Data Center Capacity Planning Tool

Results
– Substantial projected cost savings of $XX billion in 2023, validating the strategic shift towards leaner designs. This transformation supported AI integration while strategically deprioritizing lower ROI and non-critical projects across the organization. – Enhanced organizational focus, directing resources towards high-impact AI-centric endeavors, augmenting overall operational efficiency.

Methodology

– I spearheaded the design and rollout of the first Data Center prototype by collaborating and winning buy-in from partners in Engineering, Capacity Planning, and Strategy thereby establishing a foundational framework in Q4’22. This prototype became instrumental in bridging the gap before the subsequent launch of the Minimum Viable Product (MVP) six months later.

-I was responsible for creating the first draft in under a week which outlined all of the logic, inputs, and operational assumptions necessary to create a functional planning solution that could take the place of our current planning process. My focus during this phase was to ensure that engineering and data science understood the minimum requirements required for a functional software product and consistently communicated the value proposition for this effort to ensure the team worked to a common outcome.

– I validated my assumptions on our product features by insisting on alignment with cross-functional teams encompassing operations, design/engineering/construction, and capacity planning, ensuring a cohesive and efficient execution of the operational outcomes of the project.

– I was responsible for maintaining the integrity of the inputs and outputs based on our defined system logic as well as delivering on our lead times. I did this by clearly communicating requirements, timelines, and dependencies with the cross functional teams.

-After we accomplished alignment with the working prototype from the executive team, our focus shifted to building the MVP for the internal software product. This involved consistently communicating our objective to launch a working MVP and ruthless prioritization of time spent on certain features not directly in scope with our objective. I was directly responsible for ensuring that the integrity of the model’s financial and operational outputs were upheld. This was done through extensive data attestation and feedback sessions with our engineering teams to make sure that the logic was soundproof and the output matched the value promised to our end users.

Key Challenges

– The intricacies of processing extensive partner inputs and transforming them into a coherent and insightful long-range capacity plan and cost profile was perhaps the biggest hurdle. I worked with the engineering and capacity teams to assign owners accountable for attesting to the data and how the logic functioned for those particular inputs and outputs. This allowed for shared ownership and further resources to support the launch.

-With many stakeholders involved, it would be easy for the project to run off course or for the software to become complex too quickly. However, working in small sprints and aligning on our minimum requirements allowed us to move appropriate items to the icebox so that we could accomplish our goal.

Key Learnings 

– This workstream underscored the significance of agile and proactive decision-making in optimizing long-term operational strategies.

– It also emphasized the criticality of early cross-functional collaboration in achieving seamless project execution. This involvement is critical for accountability and ensuring the right considerations are made before, during, and after launch.