The Bloodwork 

It’s not often that corporate change management is a life-or-death situation. However, when you’re one of the largest manufacturers of plasma-derived and human protein products in the world, there’s a lot on the line. 

This client develops and produces therapies based on human proteins derived from plasma and human cell lines, for patients around the globe. But with so much information from disparate departments, it was drowning in a sea of unreliable data. It was difficult to rank and manage supply, much less make high-stakes decisions about collection, production, and quality. 

A Data Clot 

The vision was to continue improving patients’ quality of life through the power of plasma. But the current environment wasn’t ready to support that ambition. 

No Shared Strategy. The client’s data strategy wasn’t formalized, which meant teams weren’t working from the same information. Ownership and accountability varied, and data quality depended on the source. This was the main reason why it was so painful connecting data decisions to work the client needed to accomplish.  

Unclear Ownership. Roles and responsibilities for data management were unclear or incomplete. Without consistent accountability, important questions about stewardship and decision-making were harder to resolve.  

Gaps in the System. The organization needed stronger governance, documentation, and controls to reduce operational and compliance risk.

A Case for Change. Leaders needed a practical case for change — one that could secure funding, build support across the business, and turn data management into an actionable organizational priority.

None of this would be possible without an infusion of change management first: a data strategy that would help teams understand why the changes mattered, and how they could take part in making them happen. Just like its patients, the client needed a lifeline, and a partner that could bring these challenges into focus.

Improved Circulation 

This engagement aligned with three areas of AHEAD’s expertise. 

• Data Platform Acceleration: Defining the vision, operating model, roles, standards, and roadmap to manage data as an enterprise asset. 
• Platform and Workload Modernization: Evaluating the tools and architecture for scanning, organizing, governing, and using data more effectively. 
• Operational Excellence: Building the stakeholder alignment, communication cadence, and organizational support to move from assessment to execution. 

The work unfolded across three phases: 

Advise 

AHEAD began with a baseline assessment of the data organization. The team immersed itself in the client’s environment, reviewed existing capabilities, and conducted interviews and working sessions with business and technical stakeholders. The review went beyond just systems and tooling, examining how the organization made decisions about data and what stood between the current state and the client’s goals. 

AHEAD identified 39 process gaps across data strategy, quality, architecture, policies, and consumption, then worked with the client to rank the gaps by business impact. 

Why the Groundwork Mattered: The client’s teams and leadership had a shared plan and language for discussing data and how to improve it. Data management was directly linked to business outcomes, rather than remaining stuck in the nebulous category of technical housekeeping. 

Build 

AHEAD then began helping the client shape the people, processes, and technology needed to address each gap. That involved clarifying responsibilities and defining the necessary roles for sustaining a mature data organization. AHEAD also helped develop data domains, governance practices, and quality assurance for the target-state architecture. 

Enterprise workflow tooling for data management was vetted here as well. BigID provided a way to scan and understand the data environment. Microsoft had the strategic technology foundation and served as an important co-advisor. Databricks brought the platform for building more capable data and analytics workflows. 

Last but certainly not least, teams finalized an initial roadmap and 60- to 90-day plan for turning the assessment’s findings into action. The client didn’t have to solve every data problem at once. The roadmap created a sequence for improving standards over time. 

How It Came to Life: AHEAD turned a diagnosis into an operating model. Data strategy became something leaders could discuss, and that teams could act on and measure over time. 

Run 

The client began moving production into a scalable cloud environment. It had a system for managing plasma-related data, and using it to improve executive readouts, workshops, and governance planning. 

How It Kept Delivering: The client had better accountability and communication between business and IT. Leaders became internal champions for the proposed organizational changes. 

A Stronger Pulse 

The client now has an organized, business-aligned data strategy. 

A Prioritized Roadmap. The 39 identified gaps were focused starting points for funding, sequencing, and decision-making. 

A Data Operating Model. Clarifying responsibilities and creating a shared view of the data challenges broke down silos that had slowed progress. Formalized data domains, governance processes, and standards have led to consistent operations. 

Effective Plasma Management. Data practices are now informing supply and production strategies to support the therapies patients depend on. 

What’s Next 

The client’s data journey continues, with the next phase looking toward expanded cloud capabilities and advanced analytics. AHEAD remains an important part of the work, as a trusted advisor for technical work and human coordination. Data transformation comes from a shared understanding of how the work gets done. AHEAD helps build that understanding and the processes that make change matter.