Data Engineering · Analytics · Power BI · AI
Your numbers should end the argument, not start one.
Forward Metrics builds the data foundations, models and reporting that give growing businesses one trusted version of the truth — then teaches your team to work far faster with AI.
30 minutes · No pitch deck · US & UK
The chain
Every engagement moves a business along the same four steps — from data nobody quite trusts to decisions nobody argues with.
Scattered across systems, exports and spreadsheets.
Connected, modelled and reconciled. One definition per metric.
Reporting people actually open, built around real questions.
Faster calls, made on numbers nobody disputes.
01 The problem
Most reporting problems aren't reporting problems.
By the time a dashboard is wrong, the damage was done three layers upstream. When we look under the hood of a mid-sized business, we tend to find some version of the following.
Two reports, two answers
Finance says one number, operations says another. The meeting becomes a debate about whose data is right instead of what to do next.
The month-end scramble
Someone loses the first week of every month rebuilding the same board deck by hand, from the same exports, in the same fragile workbook.
Systems that don't talk
CRM, finance, operations and the warehouse each hold part of the picture. The only place they're ever joined together is a spreadsheet.
Dashboards nobody opens
You already bought Power BI. What got built is technically impressive and answers questions nobody in the business was actually asking.
One person holds it all
Reporting works because of one spreadsheet that one person understands. That isn't a system — it's a risk with a vacation policy.
AI is happening anyway
Your analysts are already pasting data into ChatGPT. Nobody has shown them how to do it safely, accurately, or in a way that compounds.
02 What we do
Four capabilities. One continuous chain.
Most firms sell you a single link — a dashboard here, a pipeline there. We build and own the whole chain from source system to decision, or plug cleanly into the part you're missing.
Data Engineering
Get the data out of your systems, into one place, on a schedule, without anyone touching it.
Explore 02 / TrustAnalytics Engineering
Turn raw tables into business logic everyone agrees on — so a metric means one thing everywhere.
Explore 03 / DecisionsBI & Power BI
Reporting built around the decisions you actually make — fast, trusted, and used after week one.
Explore 04 / LeverageAI & Analytics Training
Teach the analysts you already have to do a week's work in a day — safely, and with their judgment intact.
Explore03 Outcomes
What actually changes
These are the things your team should notice within the first few months of working with us.
- 01 The month-end deck builds itself, and lands the same day every month Automation
- 02 One trusted version of every number, agreed once and reused everywhere Semantic model
- 03 Systems that were never designed to talk now reconcile automatically Integration
- 04 Leadership sees performance without asking anyone to prepare it Executive BI
- 05 Teams answer their own questions instead of joining a reporting queue Self-service
- 06 Decisions get made in the meeting, not deferred to "let me check the numbers" Trust
- 07 The business stops depending on one person's spreadsheet Resilience
- 08 Your analysts ship in a day what used to take them a week AI enablement
- 09 Adding the next data source is a small job, not another project Architecture
04 How we work
A short path to something real.
No six-month discovery phase. You should have working, useful output in weeks — and know exactly what it cost before we start.
Discovery call
Thirty minutes on your business, your systems and where the reporting hurts. We'll tell you honestly whether we're the right fit.
Output: a straight answer
Data health check
A fixed-scope review of your sources, models and reporting. We map what exists, what's fragile and what's worth fixing first.
Output: findings, priorities, costed roadmap
Build in increments
We deliver in short cycles against the roadmap, starting with the thing that removes the most manual work. You see progress every week.
Output: working pipelines, models, reports
Handover & enablement
Documentation, training and — if you want it — AI enablement for your team. Our aim is that you need us less over time, not more.
Output: a platform your team owns
05 The difference
We'd rather make your analysts twice as fast than make you twice as dependent.
Every consultancy in this market now has an AI page. Most of them mean "let us build you something with AI in it." We mean something different, and more useful.
The people who know your business best are already on your payroll. They know which customer is a rounding error and which one is a relationship. What they don't have is the leverage — so the analysis that would genuinely move the business sits behind three days of manual SQL and data cleaning.
That gap is what modern AI tooling closes. Used well, an analyst who understands the business can now build things that used to require a developer: transformation logic, working data applications, automated analysis, whole reporting workflows.
This is not "AI will replace your analytics team." It is the opposite. Your team's judgment becomes more valuable, not less, once the mechanical work stops consuming their week. We teach them how, on your data, with your tools — including where AI is confidently wrong and how to catch it.
06 Why Forward Metrics
Small team. Senior people. No handover to juniors.
You work with the people who do the work
There is no account manager, no bench, and no pattern where you're sold by a partner and delivered by somebody two years out of university. The person on your discovery call is the person writing the SQL.
We fix the cause, not the symptom
Anyone can build the dashboard you asked for. If the numbers behind it can't be trusted, we'll say so and fix that first — even when it's a less exciting piece of work than the one you had in mind.
Built for teams without a data department
In practice that usually means one analyst carrying the whole reporting load, or a finance lead doing it alongside their actual job. Everything we build is designed to be run and extended by them, not by a platform team of eight — which is also exactly why making those one or two people faster matters as much as what we build for them.
Fixed scope before we start
You get a defined piece of work with a defined price. If the scope needs to change, that's a conversation we have before the work happens, not a line on an invoice afterwards.
We're probably not for you if…
…you want the cheapest available Power BI contractor, a single dashboard with no interest in what's underneath it, or a partner who won't push back. We'd rather tell you that now than three weeks in.
07 Where to start
The Data Health Check
Most people don't need a proposal. They need someone senior to look at what they've got and tell them the truth about it. That's a fixed-scope engagement, and it's how nearly every relationship here begins.
Fixed scope · Fixed price · Typically 1–2 weeks
Know exactly what you're dealing with before you spend anything on building.
We review your data sources, models and reporting as they stand today, work out where the manual effort and the mistrust are actually coming from, and hand you a prioritized, costed plan. You own the output whether or not you go on to work with us.
- A map of every system your reporting depends on today
- Where your numbers diverge, and why
- The manual work that can be automated first
- An honest read on your existing Power BI estate
- A prioritized roadmap with costs against each stage
- A recommendation you can act on without us
08 Questions
Before you get in touch
Usually, yes — and that's normally the cheaper answer. Failing Power BI estates tend to fail for one of three reasons: the logic in the model doesn't match how your business actually defines things — your version of an active customer, your revenue recognition, your financial calendar — or the refresh is unreliable, or the reports were built around the data that happened to be available rather than the decisions people actually make. All three are fixable in place. We'll tell you if a rebuild genuinely is the better option, but it's not our default recommendation.
Often not. Fabric is excellent and we build on it regularly, but plenty of mid-sized businesses are better served by a well-designed SQL warehouse and a properly built semantic model at a fraction of the cost. We'll recommend the smallest architecture that solves your problem and scales for the next few years — not the one that sounds most impressive.
If you have data in more than one system and someone spending real time each month assembling reports by hand, then yes — that's the situation this work is built for. Most of our clients sit somewhere between 20 and 500 people, with one analyst or none at all, but the size matters far less than the shape of the problem.
If you're smaller, or you're not sure, ask anyway. Sometimes the right answer is a focused week of work rather than a project, and we'd rather point you at that than have you assume you're too small to be worth a conversation.
No. We make them considerably more effective. In most engagements your analyst is our closest collaborator — they know the business, we know the platform. A large part of what we deliver is transferring capability to them, including how to use AI tooling properly, so that the system keeps improving after we've gone.
Engagements are fixed-scope and quoted up front, so you know the number before committing. The Data Health Check is a defined piece of work with its own fixed price; build work is quoted against the roadmap it produces. We'll give you a realistic range on the discovery call rather than making you wait for a document.
It is if you set it up correctly, and that's part of what we teach. There's a real difference between pasting customer records into a consumer chatbot and running AI-assisted analysis inside controlled boundaries. Our training covers what may go where, which tools are appropriate for which class of data, and how to verify output before anyone acts on it.
Next step
Let's find out if there's a problem worth solving.
Thirty minutes, no pitch deck, no obligation. You'll leave with at least one useful observation about your data whether or not we work together.