Two Pillars of Microsoft-based BI Solutions
The combination of Microsoft BI tools from the diagram above can be used to create a BI solution, though two names appear in real-life projects more often than others –
Microsoft SQL Server
In 2018 (for the sixth year in a row), Gartner has positioned Microsoft with its SQL Server technology as a leader in operational database management systems.
Microsoft SQL Server is a database management system that –
Can be deployed both on-premises and in the cloud.
Allows storing and querying both traditional and big data. For example, the latest edition – SQL Server 2019 allows deploying a big data environment with advanced analytics and AI capabilities.
Enables both batch and streaming analytics (including real-time analytics).
Goes in bundles with multiple Microsoft products. For example, SQL Server 2016 goes in hand with SQL Server Analysis Services, SQL Server Reporting Services, SQL Server Integration Services, SQL Server Data Quality Services, and SQL Server Master Data Services.
Microsoft Power BI
In 2019 (for 12 consecutive years), Gartner has recognized Microsoft with its Power BI tool as a leader in analytics and business intelligence.
A highly user-friendly self-service analysis and visualization tool, Microsoft Power BI allows –
Retrieving both traditional and big data from a variety of cloud and on-premises data sources (thanks to having over 100 out-of-the-box connectors).
Building reports and dashboards using a drag-and-drop technique (no coding mastery is required).
Providing advanced data analysis capabilities (i.e., natural language queries, what-if analysis, forecasting, and built-in machine learning features).
Building and sharing reports and dashboards by desktop, web, and mobile users.
Setting alerts on KPIs.
Make Your Microsoft BI Toolkit Shine
Here’s the list of implementation stages you’ll have to go through to make your Microsoft BI toolkit shine –
Mapping your business needs to Microsoft technology stack.
Designing a high-level architecture for your data analytics solution.
Elaborating on implementation and user adoption strategies.
Deploying and configuring all the components of the data analytics solution. For example, implementing a data warehouse on SQL Server, OLAP cubes on SQL Server Analysis Services and visualization on Power BI.
Applying SQL Server Integration Services (or another relevant Microsoft BI tool) to create ETL processes and build up complicated data flaws to ensure proper data integration from multiple sources.
Setting up data management practices with SQL Server Data Quality Services.