A Chief Data Officer Challenge: Build Your Data Strategy

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chief data officerThe Chief Data Officer landscape presents a verdant picture in many ways. Gartner, for example, has estimated that one-quarter of large global organizations had already hired a Chief Data Officer (CDO), and that by 2019 that figure will rise to 90 percent.

Noting that the Office of the CDO is responsible for Data Analytics initiatives and Data Governance, as well as defining the Analytics Strategy for the organization and ensuring that information is reliable and valuable, the research firm’s 2016 CDO survey finds plenty of reason for encouragement about the role, including:

  • Nearly 40 percent of CDOs regularly participate in executive committee meetings
  • Forty-six percent are involved in revenue generation and around 70 percent are involved in new initiatives, enabling them to contribute to the competitiveness of the enterprise
  • Seventy percent of CDOs believe their peers, superiors, and reports see the office they lead as a “change agent” and a majority think their superiors and reports see the office as successful.

New Vantage Partners in its Big Data Executive Survey 2017 adds further good news: 48% of firms believe that the primary role of the Chief Data Officer should be to drive innovation and establish a data culture, and 40 percent indicate that the role of the CDO should be to manage and leverage data as an enterprise business asset. Only 6.9 percent suggest that regulatory compliance should be the focus of the Chief Data Officer.

Yet, challenges remain in realizing all the potential of the role and the office. One of them was the focus of a presentation by Micheline Casey titled “Developing a Data Strategy – Ensuring Alignment and Prioritization with Business Strategy to Drive Value” at the CDO Vision event at this year’s Enterprise Data World 2017 Conference: How to build a truly effective Data Strategy and sell it to stakeholders?

Start with a Strategy

What defines such a Data Strategy? It’s actionable, measurable, and relevant, according to Micheline Casey, Principal at CDO, LLC, a consultancy supporting large-scale enterprise Information Management, Data Governance, and Data Security efforts. She previously served as the first CDO for the Federal Reserve Board and the Colorado State Government.

As a data consultant, Casey has seen companies embrace the fallacy that just having more data and implementing Data Science initiatives will solve the problems they experience in leveraging their data the way they need to. “You need a strategy and a plan,” she said, noting that Big Data and Data Science projects typically have the same failure rate as other IT projects because they don’t generally start with those critical pieces.

“If you are new to this, a Data Strategy aligns and prioritizes data and analytics activities with key organizational priorities, goals and objectives,” she said. After all, there are only so many resources to go around, so they must be leveraged for the most relevant ends. The business case matters: “People buy into why things are happening more than what and how,” she said.

A living document that calls out key data and analytics anchor projects in the overall Data Strategy, aligned to what is going on in the business, sets a tangible stage for inspiration, guidance, and action. A handful of manageable goals exist within these anchor efforts, each with its own projects and programs prioritized on a roadmap. The risks around being able to achieve them (such as not having the right people available on staff) must be explained, and the success enablers, like Change Management, accounted for, too. Periodic reviews of the document are important, as well: “At the Fed we had separate meetings with stakeholder groups and out of that we juggled and re-juggled a myriad of activities on the roadmap,” she said.

“How you will measure things – the KPIs and metrics that weave into performance management programs for teams and others across the organization” come into play, too, she explained.

Tell the Data Strategy Story

Casey discussed that there’s a production process to getting a Data Strategy out the door to stakeholders for funding and then for keeping the work on-track. That incorporates everything from pre-planning and alignment to understanding all components involved to telling the story of the strategy and then reviewing the lessons learned from the strategy the CDO presented and how the presentation was received.

You want stakeholders to buy in strong from the start, and it’s best to present to these high-level individuals high-level insights of what the strategy encompasses – a few dozen pertinent slides rather than a long Word document filled with technical details, for instance. Ensure that the presentation links into the enterprise planning and budget cycle: “If the budget planning is in October start working in January so you have stakeholder buy-in in advance,” she advised. (And make sure that your budget is within the range that financial staffers at your organization would expect.)

It’s helpful to be able to provide business case input that includes returns on investment projections for objectives and goals. That can be tricky, she admitted, as “sometimes a key project may not have the ROI you need” to get leverage. In that event, boost your case by perhaps taking on and highlighting a project with a bigger multiplier effect down the road, like Master Data Management (MDM) in the service of Predictive Analytics.

To build up the strength of your Data Strategy, she urged the audience to look at artifacts throughout the organization and the sector that can be pulled into the plan for identifying key projects and initiatives, whether from specific business units, the technology organization, or even analyst reports on where markets are going, for instance. “Take advantage of organizational working groups and committees to funnel their inputs, and have one-on-one interviews to help longer-term buy-in,” she said. “Make this agile and iterative.”

Run each Data Strategy component, from overall vision to business cases and roadmaps, as individual sprints, making sure you have the full confidence of stakeholders on one point before moving to the next. “If you haven’t done validation with anyone there’s a huge risk things won’t work right,” she said.

She emphasized strongly that CDOs need to hone in on their storytelling in their presentations:

“Of why data and analytics have value to the organization. Promote empathy with user stories and experience. Find narratives that strike a chord with people. Run your Data Strategy like an agile development project and tell the story from the end user perspective.”

And don’t forget the post-mortem among your own team. “Once the strategy is handed off for final review and approval, get the team together and document what worked and what didn’t do it for stakeholders,” she said. It’s important to get that down in writing ASAP, as she guarantees “that by the time you go back to update your work you are going to forget these things. The sooner you can get that information down the better off you will be.”

The bottom line, she noted, is that your Data Strategy should take you to the place where you can leverage data to add value to the organization and deliver a competitive edge.

Here is the video of the CDOVision 2017 Presentation:



Photo Credit: Wright Studio/


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