Establishing 'Making it Happen' as a 'Formal & Predictable' Discipline

	Data Quality Assurance and monitoring

Execution-MiH ENCYCLOPEDIA → Enterprise Intelligence → SECTION - Data Quality →

CHAPTER -

Data Quality Overview

Before one gets onto working on data quality, one has to appreciate on what is data quality?, why is it so important? and what are the reasons for data quality failures?

Topics

Data Quality Definition- What is Data Quality?

Data quality is not linear and has many dimensions like Accuracy, Completeness, Consistency, Timeliness and Auditability. Having data quality on one dimension is as good as 'no quality'.

Data Quality Tolerance and Business-Case

Data quality should not be perfect, but should meet the expectation of the user. Data quality pursuit has to pass the test of business case. Data Tolerances, leniency on non-financial data and historical data are some examples of staying with imperfect but acceptable data quality.

Root Cause of Data Quality Issues

The reasons for bad data quality include fast changing business dynamics, systems management gaps, un-controlled application proliferation and people issues. Before one looks at how to improve data, its prudent to understand the root cause, or the events, which result in data quality deterioration or brings out the existing data quality issues.

Impact of Bad Data Quality

Data Quality impacts range from a pure transaction level loss up to catastrophic impact for an enterprise.