What this is
Data Strategy & System Design is a focused engagement to assess and design how your company’s data ecosystem supports its recurring decisions and operational processes.
Rather than starting with dashboards or isolated KPIs, this work begins with how the business operates — and ensures that the data system, ownership model, and infrastructure properly support it.
The outcome is a clear, pragmatic data strategy and execution roadmap, grounded in real processes and constraints.
The problem it solves
Many organizations have data tools, dashboards, and teams — yet still experience friction:
- Decisions are made without consistent data support
- Ownership of metrics is unclear
- Reporting is ad hoc and constantly changing
- Definitions differ across teams
- No clear direction for infrastructure evolution or prioritization
The issue is rarely effort or intent.
It is the absence of a coherent decision-support system and data strategy.
What this includes
This engagement typically involves:
- Analysis of recurring business processes and decision points
- Identification of critical decision-support gaps
- Review of existing metrics and definitions
- Clarification of ownership and accountability
- Assessment of current data infrastructure and tooling
- Identification of architectural constraints or risks
- Alignment between business priorities and technical investment decisions
- Prioritized roadmap for improvement
Scope may extend beyond GTM into Product, Finance, or Operations — depending on organizational needs.
What you get
By the end of the engagement, you will have:
- A documented decision-support map aligned to core business processes
- Clear ownership model for key data domains
- Identified data and infrastructure gaps
- Defined metric alignment principles
- A prioritized, phased roadmap
- Recommended direction for data architecture evolution
What makes this different
- Grounded in hands-on analytics engineering and revenue operations experience
- Balances business process understanding with technical feasibility
- Avoids abstract strategy without execution realism
- Focused on system design, not slide decks
Typical outcomes
Organizations that complete this engagement typically gain:
- Clear understanding of how data supports core processes
- Realistic, prioritized roadmap for closing data gaps
- Improved clarity regarding data ownership and responsibility
- Better alignment between data teams and business functions
- Stronger foundation for scaling analytics capabilities
Most importantly, the business gains a data system designed intentionally — not organically grown by accident.
How to get started
If you are unsure whether a foundational data strategy review is necessary, the first step is a short discovery conversation.
The goal is to assess:
- Where structural friction exists
- Whether the current data system supports growth
- If a clearer roadmap would reduce risk and inefficiency