How data automation elevates your Power BI reports

Have you ever thought of Power BI and Xpert BI as competing technolgies? Despite the somewhat confusing naming, Xpert BI and Power BI are actually complementary solutions that you need for your data platform. Xpert BI helps you build a robust data platform with maximum productivity at minimal risk, while Power BI is a great visualization tool on top of quality assured data. Xpert BI even collects all the metadata from Power BI enabling documentation and governance end-to-end. So anybody looking at a report in Power BI will easily find data descriptions, understand where data is coming from (end-to-end), any business rules applied and who the data owner is. Power BI is simply better with Xpert BI. 

Power BI is one of the global market leaders when it comes to presenting and visualizing data. Power BI is all about providing decision makers with relevant and actionable information when and where they need it. As a visualization tool, it typically competes with technologies such as Looker, Tableau, and Qlik.  
 
In Azure, you would typically complement Power BI with a few other tools. You would use Azure Data Factory for data ingesting into a data lake or data warehouse, and Databricks or other tools for data transformation and modeling data ready for analytics. However, the default approach in Azure has clear limitations and gaps. The two most prominent being lack of data automation and lack of (or weak) data governance. Microsoft relies on 3rd party independent software vendors and partners to fill these gaps. According to James Serra, a Microsoft architect and MVP who we talked to in February 2023, data automation is "too specific" and thus not a focus area for Microsoft. That's where Xpert BI comes in. 

Data automation fixes the historically biggest reason for failure

As a rule of thumb, 80% of your work when building or changing a report in Power BI, is getting the data right. So working smarter should be your main priority. 

Data automation massively boosts productivity of your data engineering team, in our experience by a factor of 4 (!). It works by incorporating experience, methodologies, automation scripts, optimizations, metadata and governance capabilities for building data platforms, into a software. So instead of having to decide on coding standards, how to migrate, how to document, how to optimize for performance, how to read data from complex APIs and data sources, how to unit test etc, this is all available from day one. In stead of having to design and maintain a framework with substantial time and risks as part of your project you start directly on solving business problems. More details about Xpert BI is here.

As a result, a data and analytics project shifts from making technology work, over to understanding and delivering on the expectations of your stakeholders. It mitigates one of the biggest reason for failure of data warehouse and data platform projects, namely treating those projects as technology projects. In addition, the hurdle of starting new data analytics initiatives is significantly lowered, so projects get done and delivered on time.

Some of the benefits of high-level data automation include  

• Guided development along established practices  
• Working on a higher abstraction level, where all automatable tasks in the data lifecycle are automated, letting you focus on delivering business value rather than having to build and maintain an automation framework   
• Future proof architecture, independent of storage and processing technologies, can be swapped later without rewriting code  
• DataOps support such as test automation to manage data quality and inconsistencies in data, and source control integration for better collaboration and risk reduction   
• Ready data source adaptors and advanced API automation  
• Processing optimizations and error detections based on metadata  

Why data governance? 

Simplified, data governance matters since it states and enforces the ownership of data and how data can and cannot be used throughout your organization. It is about transparency and maintaining control over your data assets, where and how they are used, even as the use cases for data and reports in Power BI expand. Data governance is intended to provide guardrails on your pursuit of maximizing ROI from your data and analytics initiatives. 

Some examples of what data governance provide  

• Automatically generated live documentation always reflecting the actual code running in production. 
• A significant reduction in key personnel dependencies, and regulatory and operational risks. 
• The ability to tag data according to ownerships and topics e.g., for compliance reasons.  
• A data catalog for non-technical end users that is always up to date, answering questions such as “What data can help solve my problem?” “Where does this number come from?” “How was it calculated?” and “Who can I ask for additional information?”  
• Full control of the data used throughout all visualizations and other assets inside Power BI. 

How to get started  

Below is a sample setup in Azure with Power BI and Xpert BI working in tandem:  

• Data automation and data catalog: Xpert BI  
• Orchestration and runtime: Azure Data Factory and Azure Batch  
• Storage and processing: Azure SQL database, Azure Synapse DSP/ODP, Azure Data Lake Storage gen2, Snowflake, Databricks, Microsoft Fabric etc.  
• Visualization and storytelling: Power BI
  

Xpert BI adds a lot of flexibility such as being able to swap your storage and processing technologies when needed to optimize price performance, without having to rewrite code. Thus you may start with an Azure SQL database, but later swap to Azure Synapse, Databricks or Microsoft Fabric when you like. Feel free to reas about how we add the missing piece to Microsoft Fabric. 

Conclusion

By harnessing the power of data automation and data governance in conjunction with Power BI, you can unlock the true potential of your data, empower informed decision-making, and drive your organization towards data-driven success.  

Further reading 

• What is the point of data warehouse if Power BI has ETL capabilities?

• What is the difference between Power BI datamarts and a data warehouse data mart?

• Why ETL tools are not enough for agile data warehouse development