How to keep your Analytics Center of Excellence from getting derailed

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Photo: Niek Verlaan / Pixabay

By my count, at least two dozen organizations have created a “center of excellence” (CoE) for data analytics, business intelligence, artificial intelligence, and machine learning. (Most are called the Analytics Center of Excellence (ACE), or something very similar.) In my latest IDC report ($$$$), Creating and Managing an Analytics Center of Excellence, ACE directors describe their methods of keeping their centers focused on high-impact business projects. (Otherwise, data scientists may stray into intellectually interesting pursuits that don’t produce business ROI.)

The report includes insights and advice from the following CoE directors: Philip Jenkins at Verizon Communications Inc.; Margery Connor at Chevron Corp.; and Constantinos Kotopoulos at Information Resources Inc. (IRI).

 

 

 

Debut of the ‘Digital Business’ newsletter

I’ve launched a newsletter called Digital Business on the Substack platform, which is designed for subscription-based editorial newsletters. The intended audience is “digital executives,” such as CIOs, CTOs and CDOs, although I can also imagine other job roles that would find this market intelligence very valuable.

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The newsletter is currently free, but I hope to eventually make it a paid subscription product, to compensate me for the huge amount of research, writing, and fact-checking that goes into it.

The first three editions are out, with lead stories about:

In addition, each issue typically has news briefs about innovative digital initiatives at U.S. corporations; C-suite job openings; and recent appointments of digital executives (who’s in, who’s out). Sometimes there’s a “brain food” section, with thought-provoking quotes about digital business and innovation.

To compile this newsletter, I read dozens of newsletters, articles, and websites each week. Obviously, I’d love to have you subscribe and find out what I’m discovering about real-world digital business initiatives & executives.

 

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.