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Take Care of Your Power BI Environment

Power BI’s self-service capabilities can lead to uncontrolled asset creation, duplicated efforts, and inconsistent information, which undermine the reliability of your decision-making. Understanding these objects’ usage, value, and life stage allows teams to gain control of their environment and eliminate unnecessary clutter. Thus reducing costs and space so you can leverage valuable insights properly.

Minimize Risk, Maximize Results

Applying DevOps best practices in Power BI addresses common challenges
like inconsistent workflows and deployment risks. By implementing zero-touch version control and agile deployments, teams create a faster and repeatable release process that supports their organizational goals. This approach eliminates manual tasks and integrates quality assurance into every stage. By implementing guardrails, teams maintain a steady flow of insights without disruptions.

Centralize Your Tools, Amplify Your Results

Unite Power BI with your other analytics tools into one easy-to-use portal. Effortlessly search, save favorites, and get personalized recommendations based on your activity. Through simplified navigation obtain smoother workflows, greater collaboration, and save time by avoiding duplicative work. This approach helps you make quicker, more informed decisions without the hassle of managing multiple analytics tools separately.

Detect and Correct

Integrated impact analysis and consistency management in Power BI provide critical insights into how changes, such as data source modifications, affect objects like reports, dataflows, and semantic models. By tracing the lineage of artifacts, users can identify components reliant on altered parts of the data source and navigate directly to affected workspaces, facilitating the quick investigation of impacted users, including citizen developers and app users. Additionally, consistency in DAX formulas and naming conventions is vital for clarity and accuracy; variations in naming patterns can lead to misinterpretation. Detecting and addressing these inconsistencies promotes uniformity across reports, providing teams with precise, organized models while minimizing errors. These strategies allow businesses to improve their analytics operations, assuring accurate insights and a coherent reporting environment.
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As the BI space evolves, organizations must take into account the bottom line of amassing analytics assets.
The more assets you have, the greater the cost to your business. There are the hard costs of keeping redundant assets, i.e., cloud or server capacity. Accumulating multiple versions of the same visualization not only takes up space, but BI vendors are moving to capacity pricing. Companies now pay more if you have more dashboards, apps, and reports. Earlier, we spoke about dependencies. Keeping redundant assets increases the number of dependencies and therefore the complexity. This comes with a price tag.
The implications of asset failures differ, and the business’s repercussions can be minimal or drastic.
Different industries have distinct regulatory requirements to meet. The impact may be minimal if a report for an end-of-year close has a mislabeled column that the sales or marketing department uses, On the other hand, if a healthcare or financial report does not meet the needs of a HIPPA or SOX compliance report, the company and its C-level suite may face severe penalties and reputational damage. Another example is a report that is shared externally. During an update of the report specs, the low-level security was incorrectly applied, which caused people to have access to personal information.
The complexity of assets influences their likelihood of encountering issues.
The last thing a business wants is for a report or app to fail at a crucial moment. If you know the report is complex and has a lot of dependencies, then the probability of failure caused by IT changes is high. That means a change request should be taken into account. Dependency graphs become important. If it is a straightforward sales report that tells notes by salesperson by account, any changes made do not have the same impact on the report, even if it fails. BI operations should treat these reports differently during change.
Not all reports and dashboards fail the same; some reports may lag, definitions might change, or data accuracy and relevance could wane. Understanding these variations aids in better risk anticipation.

Marketing uses several reports for its campaigns – standard analytic assets often delivered through marketing tools. Finance has very complex reports converted from Excel to BI tools while incorporating different consolidation rules. The marketing reports have a different failure mode than the financial reports. They, therefore, need to be managed differently.

It’s time for the company’s monthly business review. The marketing department proceeds to report on leads acquired per salesperson. Unfortunately, half the team has left the organization, and the data fails to load accurately. While this is an inconvenience for the marketing group, it isn’t detrimental to the business. However, a failure in financial reporting for a human resource consulting firm with 1000s contractors that contains critical and complex calculations about sickness, fees, hours, etc, has major implications and needs to be managed differently.

Acknowledging that assets transition through distinct phases allows for effective management decisions at each stage. As new visualizations are released, the information leads to broad use and adoption.
Think back to the start of the pandemic. COVID dashboards were quickly put together and released to the business, showing pertinent information: how the virus spreads, demographics affected the business and risks, etc. At the time, it was relevant and served its purpose. As we moved past the pandemic, COVID-specific information became obsolete, and reporting is integrated into regular HR reporting.
Reports and dashboards are crafted to deliver valuable insights for stakeholders. Over time, though, the worth of assets changes.
When a company opens its first store in a certain area, there are many elements it needs to understand – other stores in the area, traffic patterns, pricing of products, what products to sell, etc. Once the store is operational for some time, specifics are not as important, and it can adopt the standard reporting. The tailor-made analytic assets become irrelevant and no longer add value to the store manager.