Anomaly Alerts in Data Analytics: Outliers Matter!

Anomaly Monitoring in Analytics: Managing By Exception

An article in CTO Magazine, quoted the Global Anomaly Detection Industry report, stating that the global market for anomaly detection solutions is expected to reach $8.6 billion by 2026, with a compound annual growth rate of 15.8%.

By its definition, the term ‘anomaly’ means: Deviation or departure from the normal or common order, form, or rule or one that is peculiar, irregular, abnormal, or difficult to classify. So, if anomaly detection is meant to find those few instances where something deviates from the norm, why would it be so important for a business to find those deviations? Wouldn’t a business want to focus on the larger patterns and trends, and let the outliers and exceptions tend to themselves?

‘Self-serve augmented analytics enables the user and the organization to provide anomaly alerts including context-aware triggers, and route these alerts to the right team member, stakeholder or management role. Using machine learning, advanced analytics solutions can accommodate specific thresholds and address false positives.’

Data analytics and augmented analytics that include anomaly monitoring provide crucial alerts and notification of outliers and anomalies that can significantly affect success and results, identifying issues and opportunities that deviate significantly from the established behavior or patterns and allowing management and team members to respond in a timely fashion, before the issue becomes a real problem, or before a critical opportunity is missed.

Managing by exception allows the team to spot a shift in customer behavior, an aberrant trend, an operational performance issue or a system glitch. Anomaly alerts provide proven improvement in decision-making and allow team members to call out and address unexpected changes in trends and patterns, and address issues swiftly.

Anomaly Monitoring Alerts the Enterprise to Deviations that Matter!

Self-serve augmented analytics enables the user and the organization to provide anomaly alerts including context-aware triggers, and route these alerts to the right team member, stakeholder or management role. Using machine learning, advanced analytics solutions can accommodate specific thresholds and address false positives.

When you dig deeper into results and identify outliers, anomalies and deviations, you can:

  • Solve problems by finding the root cause of those problems
  • Identify opportunities to improve performance for the target
  • Detect anomalies, increases, decreases, volatility and trends for target KPIs to discover the reasons for these issues, trends or patterns
  • Find out which factors caused the anomaly by analyzing the key influencing factors.
  • Clearly identify relationships and impact of influencers on targets
  • Let the system do the work with auto-recommendations, guidance and alerts on changing impact of influencers on targets, related anomalies and volatility
  • Receive and monitor results via email and in-portal notifications
  • Identify opportunities for improvement to optimize results

By its definition, the term ‘anomaly’ means: Deviation or departure from the normal or common order, form, or rule or one that is peculiar, irregular, abnormal, or difficult to classify. So, if anomaly detection is meant to find those few instances where something deviates from the norm, why would it be so important for a business to find those deviations?’

Contact Us to discuss the unique needs of your organization, and your users, and find out more about why and how Anomaly Monitoring and Management can help your enterprise, and about our Anomaly Alerts features, our Augmented Analytics Solution and services. Enable Citizen Data Scientists, and leverage Smarten Technology. Explore our free webinar on SnapShot Monitoring Alerts.

Original Post : Small and Medium Businesses (SMBs) Using Tally Prime CAN Enjoy Analytics!

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