Data governance: The eat-your-broccoli part of the data-driven enterprise

broccoli-1450274_640Data governance — defined as “the exercise of authority and control over the management of data assets” — is often perceived as boring and bureaucratic, and often gets bogged down in complexity. But it needs to be revitalized to ensure that organizations can rely on the data they’re using for data-driven decisions. That’s especially true in a digital economy where more decisions will be made by analytics, algorithms and artificial intelligence, without human intervention. (Bad data will produce bad decisions, P.D.Q.)

My latest IDC report, “Practices to Revitalize Data Governance,” ($$$$) examines how digital executives are taking a more pragmatic and strategic approach to data governance — to avoid getting bogged down. Savvy CIOs are revitalizing data governance with a streamlined approach that tells a more attractive story: Governance produces data the organization can trust.

The report is based on interviews with Richard Williams, CIO at Celgene Corp.; Juan Gorricho, vice president of data and analytics at TSYS, a global payments services provider; and David Chou, chief information and digital officer at Children’s Mercy Hospital in Kansas City, Missouri.

Data monetization: How to get started creating data products for external sale

Many CIOs are interested in creating data products for external sale — i.e., data monetization — to generate revenue. But not many know how to get started. It’s a dramatically new activity for most enterprises (outside of data brokers like Acxiom, Dun & Bradstreet, and Experian). It requires turning raw data into a high-quality product and building a full-fledged, customer-focused business around that data product, including hiring product managers and salespeople.

My latest IDC report ($$$$), “Creating Data Products,” is intended to help CIOs get started down this road by learning from some of the pioneers: Charles Thomas, chief data analytics officer at General Motors Corp.; Justin Kershaw, CIO at Cargill Inc.; and Sakti Kunz, data monetization expert at Honeywell International Inc.

The report cites best practices such as:

  • Ensure CEO support for risk-taking data ventures
  • Turn raw data into high-quality products that solve customer “pain points” or “data gaps”
  • Staff up with customer-focused, product-oriented talent

The initiative may be led by the CIO or by the chief data officer in close cooperation with the CIO. Either way, the role of the CIO in data commercialization is multifaceted: catalyst, educator, talent recruiter, and builder of a secure data platform.

 

 

The ‘factory of the future’ will require massive investments in skills and data

I contributed — as a subcontractor to Lundberg Media — to a new report from Harvard Business Review Analytic Services about the deployment of technology and data to front-line workers in the manufacturing sector (“The Factory of the Future: Manufacturers Invest in Data and Skills to Achieve Efficiency Goals,” free PDF to download).

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In a recent survey, 78% of respondents strongly agreed with the statement “To be successful in the future, our organization must connect and empower its [front-line] workers with technology and information.” In the manufacturing sector, those workers are what one IT leader called “the deskless people.” They include the salespeople who deal directly with the customer; the employees operating machinery, making products, and keeping the processes running on the factory floor; and technicians out in the field servicing the manufacturer’s equipment at the customer site.

But the barriers to implementing this vision of automation — whether that’s mobile apps or augmented reality, for example — are many: the cost of deploying digital technologies to a broader employee base; a lack of effective change management and adoption processes; and a lack of workforce skills. Current workers need retraining. And they need secure, integrated, and trustworthy data to make decisions.

Pundits say the future of manufacturing involves more robots, sensors and mixed reality. But that will only happen if there’s a massive, comprehensive investment in technology, data and skills.