with enterprise self-service business intelligence
Introducing Cross-tenant Power BI Dataset Sharing
Power BI Data Visualization - Ideas & Wishlist — DATA GOBLINS
Data Explorer, meet Contoso | LinkedIn
Templates | Deneb
Resources and Examples | Deneb
Power Query SDK for Visual Studio Code – Public Preview
"When a Power BI dataset refresh completes" trigger in Power Automate
Kerry Kolosko on Twitter
Data Gods
Deneb Exercise - Progress Tracker
No More Auto Aggregations in Dimension Tables
[PowerBi][UX] A Single Measure for Formatting and Button Navigation