ANOMALY DETECTION

CHALLENGE

SOLUTION

  • ETL Job Status Doesn’t Tell the Story

    Just because ETL status is green doesn’t means data is correct

  • Monitor The Data Not ETL

    Analyze data after each ETL run and make sure data is consistent

  • Data Load Successful but There is No Data or Wrong Data

    ETL status may sometime be misleading

  • Get Notified When Data Is Not Consistent

    Analyze each data set after each ETL run and detect anomaly early

  • Users Screaming Because Nobody told Them That Data Is Doubled

    When users are the ones that find data issues ahead of Data team, that becomes not good scenario

  • Pro-active Communication If Data Is Wrong

    Let the tool identify issue so that you could pro-actively notify users before they find it.

  • Data Is Stale for Long Time

    Nobody knows how fresh the data is

  • Freshness is tracked for each objects

    Tool tracks how fresh data is and when did the data change last

  • Outliers Not Tracked nor Addressed

    No one tracks the outliers and hence those are not addressed, skewing the analysis

  • Outliers Tracked & Addressed on Daily Basis

    AI and ML module Tracks Outliers on Daily Basis and Alerts As necessary

  • As CDO, I have no Idea How Good Is Data Quality

    No options to track and improve data quality across the systems on an on-going basis.

  • Solution Tracks and Logs Data Issues Across System

    Track and Log all issues in once place so that data team can analyze historical occurrences and take measures to avoid the issues.

ANOMALY DETECTION

ANOMALY DETECTION

Instill confidence in data and your team and Guarantee successful data deliveries.

4DAlert monitors data in your target platform and proactively compares metrics, identifies issues, missing values, or column level quality issues in target data set from specific data sources, and alerts data anomalies before end users find them or its downstream systems and consumers. 

SCHEDULE DEMO

AI and ML to learn patterns and adjust alert criteria for Anomaly detection

4DAlert LEVERAGES AI AND ML TO LEARN FROM PAST DATA POINTS AND ALERTS WHEN FIND ANOMALIES

AI and ML-based algorithms learn from patterns, detect anomalies, and alert data owners when anomalies are detected within data sets, a process that helps build end-user confidence in data in your warehouse and saves time for end-users to reconcile data and take a wrong decision on bad data.

CERTIFY DATA IN BUSINESS INTELLIGENCE SYSTEMS

NOTIFY USERS PRO-ACTIVELY WHEN DATA IS WRONG

TAKE PRO-ACTIVE ACTION TO CORRECT DATA BEFORE END USERS TELL YOU TO DO SO

SAVE END USERS AND SUPPORT TEAM'S TIME BY RECONCILING DATA ALL THE TIME

Certify data in business intelligence systems

What Good is an Analytics Platform If Users Can’t Trust the Data?

Business users use insights from analytics platforms to take key decisions that are in many ways consequential for an organization’s goal and overall performance. Therefore, it is very important that data is accurate and reconciled and has passed basic checks. Automated reconciliation algorithms embedded into the 4DAlert solution help to validate and confirm that data pass basic checks regularly – a process that builds trust and confidence in data in the analytics platform.

Anamoly Detection
Anamoly Detection

Notify users early when data is wrong

Just being reactive is not enough, needs to be proactive.

4DAlert helps the analytics team to tackle data issues more proactively than reactively. The solution scans data sets in source systems, compares that with data in analytics platforms, and detects an anomaly. As and when anomalies are found, the solution alerts key stakeholders responsible for the data set and helps them tackle the issue before users identify the issue. The ability to notify users and take corrective action before end-users tell you to do so helps you increase trust in your analytics platform.

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