First 30 Days: Orientation and Assessment

Goal

Build relationships, assess existing resources and processes, and identify immediate priorities.

Key Actions

  1. Understand the Department and University Landscape:
    • Meet with the Vice Chair of Research, Senior Director of Operations, and CFO to understand the department’s mission, vision, and strategic goals.
    • Review the existing structure, policies, and vision for the Data Analytics Core.
    • Identify key research priorities and funded projects requiring immediate support.
    • Familiarize yourself with UW regulations, compliance standards, and funding guidelines.
  2. Build Relationships and Trust:
    • Conduct one-on-one meetings with senior leaders, faculty, and trainees to understand their research needs, expectations, and pain points.
    • Introduce yourself as a collaborative partner who will help advance their research goals.
    • Begin developing a rapport with existing data analysts, if any, and assess their strengths and challenges.
  3. Evaluate Current Resources and Processes:
    • Audit available infrastructure (software, data storage, project management tools, financial tracking, etc.).
    • Assess current project workflows, including how requests are triaged, data are analyzed, and results are communicated.
    • Review past and ongoing projects, including associated outputs (e.g., grants, publications).
  4. Establish Immediate Analytical Priorities:
    • Address any urgent requests for data analysis or consultation.
    • Perform initial assessments for high-priority projects and identify methodological approaches.
    • Provide quick wins (e.g., power/sample size calculations, statistical guidance) to demonstrate value to stakeholders.

Day 31–60: Strategy and Infrastructure Development

Goal

Design processes, set up infrastructure, and create a sustainable operational framework for the Data Analytics Core.

Key Actions

  1. Develop Core Operations:
    • Request Intake Process:
      • Implement a structured request system (e.g., an online form) to capture project details, timelines, and resources required.
      • Assign clear workflows for triaging and prioritizing projects.
    • Core Services Menu:
      • Define available services, including consultations, statistical analyses, grant support, and training.
      • Establish a transparent pricing model for fee-based services to ensure financial sustainability.
  2. Build a Financial Sustainability Plan:
    • Partner with the CFO and Senior Director of Operations to:
      • Review Core funding streams and expenses.
      • Create a financial model to allocate staff time and cover operational costs.
      • Explore departmental or external grants to support the Core’s growth.
  3. Enhance Analytical Capabilities:
    • Identify and address gaps in available software, hardware, or technical tools.
    • Set up best practices for secure data management and reproducible workflows.
    • Provide guidance on complex methods (e.g., multivariable models, bioinformatics approaches).
  4. Recruit and Plan for Growth:
    • Draft a recruiting plan for future master’s-level analysts.
    • Define onboarding processes, including training in key tools (e.g., R, SAS, Python) and Core-specific workflows.
  5. Promote the Data Analytics Core:
    • Organize a launch event or seminar to introduce the Core to faculty and trainees.
    • Create promotional materials (e.g., website, flyers) that outline Core services and success stories.

Day 61–90: Implementation and Growth

Goal

Launch processes, start recruiting, and solidify the Core’s role in the department.

Key Actions

  1. Operationalize Core Processes:
    • Roll out the intake process, services menu, and pricing structure.
    • Monitor incoming requests, allocate resources, and refine workflows as needed.
    • Begin generating reports on project outputs (e.g., number of consultations, time spent).
  2. Expand Collaborative Networks:
    • Reach out to other departments within the School of Medicine to explore cross-department collaborations.
    • Partner with the grant office to support faculty applying for research funding.
  3. Recruit and Train Additional Analysts:
    • Begin hiring for at least one master’s-level data analyst.
    • Develop a training curriculum to onboard new team members efficiently.
  4. Deliver High-Value Outputs:
    • Ensure timely delivery of statistical analyses, grant support, and manuscripts for ongoing projects.
    • Showcase early successes to departmental leadership to build confidence and secure future resources.
  5. Evaluate and Refine:
    • Conduct a self-assessment of the first 90 days, including feedback from faculty and staff.
    • Identify areas for improvement in Core processes and begin implementing changes.

Key Milestones by Day 90:

  • Completed an audit of current resources, projects, and workflows.
  • Implemented intake and workflow processes for data analysis requests.
  • Delivered high-priority projects, earning trust from stakeholders.
  • Launched the Data Analytics Core with a clear services menu and pricing model.
  • Secured initial funding or financial support for long-term sustainability.
  • Started the recruitment process for additional team members.