For the Data Architect

Data Architects are increasingly being asked to design, architect and de-risk more sophisticated and larger investments in data-centric innovation projects and digital data assets.

More than any other group, Data Architects are well aware of the value of data components such as:

  • Master Data Management (MDM)
  • Reference Data Management (RDM)
  • Data Analytics & Big Data
  • Data Governance
  • Data Quality Frameworks
  • Business Glossary & Metadata Management
  • Data Models, Data Business Rules
  • Data Visualization & Data Reporting
  • Data Warehouse & Data Marts
  • Data Integration

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, often to the frustration of the Data Architect.

Unfortunately what Data Architect 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 business workforce being unable to perform their primary job function due to poor quality data.

Data Architects often attempt to proactively get involved in shaping or leading Data Governance and Data Quality frameworks to solve for the Data Quality problem. This is normally expected of this role.

However, to date, Data Architects have had no clear approach to measure this damage (factual metrics at scale, NOT anecdotal measurements), identify the root causes or remediate the commercial damage, which ironically is a funding component of the full list of items above.

Worse still, CIO’s and other 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, and this can run counter to the efforts of the Data Architect.

Data Architects 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. The Data Architect can proactively act as a broker to this value-unlocking process.

CIO’s (and sometimes Data Architects by proxy) may  posture against ClearDQ as it swings the “power and control” over digital information squarely away from IT and deep into the business. This is a global trend and not new.

If you are a Data Architect 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.

Data Quality for Financial Performance