Case Studies

Global Capital Markets Bank

  • 23% of all non-IT staff cannot perform their primary job function – $120m data quality damage per annum

Energy Retailer

  • 13% of all non-IT staff cannot perform their primary job function due to poor quality data – $4.2m data quality damage per annum. Data Quality Triage engine detects $24m in DQ uplift, with $2m to invest to recover that damage within 12 months.

Dot Com Insurance Provider

  • 24% of all non-IT staff cannot perform their primary job function due to poor quality data – $3.4m damage per annum.

Other estimates:

  • National Grocery Retailer – $46m data quality damage per annum
  • National Logistics – $36m data quality damage per annum
  • Global Oil/Energy Company (Upstream and Downstream)  $390m data quality damage per annum

What about your organisation ? You are only 4-6 weeks away from discovering the precise commercial damage caused by poor quality data, and a clear roadmap to recover that damage.

What customers say about ClearDQ…

“We used the ClearDQ approach over a 4 week period and very quickly saw impressive insights. We got a clear view of the commercial damage poor quality data was having on various parts of our business, down to level of detail to understand which data domains, applications and business functions were most impacted.  This insight has allowed us to fund and prioritise improvements to our data landscape. ”

Yuval Marom, Head of Analytics and Data Services, iSelect

“ClearDQ gave us a clear view of data architectural remedial work, and the sequence of that work, so we know our company can address data improvements tactically and strategically. Furthermore we now know commercial benefit uplift we can expect from data improvements. We have not seen a rapid Data Quality Funding tool quite like this before.”

Kasper Mortensen, Data Warehouse & Business Intelligence Architect

 

Data Quality for Financial Performance