by Bas de Baat

3 top priorities for a Chief Data Officer

Feb 22, 2017
Artificial IntelligenceC-SuiteEmerging Technology

“The goal is to turn data into information, and information into insight.” – Carly Fiorina

7 artificial intelligence
Credit: Thinkstock

Somebody told me the other day that “information permeates through the body of an organization.” It is a perfect expression that helps understand how important data quality is in the digital era. After all without it, there’s no meaningful information. A growing number of organizations have introduced the role of Chief Data Officer, however the priorities for this new corporate officer aren’t as clear.

Gartner predicts that by 2019, 90 percent of large organizations have a Chief Data Officer. The main responsibility of the Chief Data Officer is to manage data as a corporate asset. Having said that, the boundaries of the role vary by organization. Whereas some organizations draw a clear line between data and information, others blur the two and that’s a problem.

The ongoing debate is about business analytics. Where does it belong? Does it belong to the data or the information domain? My point of view is that business analytics belongs to the information domain. It is a shared responsibility of the corporate officers who lead the different lines of business. They are the consumers and ultimate decision-makers. The Chief Information Officer fulfills a catalyst role. The Chief Data Officer must understand their business needs as a supplier of a platform that manages one of the most strategic assets of the corporation: high-quality data.

What are the 3 top priorities for the Chief Data Officer in an era where artificial intelligence and other emerging technologies only deliver value when data is of high quality?

Evangelize the importance of high-quality data

The Chief Data Officer must go on a never-ending road trip to sell the importance of data quality. Business leaders struggle to understand why data quality remains to be an issue despite huge investments in enterprise applications, standardization of business processes and on-the-job training of employees. The key message is that business continuity and growth can only be achieved if data is managed as a corporate responsibility with the Chief Data Officer as orchestrator. To manifest this change, the Chief Data Officer must report to the CEO or COO.

Manage a consistent cross-application data model

The core of the data quality issue is the lack of a simple data model that is consistent across enterprise applications. It is not a technology issue, but a people and process issue. If you ask how many customers the organization has, guaranteed that marketing has a different number than finance. It comes down to defining what a customer, product or vendor is. And that is a challenge for organizations that become more and more complex every day and of which many still allow siloed behavior. In this situation, the Chief Data Officer drives business value by providing consistent data definitions that are adopted enterprise-wide. Simplification of data is part of the definition process. Organizations have a tendency to complify (read: make more complex) things. The Chief Data Officer must be on the run to put a full stop to that.

Synchronize data across the supply chain

In the smart world we are moving towards, organizations have to be ‘connected’. They are part of complex business networks. That means that data definitions go across organizational boundaries. As an example, automotive parts suppliers must maintain data models in their enterprise applications that are consistent with the car manufacturer. That’s the only way to optimize the supply chain. The data integration concept is not new, however in reality it is a challenge to make it happen. The role of the Chief Data Officer is to collaborate with partners and set industry data standards.

There’s a reason why these are 3 are the top priorities. As we are digitizing our business models, data quality is a must have. Organizations realize they have to be able to operate “real-time.” Enterprise applications must be able to deal with data from many different kinds of smart sensors. A chat bot in a call center must be able to understand what kind of customer it is dealing with. An algorithm of a predictive maintenance solution needs accurate asset data to make meaningful recommendations to the maintenance planner. An accounts receivable administrator must be able to rely on the exceptions that the ERP system puts on the work list for a follow-up call with the customer. Data quality is crucial and has become an asset that can give the organization a competitive advantage.