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&rsquo;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> <p><a href="https://medium.com/mlearning-ai/beyond-consolidated-data-why-you-need-ai-powered-business-intelligence-4154860698e"><strong>Click Here</strong></a></p>