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 Improve Data Clarity and Business Outcomes with Anomaly Detection
Improve Data Clarity and Business Outcomes with Anomaly Detection

December 9, 2024

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Anomaly detection in data analytics is defined as the identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well-defined notion of normal behavior. Understanding anomalies in data can help a business by revealing trends, mapping targets and adapting to change with fact-based information that will help the enterprise and prescribe strategies to encourage agility and flexibility in the market and among competitors.

‘Using anomaly alerts and monitoring tools, business team members can quickly establish key performance indicators (KPIs) and personalized alerts and reports to monitor and measure results with powerful, clear, concise results that  help users to understand and manage the variables that impact their targets and their results.’

A data anomaly is revealed when there is a dataset deviation or irregularity – something that is out of the bounds of expected patterns and behaviors. It is hard to overstate the criticality of anomaly detection. Without a comprehensive understanding of data, businesses can make risky decisions, misunderstand data integrity and depend heavily on information that is misleading, flawed or riddled with errors.

To accurately monitor and manage anomalies, the business must select an augmented analytics tool with comprehensive data visualization, data quality and anomaly monitoring tools, and the capacity to share and collaborate on information obtained through these tools.

Select interactive tools that allow a business user to gather information, establish metrics and key performance indicators (KPIs), identify crucial volatility and anomalies, and receive auto-suggestions and information to clearly identify the root cause of problems and target opportunities. These tools should include KPI monitoring, Auto Insights and Key Influencers.

These tools allow business users with average technical skills to:

  • Identify a dataset
  • Define a KPI target
  • Define Influencers Using System Recommendations
  • Define Polarity and Frequency
  • Receive Alerts Via Email and In-Portal Notifications
  • Find the Root Cause of Issues to Solve Problems
  • Identify Opportunities to Improve Performance
  • Detect Anomalies, Increases, Decreases, Volatility and Trends
  • Discover Which Factors Caused Anomalies by Analyzing Key Influencers

Using these simple tools business team members can quickly establish key performance indicators (KPIs) and personalized alerts and reports to monitor and measure results with powerful, clear, concise results that  help users to understand and manage the variables that impact their targets and their results.

‘It has been said that lack of data clarity can be ‘death by a thousand cuts.’ Without a clear picture of anomalies, outliers, and other factors that affect the quality and dependability of data, the enterprise cannot make an educated decision.’

 


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KartikPatel

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