Developing an enterprise data strategy: 10 steps to take
With data and analytics increasingly driving business decision-making in organizations, data management is no longer an isolated technical function. As a result, developing an enterprise data strategy to ensure that business operations have the information they need is becoming a top priority for data management teams.
That puts new pressure on data architects, data governance managers, data modelers and other data management professionals. In the past, many complained they were “all stuck in the basement and no one cares about us,” said Donna Burbank, managing director of consulting firm Global Data Strategy Ltd. “Be careful what you ask for, because we’re definitely in the spotlight now.”
In a Dataversity webinar, Burbank said creating a business-focused data strategy can seem like a daunting task — and is usually a significant undertaking. But it’s an essential element to support data-driven decision-making, strategic planning and digital transformation initiatives, according to Burbank and other data management consultants.
Here are 10 steps they said chief data officers (CDOs) and data management teams should take to develop a data strategy and get it approved and funded by business executives.
Business strategies should be “infused with data and analytics thinking,” Gartner analysts Mike Rollings and Frank Buytendijk wrote in an October 2019 report on crafting an enterprise strategy for both data management and analytics. Well-aligned data resources and analytics systems “can fundamentally shape the business and reinvent how it creates and delivers value” to an organization, they added. Burbank echoed the importance of mapping business goals and drivers to data management programs. “You can manage data all day long,” she said, “but what’s the business value?” She also recommended translating data management objectives into business benefits. For example, you could say you’re working to improve data quality and the use of data internally, “instead of just saying you’re doing data governance, which sounds boring.” The four phases of enterprise data strategy development
To help shape the details of an enterprise data strategy, you need to collect baseline information on your organization‘s efforts to manage data and control its use, said Bill Jenkins, managing partner at Agile Insurance Analytics, a professional services firm that focuses on insurers. Such assessments should examine staffing and skills, processes, technologies and organizational culture, Jenkins wrote in a May 2019 post on the website of consultancy EWSolutions. Other key capabilities to assess include data governance, data literacy and change management, Rollings and Buytendijk said. They added that as you work to identify deficits and gaps, you need to look beyond your organization‘s data management and analytics teams and the IT department to get a view of existing data assets and competencies across the enterprise.
As with any technology-related project, it’s crucial to have people from the business on your side when you propose a data strategy and seek approval for new investments in data management. But the need for business support is even greater because of the scope of the work involved and the potential costs of fully implementing an enterprise strategy, Burbank said in the webinar. “Having somebody else sell it for you is going to be much more impactful than you selling it yourself,” she said. “[Corporate executives] expect the data folks to talk about data.” Ideally, the early backers will also become internal advocates with other departments and business units when you’re working to get the data strategy adopted throughout the organization, she added.
Burbank said the business case for an enterprise data strategy can focus on a combination of things: increased revenue, new business opportunities, lower costs and reduced corporate risk.
