Understanding why Microsoft Power BI is a leader in analytics technology platforms and what steps you should take to make it work for your organization that will allow you to harness the power of your data.

Power BI is trending among the latest and greatest analytics technology platforms in the industry. What separates it from other tools? How can you best harness the power of its advanced analytics? How can you ensure user adoption during your own implementation? Where do you start? What should you avoid? No matter the business use case there are a handful of ways to establish a new platform successfully.

When making an investment in any new analytics technology, there are a handful of critical steps that an organization should plan for before, during, and after an implementation. Power BI is no exception—but the analytic insights, data-driven decisions, and automation available as a result will provide returns in tenfold.

Why Choose Power BI?

Power BI is positioned as a leader in the Analytics & Business Intelligence Platforms in the 2021 Gartner Magic Quadrant. Microsoft shows the most complete vision and ability to execute among other market leaders. In fact, 97% of Fortune 500 companies use Power BI today. Here are some benefits our clients have gained from Power BI:

  • Flexible System Integration: Power BI is built on top of Microsoft’s Azure cloud—one of the largest, fastest growing, and most reliable cloud systems on the market. You may already be aware of the benefits of pairing Power BI with Excel and Microsoft Teams, but the flexibility goes much further. Power BI reporting can be built on top of most modern cloud data warehousing systems (not just Azure), on premise databases, and hundreds of other connector options. Organizations can utilize Power BI REST APIs to support enterprise deployments at scale. Pairing Power BI with the Microsoft Power Platform, including PowerAutomate can help turn your reporting system into a fully integrated system for both insights and action.
  • Modern Visualization & AI: The Power BI product team’s mission is to make data analytics accessible for everyone—providing a low code to no code experience for every aspect of data analysis. Power BI implements new analytics and visualization features every month—in addition to a growing marketplace of third-party visualizations. Combining best-in-class data visualization with automatic AI insight generation powered by Azure, customers unlock features including natural language generation, sentiment scoring, image tagging, automatic aggregations, and more.
  • Economic Subscription Options: The barriers for an organization to integrate analytics into their business processes has never been lower. The Power BI Desktop application is free for anybody to download and get started. Monthly Pro subscriptions to share insights across an organization start as low as $10. Premium subscriptions to unlock all advanced AI and big data prep features start as low as $20. All while providing to scale business intelligence systems with massive amounts of data.
  • Self-Service Analytics: Power BI makes it quicker and easier to provide decision makers with the data they need, when they need it, in its most insightful form. Power BI has shifted to paradigm for data cultures. Enterprise BI is available not just for IT workers, data scientists, business analysts, and non-technical users alike. Power BI allows more control over how users access and consume data—in an instantly familiar Office format—sometimes called “PowerPoint for Data”.

Where Do We Start with a Power BI Migration?

Selling customers on the benefits of Power BI is easy—but every customer is at a different stage of data and analytics maturity. It can be difficult to understand the path ahead. For any such level of maturity, there are three things that can help ensure a successful implementation:

1.) Establish Modern Architecture

Walk before you run—your advanced analytics will only be as good as your data foundation. As the old saying goes… “Garbage in = Garbage Out”. Take a critical view of your existing architecture to maximize the capabilities of Power BI. In general, look to make sure your architecture is:

  • Cloud-based: While Power BI can be built on top of on-premise systems, a cloud-based data warehouse will be more performant, cost-effective, secure, and scalable than any on-premise system. Power BI serves as a layer of analytics on top of this foundation—make sure it’s reliable.
  • Standardized: Think about how many places an analyst can access the same data. Does each business unit have a central source of truth? Create a standard three-tier data architecture that utilizes master data elements.
  • Automated: Reduce any overhead from ETL into your data warehouse. Spend less time maintaining your data to allow more time for prototyping your advanced analytics capabilities.

2.) Roadmap the Migration

Don’t lift a finger until the path ahead is clearly defined—it can be difficult to reverse course when bad habits and processes are created. Understand these crucial items:

  • Subscriptions: Determine the requirements of your architecture based on your expected user base and the resulting costs. Consider Power BI Pro, Power BI Premium, or AAS architectures. Start small with a plan to scale. Your data volume, data velocity, and data volatility should all be considered.
  • Role Definitions: With a subscription selected, the data integration, modeling, and reporting tasks ahead become clear. Explicitly define your team’s responsibilities across data engineers, data modelers, front-end engineers, and analysts. Ensure that each role is staffed and defined appropriately to complete your migration within the project timeline. Create role-based access governance up front.
  • Key Priorities: Identify the highest value reports in your organization—the ones that make-or-break decisions are made from. Start your Power BI migration journey here. Improve the way those decisions are made and create an appetite from decision makers for a full migration. Adoption will come from the top down—include your decision makers in regularly scheduled user acceptance testing sessions along the way.

3.) Education and Training: 

You are building reporting systems that will support pivotal decisions both today and years from now. Developers and end-users of the tools must participate in an on-going dialogue to ensure buy-in and understanding.

  • Developers: Power BI developers should understand the fundamentals of data modeling best practices (see Kimball Dimensional Modeling Techniques), Data Analysis Expressions language (DAX), version control processes for continuous integration and continuous delivery, and more. Investments in your internal technical talent help drive a data culture across the organization.
  • Data Analysts: Help data analysts to navigate the Power BI landscape both as end-users and creators. Share standards for distributing reporting and analysis, including how to best utilize self-service features like dataflows, certified datasets, and more.
  • Business Users: If applications are too difficult to navigate or understand, you have missed the mark. Power BI should aid, not complicate, data-driven decision making. Where and how to access high-priority data must become second nature.
  • Everyone: The best way to increase adoption is by sharing the success stories that result from using Power BI. This momentum can quickly increase buy-in for the migration encouraging new users to rely on your applications.

What to Avoid in a Power BI Migration?

Any data analytics project will come with some unexpected errors, mistakes, wrong turns—and that’s ok. The quality of your data culture is not always a linear path. With that in mind, here are three pitfalls that should be treated with extra care during your migration:

1.) Boiling the Ocean:

Tackle your reporting needs in digestible pieces. Focus on key reports first—don’t migrate everything in one giant leap. Regroup after your pilot migration to understand what went well and what could be improved. Lock down the basics before looking to implement advanced ML or AI features. Modernizing your entire BI infrastructure does not happen overnight—digital transformation is always an ongoing project.

2.) Self-Service Overload:

Establish and maintain trust in your analytics platform. Power BI offers loads of self-service features that can be incredibly useful, but can be a double-edged sword. Problems can arise when several reports covering the same business area conflict. Effort is easily duplicated without proper governance and planning. Tip: make sure to track user adoption to see which reports are used mostmake them betterand remove the rest.

3.) Carbon Copies:

Migrating to a new analytics platform is an opportunity to take a fresh look at your reporting. It can be tempting to rebuild Power BI copies of legacy reports exactly as they are because ‘that’s the way we have always done it’—or some other flavor of resistance to change. Take advantage of the new powerful tool in your hands. The goal is to ultimately change the behavior of your business—not just where they click for data. Manage change, but always work to move your data culture forward.

 

David Walborn David is a Staff Consultant based out of our Chicago office. He leads high-impact analytics projects for enterprises providing deep expertise implementing cloud-based technologies including Microsoft Azure, Power BI, and more. When he’s not developing data solutions, David closely follows his hometown sports teams– patiently watching and waiting for his Lions to bring their first Super Bowl to Detroit… maybe next year.
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