Ways to Work Together
The starting point depends on what is already clear. We may assess one decision or workflow, evaluate an approach, or lead a defined piece of work.
We agree the outcome, scope, responsibilities and timing before starting. Ongoing leadership is available when the business needs it.
When the cause or approach is unclear
Focused assessment
A close look at one consequential decision, a recurring workflow, an AI-readiness question, or a proposed approach. The scope matches the question, so it does not have to be a company-wide review.
What you provide
A sponsor who owns the question, time with the people closest to the work, and access to the reporting, data, or workflow involved.
What you leave with
- The main constraints holding the work back
- The realistic options and the tradeoffs between them
- A recommended first step
- Who should own it
- Criteria for judging whether the result is useful
When the priority is clear
Alignment & execution
For a sound existing plan or a defined priority that needs to move. A prior Driftless assessment is not required. How long it runs depends on the work and is agreed at the start.
What this can include
- Agreed definitions for the priority business metrics
- Named owners and clear decision rights
- A prioritized business domain, workflow, or use case
- A semantic-layer, reporting, or data-foundation plan where appropriate
- A maintained roadmap and sequence of work
- An operating cadence for decisions, progress, and accountability
I lead priorities, business decisions, ownership, sequencing, coordination, and accountability. Implementation responsibilities are scoped explicitly with your internal teams or partners before work begins.
When the work needs an ongoing owner
Fractional data leadership
Part-time, executive-level responsibility for data priorities, cross-functional decisions, and delivery accountability. I can work alongside an existing data leader or executive team, or carry that responsibility where the role is not filled. Scope and cadence depend on the mandate.
What this can include
- A regular cadence with the executive sponsor
- Maintained priorities and roadmap
- Documented decisions, owners, and tradeoffs
- Coordination across executives, data teams, and implementation partners
- Removal of cross-functional blockers
- Executive readouts and delivery accountability
- Team development and durable knowledge transfer
Semantic layers and AI
Why a semantic layer matters for AI
If you want AI to answer questions about business performance from structured data, I recommend building on a governed semantic layer. Here is what that means, why it works, and what it takes.
What it is
One shared model of how the business works
A semantic layer holds your business definitions, calculations, relationships, and context in one place. Connected analytics and AI tools can use it instead of each keeping its own version.
Why raw tables are not enough
Access is not the same as meaning
Connecting AI to tables shows it what data exists. It does not tell the tool what the business means by a customer, revenue, or retention, which records to leave out, or which period to compare. Each tool, and each prompt, ends up working it out on its own.
Why I recommend it
Business logic is defined once and reused
Definitions are set deliberately and reviewed by the people who own them. Every connected dashboard and AI tool works from that same logic, and when a definition changes, it changes in one place.
What it does not do
A shared starting point, not a guarantee
It does not fix poor source data, cover every AI use case, or make answers error-free. It gives people and AI tools the same reviewed starting point, which makes answers easier to check. If AI is not yet answering questions about business performance, you may not need one now.
Example: retention
- Agree which customers count, for example whether trials and internal accounts are included.
- Agree how retention is calculated.
- Agree which period is being compared, such as this quarter against the same quarter last year.
- Record those rules once in the semantic layer, with a named owner.
- Connected dashboards and AI assistants reference the same definitions, so a question about retention starts from the agreed rules.
What makes it useful in practice
- Appropriate source data that is complete and current enough for the decision.
- Agreed definitions settled by the business before they are built.
- Accountable owners who approve changes to each definition.
- Access controls so people and AI tools see only what they should.
- Evaluation that tests answers against known results before people rely on them.
- Adoption by the dashboards, spreadsheets, and AI tools people actually use.
Where I come in: choosing the first useful business application, aligning definitions and owners, evaluating approaches, sequencing the work, and leading adoption. Technical implementation responsibilities are agreed with you and the relevant partners.
Other decisions and workflows I help with
These are common starting points, not standalone products. Any of them can be scoped as an assessment, a defined piece of work, or part of ongoing leadership.
Trusted metrics & ownership
Can leadership rely on the numbers it uses?
Define the numbers leadership uses to run the company, identify their authoritative sources, assign owners, and establish how definitions are approved and changed.
Decision systems & executive reporting
Do the reports point to a next action?
Connect dashboards, reporting, and analysis to the decisions, operating rhythms, and accountability they are meant to support.
Recurring workflows & handoffs
Why does this workflow still need so much manual work?
Find where matching, correction, or handoffs break down between teams and systems, then agree what should change and who owns the fix.
Data operating model & prioritization
What should the data team work on first?
Move the data function from an expanding request queue toward a focused agenda tied to the company's highest-value decisions.
When I'm most useful
If the work is already clearly defined and simply needs to be built, a specialized implementation partner may be the right fit.
I step in when the business outcome matters but the company still needs to determine what to build, what to prioritize, who should own it, and how to align the people involved.
Not sure where you'd start?
Tell me what you are working on. If a different starting point makes more sense, or I am not the right fit, I will say so.