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Data can be an asset to any organization if converted to information that provides actionable insights. Microsoft Power BI helps organizations transform data sets, transform and cleanse them to form a data model, and then visualize their findings using charts or graphs - creating powerful yet accessible visualizations of it all.
However, as powerful as this platform may seem at first glance, with its power also comes user error and inaccuracy in the presentation of results.
Search the web or YouTube for "Power BI mistakes", and you will discover numerous articles and videos discussing Power BI errors and mishaps.
They cover an assortment of issues - connecting with data sources, shaping it properly for modeling purposes, calculation errors or report design, final publishing, and collaboration among colleagues can all create obstacles when used for one report; further down this journey, it becomes even more apparent with how teams reuse or don't reuse information across accounts as well as financial strategies issues and common mistakes made when rolling out Power BI across an entire organization.
In this blog, we have selected common pitfalls across different areas and levels of detail, starting at the deployment level with three mistakes at the basic report design stage, two modeling issues and two transformation errors discussed herein.
Whatever your interest in Power BI may be. Hopefully, some of these examples will resonate and provoke thought.
What Is Power BI?
At its heart, Power BI allows organizations to extract raw data from multiple cloud-based (SaaS) systems and convert it to actionable information using an intuitive user interface (UI).
Furthermore, this platform isolates what's essential to offer invaluable insight into metrics relevant to specific users.
Mistakes Made At The Deployment Level
The top ten Power BI mistakes are as follows at different levels:
Under-Estimating Effort Required To Build Capability
Power BI capability development is neither simple nor quick. While proof-of-concept projects might work successfully with external help, lasting self-sufficiency across an organization requires strategy, rigor, and discipline from everyone involved.
Consumers tend to consume content, yet viewing, interrogating, and collaborating on reports requires extensive training and expertise.
At its heart lies one of the greatest miscalculations when it comes to content creators: making assumptions that all are created equal.
Content creation covers such an expansive subject that some experts specialize solely in one aspect (for instance, mastery of Data Analysis expression language). Therefore, it would be unrealistic for organizations to send staff on two-day content creation courses and expect them to leave as masters of Power BI.
Lack Of Strategy And Deployment Plan
Every organization that deploys Power BI solutions must give significant thought to how its deployment should occur, or at the very least, should.
From an executive team perspective, there should be a plan in place regarding data ownership - either decentralized to business units or centralized within IT or BI teams - as well as report ownership, which may or may not be decentralized or centralized respectively. Hybrid models exist, such as Managed Service Models, where data ownership resides centrally, but reports remain business-owned.
The strategy will depend on both the organization and industry in which they operate; nonetheless, it must be laid out from the outset.
Unfortunately, this often is not done, leading to confusion and indecision around content ownership decisions - decentralized ownership usually implies less stringent governance with greater flexibility, though often organizations fail to plan properly before deployment, resulting in meandering deployment with little focus or direction, resulting in pockets of good practice which do not become widely adopted.
Duplication Of Data Across Workspaces, Dataflows And Datasets
Organizations without an effective data culture typically suffer from a lack of trust between departments when selecting data sources to use.
As a result, multiple versions of identical information exist across teams and departments often; multiple Excel spreadsheets containing this same data were managed locally within units/departments in Excel format containing duplicate efforts that made reconciling differences more challenging; now data duplicated across team workspaces/dataflows within workspaces as well as datasets used by Power BI deployments can make tracking which version of data latest impossible - who knows which version exists?
Read More: Reasons To Enhance Data Accuracy With Power BI Data Quality Features
Mistakes Made When Creating Power BI Reports
Assuming You Know What The User Wants Or Needs
This concept should be familiar to anyone preparing a presentation, writing a report, designing new processes, or improving existing ones.
Too often, we make assumptions about our audience's, users' or customers' expectations of us; report creators are sometimes too quick to launch Power BI Desktop and start visualization software data - often because time constraints or an eagerness for progress have forced them down this route; once this point has been reached, it becomes difficult for report designers to reverse course.
Content or functionality that would benefit report consumers is often neglected during design; as a result, too much rework may need to be completed post-build to incorporate it.
Furthermore, report creators sometimes put themselves first over meeting end-user needs: while designing an easy option takes two hours, delivering what the client truly requires may take up to six - leaving their customers dissatisfied and leading them to believe Power BI does not meet them fully. This situation often results in only partially meeting requirements, leading to discontentment with Power BI's capability as consumers believe Power BI cannot fulfill its potential capabilities.
Ignoring Accessibility
In September 2019, legislation was implemented mandating that all UK public sector websites meet WCAG 2.1 level AA accessibility standards - this applies even to Power BI reports published online.
One in five British people suffer from long-term illness, impairment, or disability, according to estimates by GOV.UK (Understanding Accessibility Requirements of Public Sector Bodies (www.gov.uk)). As such, all organizations should design Power BI reports so they are accessible. Unfortunately, report designers do not always do this correctly and retrofitting Accessibility into existing reports requires significant rework - even simple issues like too small font sizes can present difficulties when adhering to accessibility standards.
Mistakes Made In The Data Model
Poor Model Structure
One of the greatest strengths of Power BI lies in its ease of deployment: because Power BI Desktop is free, anyone can quickly get up and running.
However, this also poses potential pitfalls: first-time Power BI Desktop users might become mesmerized by pulling data from various sources simultaneously but be unaware that behind the scenes, the data model created by this process induces filter paths which shape what appears on reports as output values, this phenomenon is known as filter context.
An end user might not understand best practices in data model design yet is still allowed to produce reports used for making business decisions.
"Incorrect values" could appear, even though the information was accurate but misinterpreted; more data sources add complexities that make understanding why expected values do not seem difficult and confusing.
No Date Table In The Model
Date tables seem redundant to modern applications that make sense of time and date data; we are more familiar with creating lists in Excel to plot events over time; similarly, with Power BI, creating graphs using dates on an x-axis would still work without issues if some dates are missing from data, as we could also utilize their auto date/time option in DAX on that same data set - making date tables altogether an unneeded requirement.
It is, therefore, no wonder that new users don't understand their necessity.
Problems arise when trying to use two-time series graphs on one Power BI report page derived from different sets of date-stamped data and attempting to filter their time spans simultaneously, similar to a date slicer.
You cannot do it unless they all share identical dates and there's an explicit relationship established in your model between their tables - something which rarely occurs; you need an intermediary link such as date table creation, which ensures seamless data synchronization as this makes navigation options for end users much more limited and avoiding costly Power BI errors.
Mistakes Made In Data Shaping And Transformation (Power Query)
Hard Coding Of Data In Power Query
Report creators may find themselves tempted to manually update Power Query with missing or incorrect data to release their report quickly; however, this should only ever be used as a temporary fix and should never become part of an ongoing data refresh process resulting in inaccurate, obsolete, or inaccurately listed entries, listed variously among sources; overwriting these changes locally creates hardcoded results rather than dynamic changes that respond dynamically over time.
This could cause issues if they change their name or merge with another company in the future, and makes debugging reported problems difficult; they wouldn't know which data had been overwritten, nor where in Power Query it occurred; additionally, people often leave organizations, taking with them any knowledge they built into their data model that can save time when debugging issues later on.
Assuming Excel Matrix Data Models Are Fit For Power Query
Power Query can often help report creators transform matrix data to columnar format more efficiently and with greater accuracy.
Still, each transformation insights step tutorial represents potential errors down the road. Even minor spreadsheet structural modifications - even the removal of an empty line above Total Sales - could cause errors that cause Power Query to take one of two actions when an error arises: either return data as-is or issue an alert message with instructions as to what actions should be taken next.
If your query breaks and Power BI highlights it as such, fixing it might prevent further advancement yet still give visibility to any problems that need fixing.
But suppose the query passes without an error being caught at its source and through to report creation. In that case, it creates potential mistrust among any consumers of its creator and consumers of its reports alike.
Conclusion
Producing powerful Power BI reports requires an intricate blend of technical expertise, design principles and an understanding of your data.
With these Power BI mistakes to avoid, you can increase the quality of your impactful reports while offering insightful data-driven decisions to your audiences and improving user interactive experiences with seamless user journeys.
