Enabling a data-driven workforce using Microsoft Fabric

In the late 1980s, Microsoft Excel, PowerPoint and Word were used frequently in companies around the world, but they were sold separately. In October 1990, Microsoft introduced Microsoft Office, a suite of programs that combined these tools into one product, enabling users to have access to all the programs and tools they needed to be more efficient in their roles.

In today’s world, to continue to become more efficient, companies need to be able to collect, process and analyze their data in order to make data-driven decisions. To help organizations do so, Microsoft launched their latest suite of tools, Microsoft Fabric, bringing together Power BI, Data Marts (SQL Server), Synapse Analytics (SQL data warehouse), and Data Science (machine learning and artificial intelligence) into one data platform. These tools can be run by anyone in the organization without advanced technical knowledge and can help users accomplish whatever data related goals they’re trying to achieve.

Who can benefit most from Fabric?

Any company, in any industry, that is struggling with maintaining numerous spreadsheets can benefit from Fabric. Fabric can help bring the data from disparate systems and sources into one place to help an organization start to understand how to move their processes and logic upstream.

What’s different about Fabric?

Power BI and Fabric use the same type of governance and enablement framework that allows for the creation of certified data sets, promoted data sets and ad hoc data sets, which allows users to consume data from various sources and levels of quality.

When it comes to data, Fabric has two ways of looking at tooling:

• Low code operations back-end: This is a highly scalable, robust GUI driven version of enterprise code bases. It’s easy to interact with (like power query) when analyzing, transforming and storing data in a SQL database or lakehouse SQL data warehouse.
• Professional developers: Data engineering allows users to build, maintain and share analytical models across the organization. Users can also move into the data science tools, creating Power BI reports or machine learning models.