Executive Boards are increasingly being asked to approve more sophisticated and larger investments in data-centric innovation projects and digital data assets.
Similarly, Board Members need to listen carefully to “keyword” phrases when reviewin digital data investments and initiatives such as: Master Data Management, Reference Data Management, Data Analytics, Big Data, Business Glossary, Data Visualization, Data Reporting and Metadata Management
These initiatives are all subject to the high risk of failure due to poor quality data. This is globally well known and well understood, but often glossed over by IT departments and business cases.
Unfortunately what Board members are NOT being told is the extent of the incumbent damage caused by existing system/people/process failures that result in approximately 20% of the workforce being unable to perform their primary job function due to poor quality data.
IT leaders globally, in general, have no idea how to measure this damage, identify the root causes or remediate the commercial damage.
Worse still, IT leaders are rarely incentivised or motivated to even measure or pursue the data cleanup effort, as it often exposes the immaturity of the IT leadership.
Board members need visibility and unfettered access to the metrics and facts surrounding the damage of poor quality data, and an understanding of how data is an “engineered-product” that has a specific lifecycle to capture and unlock it’s business value.
It is most likely that a business leader, such as a COO or CFO will drive the ClearDQ engagement to hold the CIO to account, relative to the IT spend velocity and IT project priority.
CIO’s will typically posture against ClearDQ as it swings the “power and control” over digital information squarely away from IT and deep into the business.
If you are a Board member of a commercial (listed or non-listed) enterprise, a not-for-profit or government agency, we’d encourage you to download our briefing paper and arrange a confidential discussion with us to being the journey of repairing the damage to your organisation due to poor quality data.