SQLBits 2024

What does Microsoft Fabric mean for a Finance/Business Analyst?

For a business team such as Finance, Microsoft Fabric allows you to go from being “IT reliant” to “IT enabled” and this session shows you how! Whether you’re a data explorer, data analyst, data engineer – or perhaps a newly coined analytics engineer – we will explore what Microsoft Fabric means for you. We will look at the need for the data platform components in Fabric, applied for a finance data use case, and consider the benefits of this being in a SaaS lakehouse architecture. We will also consider how you need to organise your Finance and IT teams to work best with Fabric so that you have the right capabilities to deliver meaningful financial insights at scale.
Based on his talk to several hundred chartered accountants for the ICAEW in the UK (https://events.icaew.com/pd/28376/microsoft-fabric-what-is-it-and-why-should-yo), Rishi will look at what Microsoft Fabric is and what it means to a finance analyst who is used to working with Excel and Power BI.

We will start with looking at how financial reporting has evolved over time towards being more self-service driven with Power BI, and some of the challenges this presents when not supported by a cloud data platform. (Transactional finance data in particular needs a lot of work to be used for analytical purposes and this needs to be done in an auditable, scalable and performant manner!).

With the need for a data platform established, we will look at how Microsoft Fabric brings much of this capability back into the hands of technically minded finance staff who are now able to self-serve on the entire analytics process. We will explore the concepts of lakehouses and OneLake being the single location for all finance data (rather than much of it being kept in local Excel files!) and consider the benefits of all analytical workloads being able to utilise this data without having to duplicate it.

Finally we will look at how Fabric needs to be implemented across the Finance and IT functions in order to reap its benefits. This includes getting the right operating model (based on data mesh or CoE models) and considering the needs and skillsets of different individuals who work with data (e.g Data Explorers, Data Analysts, Data Engineers and Analytics Engineers!).