From noise
to decision.
Most organizations have already spent real money on data. The platforms are in and the reports exist, but people still don't trust the numbers. We find out why, fix what's in the way, and help you get value from what you already have.
Sound familiar?
The issue is rarely the technology. It's usually the strategy, ownership, processes and trust around it.
Three reports, three different numbers.
Every meeting starts with an argument about whose figure is right, so decisions go back to gut feel.
Nobody owns the data.
Definitions live in people's heads. When something breaks, it's unclear who fixes it or who signs off.
The platform is live. The value isn't.
You bought the tools and built the pipelines, but usage is low and the old spreadsheets still run the business.
Reporting runs on heroics.
A few people copy, paste and reconcile every month end. When one of them is away, the numbers are late.
No plan, or one nobody follows.
Data and AI requests pile up with no clear way to decide what matters most or what to stop.
AI pilots that never ship.
The demos looked good. Then Risk, Legal and data quality questions stalled them before production.
If a few of these hit home, you don't need another tool. You need someone to make sense of what you have.
Built around your problem.
There's no standard package. Some clients are just starting and need a strategy. Others have mature platforms and a trust problem. Some need help getting AI through Risk and into production. The work starts where you are.
- ListenTalk to the people who use the data, own it and pay for it. Find out what's really going on.
- DiagnoseLook across strategy, governance, data, technology, people and AI to see what's holding things back.
- Shape the workAgree what to fix first and how involved you want us, from advice to hands-on leadership.
What we do.
We sit on your side of the table. Sometimes that means advice and a plan. Sometimes it means leading the work until your own team can carry it.
Data and AI diagnostic
An honest, independent read on where you are and what's in the way.
- Maturity assessment
- Stakeholder interviews
- Prioritized findings
Data and AI strategy
A roadmap tied to the decisions and outcomes the business cares about.
- Vision and priorities
- Use case selection
- Roadmap and business case
Governance and data quality
Clear owners, shared definitions and numbers people stop arguing about.
- Ownership and stewardship
- Definitions and metric catalogue
- Quality checks and lineage
Value from what you've bought
Make the platforms and reports you already paid for earn their keep.
- Platform and tool review
- Report clean-up
- Adoption and usage
Operating model and team
The roles, processes and ways of working that let data work day to day.
- Org design and roles
- Intake and prioritization
- Hiring and onboarding
AI readiness and adoption
From pilot to production, with Risk, Legal and Finance on board.
- Readiness and use cases
- AI governance and guardrails
- Production rollout
Data and AI products
Run data like a product, with real users, a roadmap and a reason to exist.
- Product strategy
- Requirements and delivery
- Client-facing analytics
Embedded data leadership
A senior data leader at your leadership table, setting direction and running the work.
- Direction and decisions
- Program and vendor oversight
- Bridge to a permanent hire
Vendor agnostic.
We don't resell software, take referral fees or have a preferred vendor. The problem decides the tool.
Work with what you've got.
You've paid for it and your team knows it. We start with the stack you have and only suggest a change when the case for it is clear.
Find the right tool for the job.
We test the options against your real use cases, data volumes, team skills and budget, then recommend one in writing.
Where we work.
Energy
Producers and midstream companies that have outgrown spreadsheets and need operations data they can rely on.
- Production and operations data
- Field and SCADA data to decisions
- Board and regulator reporting
Financial services
Wealth managers, credit unions, lenders and fintechs, where every data decision goes through Risk and Legal.
- Client and advisor analytics
- Data and AI governance
- Regulated reporting and lineage
Investors
Private equity, venture and corporate development teams who need to know what a company's data is really worth.
- Data and AI due diligence
- Post-close priorities
- Portfolio data leadership
Three rules.
Decisions before capability
Start with the decision you need to make better. Not the tool someone already sold you.
Trust is the gate
If Finance, Risk and Legal don't trust the numbers, nothing ships. They're in from the start.
Earn the right to scale
Prove value on one thing before the big spend. Then the next one. How that works
Earn the right to scale.
Many data and AI programs buy the platform first and look for the value later. The bill arrives on schedule. The value usually doesn't.
We work the other way. Each step has to prove itself before the next one gets funded. If a step doesn't pass, you've lost weeks, not a year.
- Pick one decision
A real decision, made by a named owner, that happens often enough to matter. We write down how it's made today and what better would be worth.
- Prove it on the smallest stack that works
Often that's the tools you already pay for. We only add platform when the problem actually needs it.
- Get it into use
Used by the people who make the decision, signed off by Finance, Risk and Legal. A demo doesn't count.
- Measure against the baseline
Faster, cheaper or more accurate than before, in numbers the business agrees with. If it isn't, we say so and change course.
- Then scale
Now the spend, the hires and the governance are justified by something working. The next one is cheaper because the foundations are already there.
Joel McInnis
Joel has spent his career in the gap between what a business wants from its data and what it actually gets. He led a client-facing analytics platform at a global wealth manager, with a team of more than 15 across analytics, BI, UX and engineering, working closely with Finance, Legal and Risk on data governance, security and AI. Before that he built operations data tools for Western Canadian producers and ran analytics in consumer goods.
Master of Management Analytics, Queen's. Based in Calgary.
Net new assets booked after he turned scattered records into one automated feed
Start with a conversation.
Tell us what isn't working. We'll listen, ask a lot of questions, and tell you plainly whether and how we can help.
