What are the limitations of BI dashboards?

Traditional dashboards don’t provide insight fast enough in today’s data-driven world. Although they convey snapshots of important measures, insights from BI dashboards are not contextualized, easily consumable or actionable by the majority of business users. Traditional dashboards are great at providing business leaders with insights into what’s happened in the past, but what if they need actionable information in real time? What if they want to use their data to estimate what may happen in the future?.

The growing volume and variety of data and the soaring speed of business means it is challenging for companies to ensure that the right data is presented to the right people at the right time - i.e. contextually. In today’s environment, traditional BI platforms and self-serve BI are becoming irrelevant for many day-to-day decisions because it simply takes too long to get the insights and then there is the “decision gap” between getting insight and making a decision. While companies around the world overwhelmingly recognize that data-driven decision-making outperforms intuition, most are still not realizing the full value of their data with traditional BI tools.

For today’s data-driven companies, there are thousands of metrics that can be gathered and collected to gain insights into business performance. As relevant data points grow exponentially, it becomes challenging to use traditional dashboards to track these metrics and make informed timely decisions. Here are some of the most significant limitations of conventional BI dashboards:

  • Over-reliance on historical data
  • Lack of real-time anomaly detections prevents proactive incident management
  • Missing small incidents that have a negative impact
  • Providing context to make it clear how KPIs and other data are relevant to end users
  • Difficulty in creating drill-down paths to plumb the data underlying surface metrics
  • Cluttered dashboards, misrepresenting data in dashboards or leaving out relevant data, leading to inaccurate analytics.
  • Lack of intelligent prioritization
  • Time consuming and the cost of creating, implementing and maintaining dashboards