A Smarter Intake Process for Better Results

A global consumer packaged goods company faced a dilemma. As the organization grew, so did the complexity of how data and analytics projects were requested and prioritized. With multiple business units submitting overlapping requests, stakeholders lacked transparency into project decisions, and valuable resources were tied up in unclear or misaligned initiatives.

Inside the Data and Analytics Office (DAO), the priority and details of incoming requests were inconsistent and difficult to act on. Outside the DAO, visibility was a critical gap: business units often submitted requests without knowing what happened afterward, or they chose not to approach the DAO at all because they were unaware of the DAO’s priorities and capabilities.

The team needed a faster, more structured way to manage intake and prioritization to keep pace with business demand and ensure maximum impact across the organization.

The client’s DAO team struggled with project submissions coming at them from all directions. Requests often lacked the information needed for DAO to begin prioritization or scoping, and there was no consistent process for intake or evaluation.

Without clear visibility into DAO priorities and criteria, stakeholders described the process as a “black box” with limited understanding of what projects were in flight or why certain initiatives were selected. DAO staff also struggled with knowing what work to tackle first.

This created frustration, slowed down delivery, and threatened to divert resources to the wrong projects. Without intervention, the DAO risked losing credibility and missing opportunities to deliver measurable business impact.

The client asked Elder Research to facilitate a four-day design sprint based on the methodology outlined in the book Sprint by Jake Knapp. The sprint focus was to address challenges in intake, prioritization, and role clarity, and to deliver a tested prototype the DAO could put into practice.

Key activities included:

• Problem framing and mapping: Defining challenges with intake, roles, and prioritization.
• Ideation and prototyping: Creating “How Might We” statements, sketching solutions, and drafting a structured intake process.
• Prototype testing: Developing and testing an intake form and process map with end users to validate the model.

The resulting prototype outlined a structured intake form, a clear process for prioritization, and defined “go/no-go” checkpoints to avoid bottlenecks—the equivalent of project purgatory.

To test the prototype’s impact, one of the client employees walked end users through the proposed process and mock tool interfaces. These end users noted that the new process was clearer than the current approach.

While some required additional explanation (due to not being typical request submitters), those familiar with DAO processes affirmed the prototype clarified roles, simplified intake, and increased transparency.

The sprint delivered an actionable prototype that will serve as the foundation for the Data and Analytics Office’s new intake and prioritization process. Immediate benefits included:

• Transparency: Clear visibility into project status and decision-making.
• Efficiency: The intake form replaced ad-hoc conversations with a structured process, reducing the reliance on leadership for constant monitoring.
• Focus: By capturing the critical details up front, it makes requests easier to scope, prioritize, and filter—freeing leaders to focus on the highest-value work.
• Alignment: Defined roles and responsibilities, improving collaboration between DAO, IT, and business functions.

A factor in this sprint’s success was the balance of perspectives: an external facilitator who brought industry experience paired with someone within the DAO who understood the culture and participants.

Drawing on the Sprint book and previous facilitator experience, several other elements also contributed to success: clear decision-making authority, shared goals set up front, leadership support, and participants open to trying a different way of working.

Streamlining intake can significantly add to analytics ROI. This work resulted in faster, clearer decisions; fewer distractions for leaders; and more capacity pointed at high-value work. Scattered requests were transformed into a transparent, prioritized portfolio the DAO can launch, measure, and scale across functions.