Mastering Data Leadership: Strategies for Impact

By Talo Thomson

Published on September 14, 2023

An aerial view of a vibrant cityscape at night, with a illuminated pathway highlighting the journey to mastering data leadership

What does it mean for an organization to be data mature? And how can leaders enhance data maturity? Assessment is the starting point, and various approaches (and maturity models!) exist. However, in most models, leadership is either downplayed within many other factors, included as an afterthought, or completely disregarded. By contrast, for Alation’s data maturity model, leadership is a core pillar. 

This blog explores (1) why leadership is critical to data maturity and (2) examples of what data leadership looks like in practice.

What is data leadership?

Data leadership is the strategic guidance from respected leaders who champion the value of data at every level of the business. These people oversee a comprehensive communication plan and often manage a change management program focused on data literacy skills. They continuously emphasize the connection between data and business value through metrics and reporting.

Data leadership is a core pillar of data maturity – which modern companies are striving to achieve. The four required “table-stakes” for data maturity include technology that makes data search & discovery easy and intuitive; data literacy training for employees; a documented data governance program with clear policies, quality rules, and monitoring, and finally, robust data leadership. This is the most critical (and often most difficult) element needed to enhance data maturity.

Finding leaders to enhance your workforce’s data literacy is a challenge. According to Gartner, "Recruiting, retaining, and upskilling talent plague the majority of D&A leaders in 2023,” noting that 43% of D&A executives who participated in their survey cited talent challenges as their key concern.

Yet those who put in the work reap the rewards: “The D&A executives who work to reduce talent shortages, change management initiatives and data literacy have an above average success rate in comparison to their peers," the Gartner report concludes.

The following real-world examples illustrate how data executives today successfully enhance organizational data maturity by crafting business-forward data strategies, growing data collaboration across departments, and leading change management initiatives focused on data literacy.

Start with strategy and business value 

What’s your vision for data? Why should others embrace it? Savvy leaders start by articulating a data strategy and communicating it. Craft a concise, impactful, and memorable vision that links directly to business value.

Steve Pimblett is the Chief Data Officer (CDO) of the Very Group, a UK conglomerate of mail-order catalogs with a rich data heritage tracing back to 1890. Today, Very is an e-commerce platform hosting more than 2.2 million daily web visits and 4.8 million active customers. When he became CDO in 2020, Pimblett faced a complex and siloed data landscape, with legacy units making automation, governance, and stewardship tough. His first focus was defining his team’s strategy.

“We set ourselves up as a team with a mission,” he recalls. Today, “There are four pillars to our data strategy and our mission. Data, insight, action is how we communicate the value that we help to create across the business.” What sounds simple on the surface has effectively shifted how the organization views the role of data, enabling them to trust how it’s put into action.

Slide deck from The Very Group showcasing the DNA Missions (Data, iNsight, Action)

Source: How The VERY Group Is Driving Data Governance with CDO Steve Pimblett | revAlation London 2022

That mission has become part of the organizational DNA. “Actually, we tend to work the other way around now: ‘What's the action you're looking to take? What insight do we need there for what data do we need to collect and own?’” he elaborates. By leading with a business focus, Pimblett has successfully linked data management to business outcomes. Not only has this transformed how people communicate and consider their needs, it has supported a new level of cross-functional collaboration — alongside growing data literacy.

Pro tip: Make sure your data platform has a dashboard to measure usage via metrics like number of users and time and money saved. Measuring the business value of your software is critical to growing your program.

Create a data operating model true to the org chart

When Steve Pimblett first arrived at Very he found data chaos and inconsistency. A lack of formal rules and processes, coupled with minimal visibility into trusted data, had led to a data culture where “there was nothing at the center.” 

To organize that chaos, he launched several data Centers of Excellence that mirror how the company is organized. This hub-and-spoke model enables leaders in respective functions to work closely with data experts best positioned to address their unique needs. 

“[Today] we've got five centers of excellence [that] we use as a partner structure,” Pimblett says. “So whether it's helping retail with forecasting financial services, credit prediction, customer experience… We have this bespoke model that works really, really well from an organizational operating model perspective.”

With this new structure as catalyst, not only did Pimblett promote the new vision — he acted as data sponsor and advocate to each unit of the business through the CoEs, which has enabled him to build trust with other leaders. 1

Train the business to “speak data” with productization

While a clear vision for data may build confidence, a larger challenge often arises in bridging the gulf between your data and business teams. Simply put: How do you get business leaders to “speak data”? 

Productization can help you speak the same language focused on business outcomes. This demands tying the business use case to the data process that powers it. “Trying to communicate a data strategy that really drives value is complex because you've got a lot of technicality and a lot of data and the business community that maybe don't understand it,” he shares. “So …to make it simple to communicate [we have] productize[d] all products and services that we offer.” 

Slide deck from The Very Group showcasing the DNA: Our key products.

A shift occurred, in which people grasp what they need in data terms, and can ask for it. “Out in the business, people really start to understand what these mean and they'll actually say, ‘We want a pipeline to push our data into a target system or I'm interested in the single customer view,’’ he says. “We've productized the vision to make the communication easier and that's really helped integrate, technology data and the business to form one team.”

This change demands a reset in people, process, and technology. Train people to comprehend their needs in data product terms – and make requests in that language. It’s worth noting this shift in process takes super user-friendly technology. In Pimblett’s case, his team uses a data catalog, which offers tooling that makes data easier to comprehend for newcomers. Features like business glossaries, embedded conversations with data-asset SMEs, and wiki-like articles can clarify an asset’s top users, history, and common use cases to support smarter usage.

One caveat: Just as upskilling your workforce to be more data literate demands a competent data leader, so too a more literate workforce will require increasingly competent leadership. This a healthy sign of growth!

Change management via ADKAR

How do we get people to embrace data and change willingly? Greg Swygart had the same question. As the VP and Director of Data Engineering and Analytics at Fifth Third Bank, one of the largest consumer banks in the Midwestern US, a core objective of his role involves enhancing data maturity. In the past 3 years, Swygart has been focused on growing organizational data literacy – which demands getting people to train and adopt new data behaviors.

How has Swygart approached this challenge? Leaders may be familiar with the popular change management framework ADKAR, which stands for Awareness, Desire, Knowledge, Ability, and Reinforcement. ”Change is really really difficult and so it's almost impossible to talk about a successful data management rollout at scale without talking through change management as a formal process,” says Swygart. “The change management procedure that we've deployed is ADKAR.”

“We start with “Awareness: do consumers have awareness?” he continues. Do they have the desire? Do they have the knowledge, the ability? And are you reinforcing it? ”From there, building desire takes deliberate data reframing. “Make data fun!” Swygart advises. “Focus on adoption and curation, ultimately directing people back to a marketplace,” where they can self-service data and connect to experts.

Slide deck showcasing the definition of ADKAR

Source: Data Management Meets Human Management Why Words Matter

To build knowledge, Swygart’s team focused on training. “Many data consumers have not had the formal training required to unleash analytical capabilities,” he shares. “So what we did here is we developed a data management training curriculum and provided the right processes and tools in order for people to unleash data capabilities.”

Why words matter

Making data fun and getting people to adopt new data behaviors can be a challenge. The words you use to reinforce those new behaviors have a big impact on your team's willingness to participate. Take data stewardship as an example. Let’s say you need to get people to embrace data stewardship tasks. Do you assign them stewardship duties or recognize their new stewardship responsibilities?

Words matter in getting people to change. According to Bob Seiner, data governance consultant, ”If we assign somebody something… it immediately feels over and above what you're presently doing. But if we recognize that you use data a certain way and we provide you with the assistance and a catalog to get to the information about the data… then it's not going to feel as though it's something that's brand new to you.”

When it comes to driving change, carefully choose the words so that you’re motivating people to join your efforts, rather than dragging them along unwillingly! Again, embrace super user-friendly software to make the process less painful. Choose a platform that helps you monitor the change over time and empower people with the knowledge they need to grow. Host regular learning sessions, and recognize and celebrate those individuals publicly.

In summary, a robust change program has three pillars: (1) The team understands, not just the reason for the change, but how it will make both them and the organization higher performing. (2) They have the desire and skills to do the work. (3) They are incentivized and rewarded for using the software.


Clearly, leadership is an essential element of any data maturity program. It is also the most difficult, which may well explain why so many organizations struggle to achieve data maturity. Yet there is a way forward. Key pieces of leadership include the need for a clear vision, a vocal and well-respected sponsor, a change management program & onboarding process, and an ultimate focus on business value. 

It takes some work, but the results are worth it!

If you’re a business leader convinced you’re ready to start the journey to maturity, you may be wondering, “Where do I get started?”

Learn how to put this advice into action:

1. For another perspective on how to manage data Centers of Excellence see Taming the Data Octopus: How Avista Transforms Data Into Value by Nolan Steiner, data science team lead at energy distribution company Avista.

  • What is data leadership?
  • Start with strategy and business value 
  • Create a data operating model true to the org chart
  • Train the business to “speak data” with productization
  • Change management via ADKAR
  • Why words matter
  • Conclusion
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