Known Error Database (KEDB)

Known Error Databases document errors, their symptoms, causes, and effective workarounds.

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What Is Known Error Database (KEDB)

A Known Error Database (KEDB) is a central repository storing information about Known Errors. It includes details like the error description, symptoms, root cause (if known), and effective workarounds or temporary solutions.

Why Is Known Error Database (KEDB) Important

A KEDB speeds up incident resolution by providing quick access to existing solutions. It promotes consistent support and helps prioritize which problems need permanent fixes most urgently. It can also reduce repeat incident tickets.

Example Of Known Error Database (KEDB)

An IT support technician receives a call about a printer issue. They search the KEDB, find a matching Known Error entry with a step-by-step workaround, and guide the user through it. This resolves the issue quickly without needing escalation.

How To Create Known Error Database (KEDB)

  • Integrate KEDB functionality within your ITSM tool
  • Define a standard format for documenting Known Errors
  • Train IT staff on how to use and contribute to the KEDB
  • Establish a process for regularly reviewing and updating KEDB entries

Best Practices

  • Make the KEDB easily searchable and accessible to all relevant IT staff
  • Keep KEDB entries up-to-date with the latest information and status
  • Integrate the KEDB tightly with incident and problem management processes

Common Pitfalls To Avoid

  • Having incomplete or poorly written KEDB entries
  • Allowing the database to become outdated with irrelevant information
  • Making the KEDB difficult for support teams to access or search

Further reading:

Latency

Latency is the time delay between an action and the resulting response in a system.

Latency Alerts

Latency Alerts are automated notifications triggered when system response times exceed predefined thresholds.

Learning Algorithms for Root Cause Analysis

Learning algorithms for root cause analysis are AI-powered tools that analyze incident data to identify the underlying causes of problems.