Enterprise DNA Learning Summit – Session 2 Preview

4 comments

After a fantastic first session of the Enterprise DNA Learning Summit we’re powering into the next one.

Firstly thanks to all who are taking part. We’ve hit around 500 now from all around the world! Great to have a meeting of the minds during these virtual workshops. The feedback around what we have gone through thus far has been very positive, so appreciate it.

Just a reminder if you haven’t already you can still register for the next two sessions and also get hold of the replay and resources from session 1. You can do that here – Enterprise DNA Learning Summit.

In the first session we really built the base of our solution really well. We set up the data model and developed our core calculations. We’ve also set our visualization format.

So, now we’re just going to build on this. This is exactly how you should complete your own solutions.

During session 2 of the summit we’re going to move to more intermediate and advanced topics and techniques.

In summary we’re going to cover;
Time intelligence
Cumulative totals
Measure branching
Budgeting / Forecasting

So plenty there for all Power BI users. I’ll also be covering many other techniques here and there and just like last session can take questions as we go and also at the end.

I will likely have a bit more time to run through some of my visualisation best practices as we ran out of time in the last webinar.

To set up our budgeting analysis there’s a few things to cover around the data model and also specifically in how to actually get these numbers. I’m going to show a cool trick around how you can do it fast in Power BI.

Ok that’s enough for the preview as the real value and learning will happen during the virtual session.

See you then!

Chrs
Sam

4 comments on “Enterprise DNA Learning Summit – Session 2 Preview”

  1. The first session was great. Sam was able to break it down to where even a rank amateur would be able to build a stable data model and create a nice looking report from it.

    Like

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