The Primary Principles of Business-Incident Detection for BI -

The Primary Principles of Business-Incident Detection for BI –

The Primary Principles of Business-Incident Detection for BI –

The emergence of business intelligence was supposed to finally enable businesses to truly utilize the massive amounts of data running through their systems. When the field initially began, offering visualizations or basic parameter settings to detect business incidents, the overwhelming feeling was optimistic that from these beginnings -- however humble -- the BI sector could begin to unlock new truths hiding right under the nose of the average analyst.

However, the reality (and now the status quo) is that the initial offering has been the only offering. Massive leaps forward based on innovative concepts have failed to materialize and progress in other sectors quickly lapped what the leading BI players could produce.

As BI accommodates increasingly large volumes of data, the technologies that facilitate the collection, analysis, visualization, and reporting of business data must become more advanced. From simple analytics to advanced machine learning and anomaly detection, it is imperative for thought leaders and decision makers to understand how BI leverages the exploration of data to discover potential anomalies and strategic business opportunities.

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There are many industries (such as e-commerce, fintech, and adtech) that find their success in the assessment of their collected data. The ability to apply predictive analytics relies on analyzing immense amounts of data from a variety of origins to learn the normal behavior of the data. This builds patterns to help understand and produce predictive analytics, leading to anomaly discovery.

What sets apart businesses providing BI solutions is timing and accuracy. The collection and analysis of data results and analytics visualizations can be output at varying frequencies, such as every minute, once an hour, or daily.

When an e-commerce business sees conspicuous buying behavior, it needs to understand the cause. Amazon noticed an unusual decrease in sales in July of 2016 during Amazon Prime Day -- second in revenue generation only to Cyber Monday. The company addressed the situation on social media, stating that the "add to cart" feature had a technical glitch, preventing customers from finalizing purchases.

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Amazon needed to immediately locate the root cause and notify technicians about the detected anomaly. Delivering results quickly can be the deciding factor in making millions or losing millions. Today's current expectation is that the speed of assessment should match the quality of results.

The primary challenge for any automated business intelligence service is to scale the number of KPI alerts and identify the significant ones.

 



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