Applying Artificial Intelligence To Detect Anomalies And Make Better Business Decisions

By Author: Digital Alpha
Total Articles: 2
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How does anomaly detection improve business decision-making?
Detecting anomalies in business data and its subsequent analysis can lead to proactive identification and faster resolution of critical issues, and the generation of new business insights.
The volume of information collected by businesses has seen exponential growth and making sense of the data is overwhelming for even the most accomplished of leadership teams without efficient systems. Even the best business intelligence dashboards fall short when explaining complex data structures, correlations, or variances. That is particularly true for business operations, where anomalies could disrupt daily operations or services. In such situations, businesses simply can’t wait for days or long time periods to find a resolution. Automated and constant discovery of anomalies can reveal significant insights leading to opportunities for optimizing the business operations
Today, we live in an era where businesses operate in real-time. Activities happen simultaneously. Also, modern work is collaborative. Colleagues with distinct job roles are responsible for monitoring ...
... business operations across departments. For instance, at the systems infrastructure level, a site reliability engineering team cautiously screens the activity and execution of the system, the servers, and the communication networks. At the business application level, an application support team monitors the website page burden times, the database reaction time, and the client experience. At the business function level, SMEs watch client activity transformations by topography and by client profile, changes per catalyst/event, or whatever KPIs are critical to the business.
Abnormalities in a single function can cause a domino effect and end up influencing different departments. If the measurements are not examined at every level, the correlations will go unobserved. This is the solution that any scalable anomaly detection framework should provide.