About Data Quality

Modern Business

Conducting business in a data driven environment has become the norm and it is creating an imperative for data quality. As a result, ‘data quality’ is becoming a forcing factor for improving data management practices, data organisation and developing better data systems where quality, coherence and consistency of the data and the resulting information become the norm and in some cases mandatory for compliance.

There are typically four (4) classes of data defects or inadequacies:

  • null


    • Data ill-conditioned relative to the business context
    • No data error per say, but unfit for use in intended business context and tends to be ignored, rejected, discounted by users;
    • Turns rapidly into a lost opportunity;
    • Requires consensus to condition the data
  • null


    • Results from equipment failure and/or human error;
    • Unavoidable and unpredictable;
    • Actionnable and preventable to some extent when error detection strategies are implemented;
    • Independent of business context;
  • null


    • Results from inappropriate standards or methods introducing non-compliant or non-conforming data representation(s);
    • Non Recurrent once fixed until such time new standards and methods are required;
    • Actionable, preventable and can be eliminated.
  • null


    • Inherent to the measuring system;
    • Unavoidable but manageable;
    • Independent of business context.

© 2017 Pimsoft. All rights reserved. Sigmafine® is a registered trademark of Pimsoft Inc.

Copyright | Terms of Use | Privacy Policy | Sitemap

Log in with your credentials


Forgot your details?

Create Account