Beyond Consolidated Data: Why You Need AI-Powered Business Intelligence
<p>Modern organizations rely heavily on business intelligence (BI) tools to consolidate and analyze data.</p>
<p>However, an overdependence on manual analysis of consolidated datasets can obscure valuable insights and prevent timely action.</p>
<p>Here are some of the major pitfalls of traditional BI approaches:</p>
<h2>Information Loss :</h2>
<p>Consolidating data from multiple sources inevitably leads to a loss of granularity. Nuances get glossed over and vital details can get buried.</p>
<h2>Divide and Conquer</h2>
<p>Seeing consolidated ratios causes users to focus only on numerators while ignoring informative denominators.</p>
<h2>Simpson’s Paradox</h2>
<p>Trends in aggregated data may be the opposite of trends in its disaggregated components. Consolidation can therefore produce misleading perspectives.</p>
<h2>Manual Bottleneck</h2>
<p>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.</p>
<h2>Tunnel Vision</h2>
<p>When analysts manually comb through data, they bring their own limited biases and may miss indicators outside their focus.</p>
<h2>Flawed Forecasting</h2>
<p>Consolidated datasets rely heavily on historical data. However, past performance does not always predict future trends.</p>
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