Now that every company strives to be a data-driven company, data quality is more critical than ever. According to Gartner, the average company loses $8.2B USD annually as a result of poor data quality. Moreover, a recent Experian study found that 4 in 5 (83%) businesses see data as an integral part of forming a business strategy, yet they suspect 30% of their contact and prospect data may be inaccurate. With ‘improving the customer experience’ being called out as a top priority for 2018, the research also reports that 69% believe inaccurate data is undermining their ability to provide this.
Testing data warehouse implementations has become an imperative for instilling trust in the data that the business relies upon. Read this by paper by Data Quality Analyst Wayne Yaddow to learn:
- Why data warehouse projects require specialized testing
- What’s involved in data warehouse testing
- How to build an effective data warehouse testing strategy and test plan
- What issues to look out for as you plan and execute tests