My last post was apparently my 100th post since BI Polar kicked off last October.
That’s an average of right around two posts per week, although my actual writing output has been much less even and predictable than this number might suggest.
After 100 posts and a little over one year, where should BI Polar go?
What are the topics you would like to see emphasized in the next year and the next hundred posts?
For the past month or so I’ve been sticking to a three-posts-per-week schedule, but I don’t know how sustainable that is. I’m thinking about switching to a Monday-Wednesday schedule, with one post each Monday to accompany the week’s new YouTube video, plus one additional post each week. This feels like a much more reasonable long-term plan.
So… what topics or themes are you interested in? What would you like to see more of, based on what you’ve seen over the last year? I can’t promise I’ll do what you want, but I can promise that I’ll read every comment, and I expect I’ll be inspired by whatever ideas you have…
14 thoughts on “Where has the time gone?”
I have a personal wish. I know PBI dataflows are not a replacement for a DW.
But… Nowadays more enterprise required features are being announced like source control etc and the product group also starts to talk about power bi dataflows an enterprise ready in the same sentence.
I have been looking for a AdventureWorks / WideWordImporters power query and/or power bi dataflows OLTP —> OLAP transformation, kind of a OLTP —> OLAP SSIS packet replica in M
So some blog post around this subject is on my most wanted list 🙂
I’ve been wanting something like that as well. 😉
You have a lot of experience interacting with companies that adopted Power BI right? If so, I’d like to see more on the lessons you learned from that and the advice you have for business analysts and Power BI Report creators in these companies.
That’s an awesome idea – thank you!
It would be good to see you run through demos of setting up and managing data flows for all sources, large datasets and complex transformations. It would be good to understand the limitations of each source, the best way to work with each and how to manage a large number of data flows.
I would need to learn those things first. 😉
In all seriousness, please keep your expectations low for these requests, at least for the short term. Since I’m typically not elbows-deep in large real-world dataflows environments I’m hesitant to too far down this path too quickly.
Hi. I really enjoy your blog and especially posts around what your team are suggesting that your customer should do to be successful with Power BI. Culture, governance and organization for example.
When Power BI first became enterprises ready it lacked features like Data Flows or even sharing datasets cross Workspaces. This anti-pattern made sense then. With the advanced capabilities if Power Query and Data modeling offered in Datasets it’s now possible to leverage this environment as a single solution.
To add to Frederik’s point, Dev Ops and PowerBI – better lifecycle for those organizations that have got the IT department more “hands-on” with PowerBI, and it is not only Self Service.
Happy 100th! It is so impressive and inspirational how you made all this work for the community to learn. Thank you so much.
Deep dives into technical Dataflows stuff like something to accompany or expand the already existing documentation. Preferably in the video format as it is much better for showing different processes and techniques.
+ 1 for Dev Ops and ALM in BI dev/non-self-service scenarios. On the flip side, I would also like to learn about governance for large self-service programs. What are the most productive ways to use shared and certified data sets, lineage, data dictionaries, etc. in an environment with users of varying technical abilities? How do large self-service programs actually organize and share content, and is it working for them? How do companies respond to audit questions about their Power BI environments, especially regarding separation of concerns?
LikeLiked by 1 person
Pingback: Building a data culture – BI Polar