The healthcare industry relies on data to uncover inefficiencies, improve patient outcomes and maximize financial performance. And many healthcare organizations still use traditional business intelligence (BI) tools like PowerBI and Tableau to make sense of their data. Unfortunately, these legacy tools often create more inefficiencies than they solve.
While these tools are widely used in other industries, their application in healthcare presents challenges that drain time, resources, productivity and divide strategy among business units.
Here’s why relying on PowerBI and Tableau for healthcare analytics can be a costly mistake:
#1. Dependence on IT for Report Creation
One of the biggest roadblocks with PowerBI and Tableau in healthcare is the reliance on IT teams and data analysts to generate reports. Clinical and operational leaders need in-the-moment insights to be effective; however, they often wait days or weeks for IT to develop custom reports or dashboards. This dependency slows decision-making and overwhelms IT teams with constant update requests.
#2. Know What to Analyze Before Reports are Created
Traditional BI tools require users to know exactly what they want to analyze before reports are built. Think limited flexibility and rigid reports that only scratch the surface. If new questions arise after a report is generated, users must go back to IT or a data analyst to request additional data, causing delays and limiting the ability to act on insights in a timely manner.
#3. Limited Data Exploration and Drill-down Capabilities
Once a report is created with traditional BI tools, users are often locked into predefined views and dashboards. If a healthcare executive, care manager or clinician wants to explore beyond surface-level insights, they must go back to IT to request additional data slices or new visualizations. This rigid structure prevents organizations from being truly data-driven, as they lack the ability to pivot quickly when new questions arise.
#4. Fragmented and Incomplete Data Insights
PowerBI and Tableau rely heavily on structured data sources, but healthcare organizations generate vast amounts of unstructured data from EHRs, claims systems and operational workflows. These BI tools often fail to integrate disparate data sources seamlessly, resulting in incomplete insights. Without a holistic view of financial, operational and clinical data, organizations risk making decisions based on partial or outdated information.
#5. Time and Cost Inefficiencies
Building and maintaining dashboards in PowerBI and Tableau requires significant time and expertise. IT teams must continuously update data models, manage system integrations and optimize performance. Licensing costs for these tools—along with the hidden costs of IT labor and training—can quickly add up, making them an expensive and inefficient choice for organizations looking to scale their analytics efforts.
#6. Low Adoption Rates Among End Users
Healthcare providers, operational leaders and executives are not data scientists. Despite training efforts, many stakeholders find PowerBI and Tableau unintuitive or cumbersome, leading to low adoption rates. When users struggle to extract meaningful insights on their own, analytics efforts become bottlenecked within a small group of technical users rather than empowering the broader organization.
#7. Democratize Data Across the Organization
While traditional BI tools allow reports to be shared, they often lack relevance for specific departments or roles. A finance team may require different insights than a clinical operations team, yet both rely on the same foundational data. With more advanced analytics capabilities, organizations can empower users to modify reports based on their specific needs while maintaining data integrity. This approach fosters greater collaboration and ensures each department can act on insights that matter most to them.
Rather than rely on legacy BI tools that introduce inefficiencies, healthcare organizations need an analytics solution built for their needs. A flexible, modern platform can provide in-the-moment, self-service analytics that empower decision-makers without IT dependency.
The ideal solution should:
- Offer intuitive, self-service data exploration
- Provide in-the-moment analytical analysis rather than static reports
- Integrate seamlessly with healthcare data sources, both structured and unstructured
- Be designed for use across the entire organization, not just by data scientists
