Case Study: Executing an Effective Data Strategy

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Few tasks are more logistically and technologically daunting than providing air, land, and sea transportation for the U.S. military across the entire world. Yet that is precisely the mission of the United States Transportation Command, or USTRANSCOM. According to a Congressional Research Service report, on any given day USTRANSCOM conducts over 240 air missions, sends 1,500 ground shipments, and has 20 ships underway, as well as aiding in humanitarian relief efforts and transporting patients who need aeromedical evacuation.

In his presentation Data Strategy to Effective Execution at DATAVERSITY®’s Enterprise Data World Conference, USTRANSCOM Chief of Emerging Technology Larry McLean discussed how such a complex mission naturally produces complex data and requires an effective and deeply considered Data Strategy plan to manage and secure that crucial data. It all begins, he said, with finding the why: What is the purpose of having a Data Strategy in the first place?

Asking the Right Data Strategy Questions

First and foremost, the Data Strategy nests under the company’s broader organizational vision. The organization’s vision is the basis of the strategy, the strategy guides the creation of goals, and goals are achieved through objectives.

The organization must ask itself: Is the current status quo good enough to survive? Do the data and digital strategies align with the organization’s goals? And do you hope to gain or maintain a strategic advantage over your peers? Having an effective Data Strategy allows the government to make better strategic and tactical decisions. “Especially in our military and government world, we need to make decisions quickly, and they need to be accurate,” said McLean.

Having an effective Data Strategy also helps organizations reap the benefits of data in the first place: better-informed decision-making, understanding customers and trends, providing better products, improving internal operations, and creating additional revenue.

“In the government, we don’t make money. But we sure as heck really want to look at how we can become more efficient, to find ways to do things faster, better, cheaper,” said McLean.

In the particular case of USTRANSCOM, there were a variety of specific reasons that a Data Strategy was necessary, including the need to:

  • Advance decision-making
  • Mature as a data-driven organization
  • Offload common tasks
  • Provide information at the speed of operation needs
  • Use personnel where they’re needed most
  • Understand the vast amount of data that the organization uses and produces every day

Developing the Data Strategy

For McLean, it became clear that developing a Data Strategy was not going to happen over a weekend, especially within the context of a government agency with longstanding bureaucratic norms and entrenched ways of doing things. It would be a multi-year endeavor, requiring organization, patience, and support. The first step was to create a vision: What was the realm of possibility for the organization? What did it want to do?

“You need to ensure that your organization can achieve it. But don’t be so short-sighted that it’s too easy. Make it a challenge, make it difficult,” said McLean.

If the vision is the “to-be” state, the next step is to define the “as-is” state. In the case of USTRANSCOM, there was a great deal of legacy infrastructure that worked well at the time of development – before cloud computing was a widespread option – but now led to unhelpful silos that threw up barriers to enterprise interoperability.

Key exercises during this period included a gap analysis that looked at what the organization needed to accomplish to go from the “as-is” state to the “to-be” state, a consideration of organization priorities, and a SWOT analysis considering strengths, weaknesses, opportunities, and threats.

A major challenge at this point is to bring about cultural change. “You’re potentially uprooting the very processes and legacy knowledge and skills that brought people to their career pinnacle,” said McLean.

People will be highly motivated to defend legacy programs that have worked in the past. The value proposition must be equally defensible, with clear benefits outlined, in order to overcome that resistance. On the other side of the coin, overselling the vision can feed into the hype cycle, where inflated expectations deflate into disillusionment. So, the vision has to be broken down into clear, digestible chunks, to give people some early clear “wins” in what will inevitably be a multi-year process.

Properly selling your vision is key, and that means engaging at all levels of the organization: the C-suite, middle management, and the grassroots level of personnel who do the frontline work. There has to be a sense of transparency and partnership, so that the program isn’t seen as something merely for the data people, but rather something that will make everyone in the company function better. There has to some marketing of the vision to create buy-in at all levels. “People support what they help create,” said McLean.

However, one advantage the organization did have was strong support for a Data Strategy from the commander of USTRANSCOM. There was a standing monthly meeting with the commander to keep him updated on how they were moving forward with strategy and what their achievements were, and this created strong forward momentum and buy-in from management.

Executing the Data Strategy

After the strategy is developed, it’s time to make it a reality. The first step, McLean said, is establishing a foundation by investing in people, technology, and processes. USTRANSCOM established a new chief data officer (CDO) position and a new Data Management team, added people to the Data Architecture team, and hired new data scientists and data engineers.

As training materials, the organization used DATAVERSITY courses, sent personnel to conferences, and brought in noted speakers. For technology investment, the organization bought into Plateau as an integrator and installed IBM’s Cloud Pak for data.

In terms of processes, the team first defined terms in a common lexicon to create a business glossary, which was sent out for peer review, and created a DataOps team. They identified data sources, tables, and elements, then created data profiles and meta-tagged data for quality standards.

Next, they ingested the data, making system connections and security adjustments and optimizing the portfolio. After that came data enrichment and quality review, and that led finally to the creation of data visualizations, expanded analytics, and data services.

The key was to approach the execution from both the “top down” and “bottom up.” Approaching from the top down meant finding ways to immediately show value through tangible results, such as by reducing IT portfolio costs. That reduction in costs led to greater funding to do even more for the organization. The bottom-up approach was to start populating the environment with trusted, valuable data. Although that approach is not glamorous and results are not so immediately tangible, it’s still crucial.


Since starting the process of executing a Data Strategy, USTRANSCOM has embarked on a number of ambitious data projects. The organization currently has 10 active data analytics use cases in motion, and they are working on building a data environment – one project alone has 1,425 tables of reference data that need to be moved out of silos. The current goal of the Data Science team is to build out reusable analytics to be used for future endeavors.

While there have already been notable achievements, there’s still a long way to go. Thinking in the long-term is, ultimately, one of the biggest factors in successfully executing a Data Strategy.

“The real takeaway here is it’s always a multi-year plan,” said McLean. “There’s nothing that you’re going to solve in the first year or two. These are things that you have to be committed to as an organization.”

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Here is the video of the Enterprise Data World Presentation:

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