Data Quality for Analytics Using SAS

Chapter 9: Process Considerations for Data Quality

9.1 Introduction

9.2 Data Relevancy and the Picture of the Real World

Technical data quality and business data quality

Relevancy

Intent of the data retrieval system

Possible consequences

Reformulation of the business questions

Conversion of real-world facts into data

Conclusion

9.3 Consequences of Poor Data Quality

Introduction

Analysis projects are not started

Analysis results are not trusted

Analysis projects take longer than expected

Wrong decisions can be made

Loss of company or brand image

Regulatory fines or imprisonment

The desired results are not obtained

No statistical significance is reached

Conclusion

Different consequences for reporting and analytics

Required degree of accuracy ...