Research Hub > How CDW Built a Modern Data Ecosystem for a Medical Technology Leader
Case Study
12 min

How CDW Built a Modern Data Ecosystem for a Medical Technology Leader

With embedded experts and flexible support, CDW helped a company move from manual, siloed workflows to resilient automation.

It’s not unusual for consultants to pivot when clients’ needs evolve, but converting a short-term workshop into a completely different service — and then extending the engagement for more than two years — is rare, says Dan Csoke, a consulting solution architect for data and analytics at CDW.

The customer, a leading global medical technology corporation, originally engaged CDW to provide a Modern Data Ecosystem Workshop for a key business unit. This five- to seven-week service would deliver data architecture and strategy recommendations in response to the company’s recent adoption of Snowflake. 

In discovery, however, CDW learned that the business unit would have to request data access from corporate IT as needed, which prevented CDW from introducing new tooling or architecture into the environment. The business unit had a choice: continue with Google Cloud and BigQuery and budget for it independently, or migrate to Snowflake. It chose to migrate, a decision that created an entirely new set of needs. 

Csoke says the team regrouped to ask how to best help the client going forward, “And that was to immediately stop the workshop activities and convert the remaining budget into more of a consulting and advisory project.”

Another factor making this engagement unique: It began not with the customer’s IT department, as is typical, but through Csoke’s relationship with a marketing leader familiar with CDW’s data architecture expertise. CDW was also able to tailor its support to provide the right skills at the right time as the work evolved. 

“CDW has a great ability to navigate very ambiguous situations,” says Csoke. “We’re able to listen and apply bespoke solutions that are unique for the customer’s individual needs, on an ever-changing basis.”

Building a Data Pipeline for a High-Stakes Business

CDW worked primarily with a business unit that develops minimally invasive technology for cardiology patients. Marketing is a major focus — not routine promotion, but deeply researched, analyzed and regulated data that supports patient outcomes and outreach to cardiologists, primary care physicians and hospital groups. For example, the company uses machine learning to analyze patients’ CT scans for device anomalies. A modern, resilient and automated data analytics pipeline built around Snowflake was essential.

The engagement has lasted for more than two years, with two to eight CDW employees or subcontractors engaged depending on the phase. CDW initially supported the business unit’s migration to Snowflake and helped with making existing Amazon Web Services microservices more efficient and performant. 

The next major phase was a data architecture and data build tool (dbt) engineering project to replace the business unit’s manual, siloed processes with an automated pipeline — a significant undertaking led by CDW’s Jay Brophy, a principal consultant for data, and CDW Engineer Eric Wilka.

Several factors added complexity. The business unit had multiple data sets with varied stakeholders and disparate requirements. Existing processes were manual and inconsistent, with data terms meaning different things to different groups and no documentation to reconcile them. The unit relied on “data heroes,” individuals with total ownership of data analytics workflows — and all of the institutional knowledge around them. 

“Jay had to look at all of the data sources, figure out what the customer needs to make that data manageable and useful, and organize it so that when all the different stakeholders ask questions of it, those questions can be answered,” says Wilka.

As the engineer, Wilka was responsible for implementing Brophy’s data architecture, moving data between systems and ensuring data quality. 

“There were a lot of manual processes, and that means errors, inconsistencies and no backup,” Brophy says. “The value of what we were doing was to automate that whole path.”

As each data set migrated to Snowflake, Brophy modeled the data and created the blueprints, and Wilka built dbt pipelines to move them from development into production.

“As the architect, I had one foot in the business side, which needed to use that data to create their dashboards and visualizations, and one foot in the implementation,” says Brophy. “I had to be sure that I was designing it well for Eric to implement.” 

By collaborating closely, the two ensured that their work supported each other’s objectives without unnecessary friction. 

“That’s what made this whole process operate smoothly,” Brophy says.

57%

The percentage of data practitioners that struggle to interpret their data as a result of missing definitions, context and shared meaning

Source: Modern, “The Modern Data Report 2026: The Data Activation Gap,” February 2026

Flexible Support for Successful Outcomes

While CDW’s technical expertise drove the engagement’s success, its flexibility was a close second, says Csoke. That included converting the initial workshop budget to a consulting engagement, aligning staffing models with the customer’s corporate rate cards, and leveraging subcontractors or badged employees depending on what made sense at each phase — all while maintaining accountability to earn the customer’s trust.

“Instead of having to hire full-time employees, they could rely on us as consultants for six to 12 months and then say, ‘We have another technical need that we’re having a hard time filling,’” says Csoke. “They didn’t have time to upskill their people, and they didn’t want to hire someone for a one-year time frame.”

Wilka, the engineer, says that embedding with the customer for several months gave him a deep understanding of the business unit, which enabled him to be proactive and effective in solving problems.

“Because of the experience from a variety of projects that a CDW engineer has over time, an organization is adding a breadth and depth of knowledge to their team that they wouldn’t necessarily have with a direct hire,” says Wilka.

Flexible Support for Successful Outcomes

While CDW’s technical expertise drove the engagement’s success, its flexibility was a close second, says Csoke. That included converting the initial workshop budget to a consulting engagement, aligning staffing models with the customer’s corporate rate cards, and leveraging subcontractors or badged employees depending on what made sense at each phase — all while maintaining accountability to earn the customer’s trust.

“Instead of having to hire full-time employees, they could rely on us as consultants for six to 12 months and then say, ‘We have another technical need that we’re having a hard time filling,’” says Csoke. “They didn’t have time to upskill their people, and they didn’t want to hire someone for a one-year time frame.”

Wilka, the engineer, says that embedding with the customer for several months gave him a deep understanding of the business unit, which enabled him to be proactive and effective in solving problems.

“Because of the experience from a variety of projects that a CDW engineer has over time, an organization is adding a breadth and depth of knowledge to their team that they wouldn’t necessarily have with a direct hire,” says Wilka.

Automation Builds Resilience and Increases Consistency

Automating the pipeline was a significant shift from how the unit had been working, with manual processes that relied heavily on data heroes.

“These were people who knew their business really well and knew everything about their data sources, but when you dive in to automate this and make this more resilient, you realize the source of truth is really someone’s laptop, because that’s the only place this data lives,” says Wilka. “A lot of their process was, ‘Ask this person,’ and then when people leave or get promoted, suddenly there’s a big gap.”

CDW also had to establish data consistency, ensuring everyone used the same terms to mean the same things. With no documentation available, Brophy built the data dictionary from scratch. 

“We had to make sure that if we called something ‘revenue,’ everybody else called it ‘revenue.’ That was so far from what was happening, it ended up being a major challenge,” says Brophy.

Wilka explains, “Jay went through the hard parts of tracking down important information such as: Who’s the human being who understands what this is supposed to represent? How should this be written to make it useful for other people? What are all of the questions we’re looking to answer from this data so we can put it together for them?”

By automating the unit’s analytics processes, CDW eliminated siloes and sources of friction that had created risk and reduced resilience. Previously fragile processes dependent on one or two people were now hardened and repeatable, with consistent results and documentation. 

“This allows folks to come and go more easily,” says Brophy. “They’re now focused on how to do it instead of chasing someone for the answer.”

“We’re able to listen and apply bespoke solutions that are unique for the customer’s individual needs, on an ever-changing basis.”

— Dan Csoke, Consulting Solution Architect for Data and Analytics, CDW

The Value of Embedded Experts

While CDW provided various staffers over the engagement, Brophy and Wilka were fully integrated, immersed in the unit’s day-to-day business and meeting with colleagues several times a week. 

“The value for the customer is that their problems become our problems,” Wilka says. “When you’re embedded, you get to know the stakeholders, you get to understand their vocabulary and you know what their problems are.”

It’s a dynamic that drives accountability, says Brophy. “Being embedded absolutely is about our ownership and accountability. The customer can depend on us integrating and taking the work seriously,” he says. 

From Csoke’s perspective, CDW’s embedded model lets consultants contribute in ways that surpass the customer’s expectations.

“Jay and Eric are looking at the bigger picture. Jay, for instance, was going out and finding issues and solutions that people weren’t even asking for. He was educating their people on areas of Snowflake they weren’t aware of, so he brought a lot of value,” says Csoke. “Now Eric is doing the same. They’re finding that because he is able to buffer all of these requests coming in from the business, and he understands the business, he can work directly with them.” 

Because Wilka has come to know the business unit so well, Csoke explains, he’s able to work directly with stakeholders and solve requests on his own, which frees up the unit’s leadership.

“According to the customer, Eric essentially became the SME for their current projects. They lean on him heavily,” says Csoke. “He’s providing a lot of value beyond what a subcontractor would, offering multiple solutions for potential problems.”

Amy Burroughs

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Amy Burroughs is an award-winning writer specializing in journalism, content marketing and business communications.