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Rishi Sapra

Rishi is a Microsoft Data Platform Most Valuable Professional (MVP), being formally recognised by Microsoft for his community contributions. He is a Chartered Accountant (ACA) with a background in Financial Modelling and Consulting in Financial Services, having worked at Deloitte, HSBC, Barclays and KPMG prior to his current role as a Group Manager at Avanade. Rishi is a co-founder of Learn Data Insights (www.learndatainsights.com)

LinkedIn / Twitter

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Chris Barber 

Chris is a Microsoft Data Platform Most Valuable Professional (MVP). He is a Senior Consultant at Avanade, CIMA certified accountant and recipient of an  IBM Academic Achievement Award received during his MSc in Business Analytics (University of Warwick).

LinkedIn

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Naveen Ganpat

Naveen is a credit analyst having worked at a number of banks and asset managers including ING Bank in the Netherlands, Commerzbank, Friensdlife (now Aviva). He is currently working in the Treasury department of a leading challenger bank. Naveen is also a

co-founder of Learn Data Insights (www.learndatainsights.com)

LinkedIn

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Alp Akdeniz
Kyle A Mueller 
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Rajan Chavda

Alp is a certified Microsoft Power BI professional who is very passionate about educating users on Power BI, DAX, and M. He is currently working as a consultant specialized in Power BI at Avanade. Prior to Avanade, Alp worked in Corporate Finance for CA Technologies, Intrado, and Broadridge Financial Solutions. He has also presented in multiple Dashboard-in-a-day events in Manhattan.

 

LinkedIn

Kyle is a Logistics and IT Solutions fanatic with experience spread across multiple industries including pharmaceutical, hospitality, higher education, and  non-profit.  As a specialist in DAX and PowerQuery M languages, he is active in the Global Power BI User Group offering advice and assistance to other users of Power BI and PowerPivot

 

LinkedIn / Twitter

Rajan is a consultant in the Microsoft BI stack - specialising in designing and implementing effective Power BI reports that generate actionable insight. He has worked with clients from a range of industries including retail and hospitality where he has been able to transform their reporting solutions into a compelling visual data story.

LinkedIn

Gain hands-on experience with developing solutions in low-code/

no-code platforms that can deliver immediate business value within the finance function 

Network with peers and understand how the Microsoft technology

stack is being used in Finance

across different companies/industries ​ 

Keep up to date with the latest developments in these

technology platforms and

how new features can be applied within finance  

Young Business Colleagues
A space (virtual and physical) where finance professionals can come together to learn about modern approaches to financial reporting & analytics using the Microsoft Power Platform tool set

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Last Webinar - 10th December 2020 (6pm BST)

In this video, Imke takes us through carrying out advanced scenario modelling in Power BI, with a focus on Power Query. The scenario here is that we want to model Shipping/Marketing/Overhead costs as calculate sales revenue/net profit.

These depend on a number variables including the version of the product (Standard/Premium - each of them has a different set of fixed and variable costs) and the sensitivity to drivers such as price, quantity and exchange rates. Using a series of simple tables in excel as inputs, Imke shows how to produce:

• Sensitivity Analysis charts in Power BI showing how sensitive net profit is to a % change in Sales of the Premium/Standard product • Tornado charts showing the specific changes (in either direction) for a particular driver: e.g. showing how much net profit increases or decreases if price increases or decreases by 5%.

• Having different scenarios (with the excel input defining which drivers can be flexed and by how much under each scenario) selectable through a slicer to show the impact of each scenario on net profit.

• A monte carlo simulation with the number of iterations (e.g. 1000 or 10000) defined through a Power Query parameter, and the probability of the various driver values - in this case exchange rates - calculated using actual historical exchange rates from Yahoo Finance.

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