IDC PlanScape : 데이터 품질 관리

This IDC PlanScape discusses the importance of data quality management. The dimensions of data quality may seem simple, but data quality issues are complex. "Dirty data is often a symptom of deeper people, process, and technology issues that impact the quality of the data, but getting to the root cause can be difficult and time-consuming and may shed light on less-than-optimal processes and practices," says Stewart Bond, research vice president, Data Intelligence and Integration Software research at IDC. "Cleaning up data within operational and analytical repositories without first understanding where the bad data is entering the system is the equivalent of bailing a boat without knowing where the holes are. Figure out where the holes are and where the bad data is coming from and why, then correct the issue and plug the hole so that you don't run out of energy continuously cleansing the data."

IDC PlanScape Figure

Executive Summary

Why Is Data Quality Management Important?

What Is Data Quality Management?

Who Are the Key Stakeholders?

How Can My Organization Take Advantage of Data Quality Management?

• Defining a Data Quality Strategy
• Data Quality Assessments
• Root Cause Analysis
• Resolution
• Clean and Correct
• Measure and Monitor
• DataOps

Advice for Technology Buyers

Related Research