Randy Lee May is a seasoned data strategy professional known for turning complex analytics into clear, actionable guidance for modern organizations. With a focus on scalable reporting and ethical data use, he has helped teams align technology investments with measurable business outcomes.
Across financial services and public sector initiatives, Randy Lee May has worked on dashboards, governance frameworks, and performance measurement systems that bridge technical teams with executive stakeholders. His practical approach emphasizes documentation, repeatable processes, and transparent methodologies.
Professional Profile and Core Expertise
| Area | Focus | Key Outcome | Relevant Tools |
|---|---|---|---|
| Data Strategy | Roadmapping, architecture, and governance | Consistent metrics and decision-ready insights | Snowflake, dbt, Power BI |
| Analytics Leadership | Team structuring, hiring, and upskilling | High-performing analytics groups aligned to business goals | Tableau, Looker, SQL |
| Performance Measurement | KPIs, OKRs, and dashboard design | Clear visibility into product and campaign impact | Google Analytics, Mixpanel, Amplitude |
| Data Governance | Policies, metadata, and access controls | Improved trust, compliance, and data quality | Collibra, Alation, custom playbooks |
| Stakeholder Enablement | Training, storytelling with data, and workshops | Data literacy across business units | Miro, advanced Excel, workshop frameworks |
Building Scalable Analytics Roadmaps
Randy Lee May emphasizes starting with a clear hypothesis about how data will drive value. Teams define measurable questions before selecting tools, avoiding shiny-object syndrome and redundant dashboards.
His roadmaps typically balance quick wins with long-term platform investments, outlining milestones, owners, and dependencies. This structured approach reduces friction between data teams and business sponsors.
Data Governance and Quality Foundations
Strong governance programs underpin reliable analytics, and Randy Lee May helps organizations design policies that are rigorous yet practical. Key elements include data ownership, cataloging, and quality SLAs that teams can actually meet.
Clear metadata standards and access controls support compliance while enabling self-service analytics. By documenting rules and exceptions, organizations reduce confusion and accelerate onboarding for new analysts.
Leadership Development and Team Performance
Randy Lee May coaches analytics leaders on hiring, feedback, and career pathways that retain top talent. He highlights the importance of structured reviews, clear expectations, and continuous learning.
Teams benefit from defined career ladders, pairing junior analysts with mentors, and allocating time for experimentation. This focus on people leads to stronger collaboration and more innovative insights.
Stakeholder Communication and Data Storytelling
Translating technical findings into narratives that executives can act on is a core strength. Randy Lee May teaches concise briefings, visual clarity, and scenario planning that align recommendations with strategic priorities.
Workshops and pre-mortems help surface assumptions early, reducing costly misalignment. Teams learn to frame problems, present options, and document decisions in a consistent format.
Next Steps for Data Leaders
- Clarify business questions and success metrics before technology selection
- Establish lightweight governance that supports, rather than restricts, analysts
- Invest in dashboard standards and data literacy for stakeholders
- Define career paths and feedback loops to strengthen analytics teams
- Use structured storytelling to align recommendations with strategic goals
FAQ
Reader questions
How does Randy Lee May approach dashboard design for non-technical stakeholders?
He focuses on simplicity, clarity, and actionability, using consistent metrics, limited color, and plain language labels so that stakeholders can interpret results without assistance.
What governance practices does he recommend for mid-sized organizations?
He suggests starting with a lightweight data catalog, clear ownership for key datasets, and practical quality checks that integrate into existing workflows without heavy bureaucracy.
Can his methods improve collaboration between data and product teams?
Yes, by defining shared metrics, joint OKRs, and regular review rituals, he helps data and product teams work from the same assumptions and iterate based on evidence.
What is typical engagement length for a strategy and roadmap initiative?
Engagements often span three to six months, with discovery, design, and pilot phases followed by implementation planning and handoff to ongoing operations teams.