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How to measure BI performance? 20 KPIs that matter
Performance BI (business intelligence) – analyzing and optimizing business intelligence tools – is a practice enterprise leaders need to take very seriously. Measuring the means of measurement may sound all a bit meta, but the numbers around BI impact tell a different story.
For example, those organizations using business intelligence systems are likely to reach decisions five times faster than those that do not. More than two-thirds of the global workforce (67%) has access to BI tools. Both of these figures underscore why the global BI market is expanding so quickly: from a value of $29.42 billion last year, its value is forecast to be $63.76 billion by 2032.
But here’s the kicker: 70-80% of corporate BI programs are unsuccessful. They fail to return the desired actionable insights, they don’t get business buy-in, or they deliver underwhelming ROI.
As valuable as business intelligence has the potential to be, it’s all too easy to get BI deployments wrong. Performance BI provides business leaders with a toolbox of key performance indicators (KPIs) from which to measure and optimize business intelligence systems. This helps offset these risks, accelerate data-driven decision making and improve BI’s overall business impact.
20 KPIs for measuring BI performance
Business intelligence success has many dependencies, and the drivers of success vary from one organization to another. However, careful consideration of the following KPI groups will provide an ongoing BI health check and the levers to improve BI performance.
1. User adoption metrics
KPIs monitoring user adoption of business intelligence platforms are vital. Even the most perfectly designed, perfectly delivered BI software will deliver little business value if no-one uses it (or if it's only used by data science professionals).
Three of the biggest business intelligence benefits hinge on user engagement. Firstly, much of BI’s power comes from providing a single, authoritative version of business truth – accepted and accessed by all teams. Secondly, BI systems promote collaboration between different business teams by providing a centralized, data-driven platform that enables shared understanding, informed opinions, and cross-functional insights. In short, BI desilos the strategic decision-making process. Finally, BI tools typically enable each business user a degree of self-service insight discovery. Subject matter experts can use the BI platform to drill into the data independently – accelerating the flow of insights to decision makers.
There is a range of quantitative and qualitative measures business leaders can employ to gauge for BI adoption, usage and user satisfaction. These metrics include:
User engagement: This is your baseline engagement metric. It can be measured by tracking elements such as individual login frequency, average BI session duration, and number of active users, providing a dynamic indication of engagement with your business intelligence tool.
Content utilization: Tracking BI content consumption, like number of views, interactions or shares of specific reports or dashboards, provides important insight into what is and is not deemed interesting or useful by end users. This promotes continual optimization of BI reporting and a catalyst for increasing engagement.
Content creation: Monitoring the nature and frequency of self-service BI content creation and data exploration helps improve overall BI performance in three key ways.
It helps identify citizen data scientists in the non-technical workforce, potentially suitable as super users to support other business users and accelerate the BI embedding process.
It indicates the usability of self-service interfaces as well as potential areas of training need.
It highlights trends in user-generated BI, helping identify areas where new centralized, business-wide reports might be needed.
User support: By tracking support ticket volumes and individual access to training materials, business leaders can identify both areas for improvement in the BI system and also users in need of greater assistance. This enables the platform to become more usable and useful.
User satisfaction: Conducting regular polls, ratings, or qualitative user reviews (and where possible acting on the results) will encourage engagement and optimize overall BI platform performance.
2. Data metrics
Every data source that feeds into the BI data model has the potential to multiply its usefulness – just as long as the raw data ingested is of sufficient quality. Every visual, dashboard and report, the veracity of AI analytics, in fact all the actionable insights and ultimate business value rest upon a solid data foundation. The credibility of BI reporting and, by turn, user engagement, is equally dependent on reliable data. Nothing will undermine a business intelligence initiative faster than BI software returning demonstrably inaccurate results.
That’s why it’s vital to set BI data governance and controls that include regular data audits and the establishment of data quality KPIs, including:
Data accuracy: Thesemeasurescross-referencethe degree to which business intelligence tools’ data matches (or is consistent with) source system data, ‘real-world’ values, or verified external sources.
Data completeness: Missing or duplicate data is almost as unhelpful as inaccurate data. Data completeness metrics might include monitoring the percentage of required data fields that have valid values without gaps.
Data consistency: Aligning outputs from common data fields sourced from different systems is among the most important checks and balances for BI data validity. BI dashboards are about one version of the truth, but if the sales revenue data pulled from an accounting system shows a different total value compared to the sales revenue data imported from a CRM system, insights are compromised. Ideally, such anomalies should be addressed ‘upstream’ before being imported into the centralized BI model, but it’s sensible to conduct regular platform audits.
Data delivery time: The more up-to-date the information in a BI system, the more useful and engaging it is. That’s why it’s important to measure the freshness of the data, by tracking the time lag between data generation and data availability. Some BI solutions include out-of-the-box functionality that monitors and flags any bottlenecks or performance issues (such as the Performance Analyzer in Microsoft’s Power BI).
Data provenance: For users to maintain confidence in the data being presented, particularly when it comes to performance data across departments, it’s important to be able to trace its origins plus any transformations made to it. A data lineage completeness metric measures the percentage of data elements in the BI system for which the full lineage – the origin, transformation history, and movement across different systems – can be accurately traced and documented.
3. Infrastructure metrics
Business intelligence systems enable enterprise leaders to make informed decisions based on BI data analysis and data visualizations. But channeling an organization’s big data into robust, agile, and consistently available platforms places a considerable (and continual) strain on IT infrastructure. If a system is too slow, too glitchy, or too regularly offline it loses credibility and won’t get used, rendering any BI investments wasted. But, like any system, if it’s usable and useful it gets used.
Useful KPIs for ensuring your BI platform infrastructure is withstanding the data processing burden and delivering an optimal user experience include:
System uptime / unplanned downtime: Tracking the time (or percentage of time) a BI system is available and accessible to users.
Data ingestion rate: The data ingestion rate is a useful metric for measuring BI system performance because it indicates the speed and capacity at which the BI platform can ingest and process new data, which is critical for ensuring timely data availability and supporting real-time or near-real-time business analytics.
Data transformation time: The data transformation time indicates the efficiency and responsiveness of an enterprise’s data processing and ETL (Extract, Transform, Load) workflows within the BI platform. This directly impacts the freshness and reliability of the data available for business analysis and decision-making.
Query response time / content generation time: These are crucial performance BI usability metrics, measuring the time it takes for the BI system to execute user queries and return results or to generate and render BI reports and dashboards.
Time to insight: The time to insight metric typically measures the time required for a user (such as a business intelligence analyst) to go from initiating their analysis or reporting request to deriving meaningful insights.
4. Business metrics
The ultimate benchmark for measuring BI performance is whether it has contributed to the achievement of strategic business objectives and whether those benefits outweigh the costs of implementation. The business KPIs selected to measure BI performance could reflect its impact on broad business metrics such as revenue and profit. But they could also measure business intelligence tools’ influence on the performance of specific business functions. Here’s snapshot of functions and some example KPIs:
Customer service / satisfaction metrics: Net Promoter Score, lifetime customer value, or customer retention rates.
Sales: Sales revenue, sales cycle length, or sales funnel conversion rates.
Finance metrics: Days Sales Outstanding, Days Payable Outstanding, or cash discount realization rates.
Supply chain metrics: On Time in Full rate (OTIF), Supply Chain Cycle Time, or inventory turnover rates.
Procurement metrics: Perfect PO rates, procurement ROI rates, or contract compliance rates.
The above is clearly tip of the iceberg territory. But whichever of the many business performance metrics is selected to measure BI performance, it is only a valid assessment if business intelligence insights are actioned. BI can inform decision making and highlight crucial operational trends, but can't implement business change.
For that, business leaders can turn to Process Intelligence. Process Intelligence is the connective tissue of the enterprise. It provides a common language, connecting people to processes, teams to each other and emerging technologies (like GenAI) to the business. Using the Celonis Process Intelligence Graph, organizations can analyze, improve and monitor their processes in near real time—allowing teams to know where value is hiding in your business, and how to capture it, fast.
For more information see: Process Intelligence vs Business Intelligence
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