Modern organizations rely heavily on business intelligence (BI) tools to consolidate and analyze data.
However, an overdependence on manual analysis of consolidated datasets can obscure valuable insights and prevent timely action.
Here are some of the major pitfalls of traditional BI approaches:
Information Loss :
Consolidating data from multiple sources inevitably leads to a loss of granularity. Nuances get glossed over and vital details can get buried.
Divide and Conquer
Seeing consolidated ratios causes users to focus only on numerators while ignoring informative denominators.
Simpson’s Paradox
Trends in aggregated data may be the opposite of trends in its disaggregated components. Consolidation can therefore produce misleading perspectives.
Manual Bottleneck
Traditional drill-down analysis depends on the manual work of analysts. This not only takes time but can also lead to overlooking key insights in large datasets.
Tunnel Vision
When analysts manually comb through data, they bring their own limited biases and may miss indicators outside their focus.
Flawed Forecasting
Consolidated datasets rely heavily on historical data. However, past performance does not always predict future trends.