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Tallan’s Experts Share Their Knowledge on Technology, Trends and Solutions to Business Challenges

Webinar Q&A: Write-Back & Planning in Power BI

On September 2nd, Tallan and Power ON teamed up for the webinar, The Art of the Possible: Write-Back & Planning in Power BI. This was an in-depth presentation of the world’s first fully integrated BI & CPM solution for Power BI. Together we demonstrated exactly how users of any level can enter data, handle end to end workflow & approvals directly within their Power BI platform in addition to other techniques. Click here to view webinar. 

Mike Marotta (Vice President, Sales – Power ON) and James Arey (Lead Consultant, Tallan) facilitated an enlightening Q&A during the webinar. Here were some of the questions raised during the webinar as well as our answers, which have been edited for clarity:  

 

How is Power ON deciding to allocate its Q1 update across the months? 

It allocates based on the proportion that each cell makes up of the total, so a cell that makes up 10 out of a total cell with 100 will receive 10% of the increase to the total cell. Unless you use the smart formula ‘E’ which allows you to evenly distribute across the individual cells. 

 

Is there flexibility to allocate based on a period either in the past or future? In example, you updated Q3 of 2020 and only want to update the future months, so only September changes.

If that is part of the logic we created, yes, it must be built it but it will not be available right out of the box. 

 

Are we using Microsoft flows to push the data to the database? 

The simple answer is no. There is a service that the visuals call hosted either in Azure or on an on-prem server handling the write-back. 

 

Can the ML forecast be automated so you don’t need to approve adding the changes to the model? 

Of course! You can have any level of automation. We have many customers with full automation that don’t have to pick and choose. Based on the data that is in the model itself, it will do its own assessment and then run the machine learning algorithm itself automatically. 

 

Can you please share more details on how Python/R code could be leveraged? 

It’s a matter of a company determining what they want to use, or how they can engage with Tallan and Power ON. We then begin with a planning/requirement session with our own data scientists to help put together a plan of how it can be incorporated into one’s own business. Power ON is the technology piece that can incorporate into one’s reporting or give end-users the ability to click two buttons and run something, whereas Tallan dictates what scripts one wishes to use before running them.

You determine what scripts to be available, and then we simply ask if you want them to be automatically auto-populated. Once we do that, we will make it available using a SQL Database or a model, and then we can recall that using the table editor you saw today. 

 

Is there a best fit model out there that picks the leading model given? We have different models to choose from based on error – MPE, MAPE, MSE, etc. 

A best fit model, like a pre-baked ssas model to use that matches common scenarios. A licensing module has four options: 

Named: Each individual contributor has their own license 

Concurrent: All contributors share a pool of licenses 

Season: Contributors are given access for a limited number of months 

Enterprise: Unlimited contributors for a business unit, division, or organization

 

This Q&A was managed by Mike Marotta (Vice President, Sales – Power ON) and James Arey (Lead Consultant, Tallan). We appreciate everyone’s excellent questions and look forward to our next event! View our calendar here

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