Data Strategy
Figure out what you actually need before spending money. A roadmap grounded in implementation reality, not vendor wishlists.
The Problem
Why most data strategies fail
Most data strategies are written by people who've never implemented them. They recommend tools based on trends, not fit. They promise timelines that ignore reality. They leave before anyone discovers the flaws.
I've implemented dozens of data platforms. I know what breaks at 2am, what vendors oversell, and what "simple" requirements actually cost. You get that experience before you commit to anything.
Deliverables
What you get
Current State Assessment
Where you are today: data sources, existing tools, team capabilities, pain points. No assumptions, actual discovery.
- Data source inventory and quality assessment
- Existing tooling and infrastructure review
- Team skills and capacity evaluation
- Stakeholder interviews and requirements gathering
Architecture Recommendation
What you should build, and what you shouldn't. Vendor-neutral recommendations based on your actual needs, not what's trendy.
- Tool and platform recommendations
- Build vs. buy analysis
- Cost projections (realistic ones)
- Alternative approaches and trade-offs
Prioritized Roadmap
What to build first, second, and what to skip entirely. Sequenced by business value and technical dependencies.
- Quick wins vs. foundational work
- Phased implementation plan
- Resource and skill requirements
- Risk identification and mitigation
Decision Support
Materials to get buy-in from stakeholders, budget approval, and alignment across teams.
- Executive summary and business case
- Technical architecture diagrams
- Vendor comparison matrix
- Q&A and presentation support
My Approach
What makes this different
Implementation-tested
Every recommendation is something I've built myself. I know what's realistic because I've done it.
Cost-conscious
I'll tell you when PostgreSQL is enough. I have no vendor partnerships pushing me toward expensive solutions.
Executable
You get a plan you can actually follow, not a 100-page document that sits in a drawer.
Common Questions
Questions I help answer
"Do we need a data warehouse?"
Maybe. Depends on your data volume, team size, and use cases. Often you don't, at least not yet.
"Snowflake, Databricks, or BigQuery?"
Wrong question. The right question is what problem you're solving and what your team can actually maintain.
"How much should we budget for this?"
I'll give you real numbers based on your requirements, including the hidden costs vendors don't mention.
"Should we hire or outsource?"
Depends on what you're building and how core it is to your business. I'll help you think through the trade-offs.
Process
How it works
Discovery
Interviews, data audit, existing system review
Analysis
Requirements synthesis, architecture options, cost modeling
Recommendation
Strategy presentation, roadmap, decision support
Handoff
Documentation, Q&A, optional implementation support
Not sure where to start?
That's exactly what a data strategy engagement is for. Let's talk.