Every project has a moment where someone says: “I wish we’d known this two months ago.” That moment triggers rework — rework of the concept, the design, sometimes vast parts of the solution.
A proper Strategy Session at the start eliminates most of that. We establish what’s actually broken, what a good outcome looks like, and what not to build. As my late father used to say: measure twice, cut once.
It’s not a formality. It’s the difference between building the right thing and building the wrong thing very efficiently.
The seven steps — Strategize, Analyze, Design, Program, Implement, Document, Review — aren’t a methodology for its own sake. Each step exists because skipping it creates a predictable failure mode.
Most productivity improvements I deliver exceed 10%. The process is why. It ensures nothing important is missed, the solution fits how your business actually works, and your team can run it without me in the room.
A model that requires constant manual handling is a model that gets resented. People work around it, avoid it, or quietly build their own version that nobody else knows about.
When you automate the repetitive parts — the data transfers, the reconciliations, the reformatting — the person using the model can focus on what actually matters: interpreting the results and making decisions.
That shift from data handler to decision-maker is where satisfaction comes from. The tool stops being a burden and starts being an asset.
A solution built for today’s volume will start groaning under tomorrow’s. I’ve seen it dozens of times: a model that worked perfectly at $5M starts breaking at $15M — not because the business grew, but because the architecture wasn’t designed with growth in mind.
Every solution I build is designed to scale. That means thinking about data structure, user volume, process complexity, and edge cases before writing a single formula — not after.
When was the last time anyone looked under the hood of the models your business relies on?
Most haven’t been audited since they were built. They were built by someone smart, under time pressure, with good intentions. Then they were used. Then they were trusted. Then they were never questioned again.
The person who built them? They’ve either left the company, been promoted to something else, or nobody’s quite sure where they went.
You’re making million-dollar decisions on these models. It’s worth knowing whether the numbers are right.
This one surprises people, but it shouldn’t.
When critical files have no protection — when anyone can overwrite a formula, break a link, or accidentally corrupt a model — the people who maintain those files carry all the risk. They become the human safeguard against disaster. That’s exhausting.
Properly protected, well-structured systems remove that burden. People stop being the last line of defence and start being able to do their actual job. That makes a measurable difference to how long good people stay.
It means the answer is always right. Not approximately right. Not right enough. Right.
Precision in data work means every formula is intentional, every input is validated, every output can be traced back to its source. There are no rounding shortcuts, no hardcoded assumptions buried three layers deep, no “it’s always been like that.”
Transparency follows precision naturally. When a model is built with rigour, anyone can open it, follow the logic, and trust what they see.
When anyone evaluates a business — whether for acquisition, investment, or succession — they look at three things: the product, the team, and the systems.
A business where critical knowledge lives in one person’s head, or in an unlocked spreadsheet nobody fully understands, is a business with a valuation problem.
Documented, structured, automated systems mean the business runs independently of any individual. That’s not just operationally valuable — it’s financially valuable.
Most data errors don’t happen because people are careless. They happen because the tool makes the wrong input too easy.
A well-designed interface constrains what’s possible. Dropdowns instead of free text. Validated fields that reject bad data at the point of entry. Clear visual cues that distinguish inputs from calculations.
The goal is a tool where doing the right thing is easier than doing the wrong thing. When you build it that way, errors drop dramatically — not because people try harder, but because the system stops letting them happen.
Forty years of working inside and alongside businesses at every stage of growth means I’ve seen most problems before. I know which solutions scale and which ones create new problems six months later.
When I’m building a solution for you, I’m not just thinking about the technical output. I’m thinking about how your business actually operates, where the pressure points are, and what a good decision looks like from a commercial perspective — not just a systems perspective.
That context is what separates a tool that works from a tool that gets used.
Most quote templates I’m brought in to review have a silent problem: they give salespeople too much flexibility.
Discounting is easy. Removing a cost line is easy. Overriding a margin threshold is easy. None of it requires malicious intent — it just requires someone eager to close a deal.
A well-engineered quote template sets the boundaries. It allows the flexibility your sales team needs while protecting the floors your business requires. The margin doesn’t erode because the system doesn’t let it.
Most people use Excel for about ten percent of what it’s capable of.
Beneath the surface, Excel is a full programming platform. With VBA and the right architecture, it can automate complex workflows, integrate with external data sources, enforce business rules, generate formatted reports at the push of a button, and run processes that most businesses are currently paying SaaS subscriptions to handle.
The irony is you already own it. You’re already paying for it. It’s just never been built properly.
