The Chief Data Officer

Many organisations are experimenting with the concept of a Chief Data Officer. Although according to Raconteur most are struggling with the concept as well as making headway with the concept of managing data for value.

It seems that there are a number of problems:

• Business Understanding of the Role and Data Issues;
• Resourcing, Funding and Provisioning Data Initiatives;
• Current Starting Points for many Businesses with Poor Data Quality;
• Focusing too much on compliance and not enough on Value.

Personally, I would suggest that there is a value staircase for Data Management starting with Compliance and Protection on the Bottom Rung and then progressing through Quality, Integration, Sourcing (external data feeds), Insight & Intelligence to Adaptive Control (involving use of machine learning, experimentation and up to the minute data feeds to optimise control).

It is difficult howver to obtain value if a business tries to progressively work its way upwards through this staircase if it attempts to do everything in the first one before attempting the next step. The scope is too wide. Instead busnesses need to prioritise a business problem or opportunity and address a vertical slice down this value staircase to deliver value quickly. Progression can be achieved by addressing the next opportunities and reviewing what is needed in each step to improve overall capability as each increment is implemented.

However, probably the biggest issue is the role. What is the difference between a CIO, a Chief Digital Officer, a CTO, a CSO, a Principal Data Archiect and a Chief Data Officer? These all overlap and are often confused in scope. Many businesses are handing the Chief Digital Officer role back to the CIO, so it may be time to do the same with the Chief Digital Officer, because if they are separated the CIO's role is hollowed out to just look at the plumbing and not the value to the business.

It is also important to delegate data quality and integrity to business managers responsible for exploiting data in their day-to-day operations. IT may provide the policy framework, tools and measurement framework for managing data quality, but line management needs to take responsibility and include data quality in their personal objectives, just like they do with budgets. Otherwise value will never be achieved.