What is Data Strategy? “Well, the short answer is, it’s the data chapter of your business strategy,” said John Ladley, consultant and author of the book Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program. Contrary to popular belief, the one thing it isn’t, he said, is a technology procurement plan.
Historically, such strategies have tended to be extremely technical, but an organization’s data ecosystem requires broad framing; something to position it within the organization, not just the technology stack or the Business Intelligence stack, Ladley said. “A Data Strategy is a most important artifact that defines your enterprise Data Management strategy. It frames how you treat your data assets,” and defines how data will help the organization accomplish its goals.
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In a recent interview, DATAVERSITY® spoke with Ladley about Data Strategy Trends in 2022 and beyond, what trends he’s been seeing, and what obstacles companies encounter when implementing a Data Strategy.
Data Strategy Scope
A Data Strategy encompasses everything in the supply chain of data from where it’s created, to where it is disposed of or leaves the organization. “It’s the framing artifact for all the technologies in the Data Management ecosystem, encompassing operational systems, new apps, internet of things, data warehouse, data lakes, data fabric, data mesh — it’s not just one end or the other,” he said.
More importantly, it connects what the organization wants to do and where it wants to go with the data capabilities required to” help the organization get there.
Data Strategy by Default
“Every organization on the planet has a Data Strategy,” he said. Even if that strategy is not to have one, “no matter what your strategy is, whether you’ve declared it officially or not, you’re going to have predictable results.” Not to declare a strategy is, in itself, a strategy, which means letting the cards land where they fall, he said.
Data Strategy Redux
A lot of organizations put together a Data Strategy and it just sits on a shelf gathering dust until another consultant gets hired to do a Data Strategy a few years later, he said. Twice, Ladley was hired by large organizations to do a Data Strategy, and at the end of all the discussion, he sent them the Data Strategy he’d done for the same organization five to ten years previously. “What’s really interesting is, there’s not much difference in what they need, if they haven’t had any substantial changes in their business model, their market, or their environment.”
Data Quality Efforts Drive Awareness
One Data Strategy trend Ladley sees is a growing number of organizational leaders coming to the realization that their organization needs to improve the quality of its data. Without a strategy to determine what data should be their first priority for cleanup, an organization’s efforts at remediation can be derailed.
A company planning to implement Data Governance and avoid the regulatory consequences of personal information mismanagement can’t effectively mitigate risk without a formal Data Strategy to understand data lineage. As a result, organizational leaders are showing more interest in Data Strategy.
Leadership Suite Gets on Board
The leadership suite is becoming aware of the relationship between planning and the realization of data value and, as a result, more organizations are starting to get serious about being formal with data. “Whether it’s MDM, AI, Data Governance, or some regulatory impetus, leaders are now asking their Chief Data Officers (CDOs): ‘Well, what’s our plan? What’s our strategy?’”
In response, CDOs don’t often have a strategy because they haven’t been allowed at the table, but recent surveys about Data Strategy trends have shown that this is changing, he said. Anytime the organization plans to use data, that data must be aligned with the organization’s direction, and therefore, “You need a formal plan, and that plan is a Data Strategy.”
Differing Data Strategy Trends Based on Digital Maturity
Trends in how organizations approach Data Strategy differ based on the level of digital maturity the organization has been able to achieve, Ladley said. Organizations with a lower baseline of digital maturity will typically misunderstand the fundamental components of a Data Strategy, assuming that key elements are optional.
Often considering Data Governance “too technical,” they will decide to add it later, and instead of a Data Strategy, they end up with a technology implementation plan. Companies that don’t understand and accept that getting formal with data assets requires change, will continue to settle for a technology implementation plan instead of a true Data Strategy, he said, and he sees that flatline trend continuing in less-mature organizations.
Understanding the Value of Data
Organizations with a more sophisticated understanding of data are now adding new elements to their Data Strategy, such as net present value numbers, asset type numbers, income statement topics, and the impact of data on the balance sheet.
“This is a Data Strategy trend I see slowly gaining momentum as more and more organizations become aware of the enormous, untapped monetary value of doing things with their data.” He predicts gradual linear growth in this area for the next few years, rather than an upward exponential curve.
Data Literacy Challenges
Ladley identified three major challenges to Data Strategy, the first being a lack of Data Literacy in the organization as a whole. “If you’re going to capitalize on da
ta, you need to understand what that really means,” he said. It’s possible to start small and grow in capability and maturity over time, but the overarching need is to embrace a more formal relationship with data and the behavior change that formality entails.
The concept of “Data Literacy” is not just about using and communicating with data. It is also the management, the care, the feeding, and the administration of data as part of the big picture, he said. “This is a big challenge. This needs to be taught.”
The CEO typically doesn’t know the financial impact of Data Quality on the organization, or that intrinsic in the data stack is enormous risk, and the risk can be quantified and managed. “And they do need to know that. That’s not all they need to know, but they need to know that.”
This literacy barrier is what Ladley considers the most significant challenge, but a good Data Strategy will address it.
Staffing Challenges
The next challenge that he’s seeing he considers partially a sign of the times, but also a sign of maturity of the profession: a lack of staff overall, but particularly a lack of staff who can really do a proper strategy. Many data people have been trained to think of a technology procurement plan as a Data Strategy.
The process of learning to do a real strategy takes some specialized training — it’s just not a boxes and arrows exercise, he said, and it can be quite a bit of work. More sophisticated strategies for big organizations can take several months to come together.
Cultural Barriers
The third challenge is managing cultural barriers around data, he said. “You don’t change the culture — you change behaviors within the culture.” There is no magic word that will make everyone jump on board, he said, because people simply don’t like change. It’s hard work to engender change in an organization. “And if it wasn’t hard work, there wouldn’t be so many people talking about it.”
Image used under license from Shutterstock.com
