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)
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Chris 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).
Naveen is a credit analyst having worked at a number of banks and asset managers including ING Bank in the Netherlands, Commerzbank, Friendlife (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)
Kyle A Mueller
Alp Is a highly motivated young individual with a wide array of experience having worked in commercial and start up roles. His interest includes Finance, Analytics, and Business Intelligence. Alp has earned his Bachelors of Science in Business Management with a concentration in Finance from SUNY Stony Brook in May 2016. Currently, he is working as a Finance and Accounting Rotational Analyst at CA Technologies, a technology company specialized in producing system software for a variety of clients, a majority of which being Fortune Global 500 companies.
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
James Is a Business Intelligence, data and analytics specialist with 20 years experience covering the full lifecycle. He specialises in the Microsoft BI stack, on-premise and in the cloud including: SQL Database, Integration Services and ETL, OLAP and Tabular Analysis Services and Power BI. He’s interested in the application of Data Science to inform decision making and have gained several certifications in this space.
Most recently he’s led consulting engagements with clients across a number of industries focusing on data integration, migration, IoT and analytics in conjunction with Dynamics 365 in Azure.
Gain hands-on experience with developing solutions in low-code/
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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
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how new features can be applied within finance
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
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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