Most companies that abandoned a BI tool didn't have a tool problem. They had four systems that disagreed, no agreed definition of "revenue," and an analyst who became a bottleneck. We build the foundation, a warehouse, a modelled metric layer, and definitions everyone accepts, so the tool on top becomes almost incidental.
No reseller commissions · Open-source stack by default · You own the warehouse, the models, and the code
Can't answer: Anything that spans two systems, or anything about last year
Can't answer: Anything the tool's data model doesn't already contain
Can't answer: Anything nobody thought of when the dashboard was specified
Can't answer: Fewer and fewer things, and new questions cost hours, not weeks
Most teams calling us are on rung three.
If you're on rung one or two, this page probably isn't what you need yet. Section three explains why.
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The symptom: The numbers live in exports, and reporting means someone rebuilding a sheet on Monday.
The verdict: Not yet, and probably not for a while. The bottleneck isn't analysis, it's that the source data isn't reliably captured anywhere. Fix that first. Usually the honest answer is a better process, an internal tool where the work actually gets recorded, or automation that stops the copy-paste. A warehouse built on data nobody maintains will publish the mess faster and more expensively.
The symptom: Your CRM reports on the CRM, your store reports on the store, and any question spanning both needs a human.
The verdict: Not yet, buy a dashboard, not a BI programme. If you have three or four specific questions and a known audience, one custom dashboard on a light pipeline answers them for a fraction of what BI costs and takes weeks, not months. Come back when you've built the third dashboard and noticed you're rebuilding the same joins each time.
The symptom: Every new question is a ticket. Two departments' dashboards disagree and nobody can adjudicate. Someone maintains six pipelines that each do a slightly different version of the same transformation.
The verdict: Yes, this is exactly the moment. The economics have flipped. You're now paying more to maintain fragmented pipelines than a shared foundation would cost, and the fifth, tenth, and twentieth question become nearly free once the modelling is done once.
The symptom: It exists, it's stale or contested, and people have quietly gone back to their own extracts.
The verdict: Yes, but it's a rescue, not a build. We'll audit what's there and tell you honestly whether to fix the modelling and governance or restart. It's usually fix, a sound warehouse with poor definitions and no ownership is a much smaller job than it looks.
BI sold to a company on rung two is how BI got its reputation. The tool wasn't the problem and the next tool won't be either.
One place where data from every system lands and stays, modelled properly. We'll pick whichever engine is cheapest for your volume, because we earn nothing on the choice. Building the pipelines that land it there is its own scope, see data warehousing & ETL.
Best for: everyone at rung three or above. This is the foundation the rest sits on.
Every metric defined once, in version-controlled code, with the definition visible to anyone reading a number. “Active customer,” “net revenue,” “churn,” agreed across departments and enforced by the system rather than by memory. Typically dbt or an equivalent.
Best for: any company where two teams have ever disagreed about a number in a meeting. So, any company.
A BI tool your team can actually use, Metabase, Superset, Looker Studio, or Power BI if you already own the licences, connected to the modelled layer so people explore safely. Every answer comes from the same definitions, so self-serve doesn't mean self-invented.
Best for: teams where an analyst has become a queue.
Cohorts, retention, margin by segment, inventory ageing, the questions that need modelling rather than a chart, not a forecast. Built into the warehouse as reusable models, not delivered as a one-off slide. Estimating what happens next rather than describing what already did, forecasting, lifetime value, churn risk, is predictive analytics, a different scope with a different error bar.
Best for: teams who've hit the ceiling of what a dashboard can express.
Access control, data lineage, documented definitions, freshness monitoring, and a written answer to “who owns this metric.” Unglamorous, and it is the difference between a warehouse that's trusted in year two and one that isn't.
Best for: everyone, and it's the piece most often cut from a quote. We include it.
Getting data into the warehouse on a schedule, from every source, reliably, is data warehousing & ETL, and it's priced as its own scope. If your data is already centralised and clean, say so on the call, the project gets substantially smaller and we'll tell you so before you commit.
Abandoned BI tools have a small number of causes and they repeat almost exactly.
Licences signed, a consultant connects it to four sources, and it surfaces four systems' worth of disagreement with no layer to reconcile them. People conclude the tool is wrong. The tool was fine.
The decision that prevents it: foundation first, tool last. We build the warehouse and metric layer before recommending a BI tool, and by then the choice matters far less than anyone expected, which is why we can be indifferent about it.
Finance and sales define revenue differently, both legitimately. The dashboard picks one, the other team rejects it, and both go back to their own extracts. The disagreement was never technical, but it kills the system just the same.
The decision that prevents it: definitions are agreed in a workshop before modelling, written into version-controlled code, and displayed next to every number. Contested definitions get both versions, named distinctly, rather than a silent winner.
Self-serve was the promise; in practice the model was too complex to explore safely, so everything routed through one person. They became a queue, the queue got long, and people stopped asking.
The decision that prevents it: model for the question, not for the source. Tables shaped around how the business thinks, named in business language, with the joins already resolved. Enablement is scoped in, we train your team on your own data, not on a demo dataset.
A pipeline broke. Charts kept rendering the last successful load. Someone spotted a number that hadn't moved in three weeks, and from that day nothing in the warehouse was trusted again, including everything that was still correct.
The decision that prevents it: freshness monitoring and alerting on every model from day one, plus a visible last-updated timestamp on every dashboard. Trust is lost once and regained slowly; the monitoring costs almost nothing by comparison.
Three of these four are decisions made before any code is written. None of them are about which BI tool you chose.
That's usually settled in one call. Tell us what questions you're trying to answer and how many places the data lives, and we'll tell you which rung you're on, including if the honest answer is that you're not ready and shouldn't spend anything yet.
Book a discovery callMetabase, Superset, and dbt cover what most mid-market teams need at a fraction of per-seat BI licensing. We have no reseller relationship with any vendor, so nothing about our recommendation is commission-driven. If you already own Power BI licences, we'll build to those instead, that's usually the cheapest correct answer.
Postgres handles far more than people expect and costs a fraction of a cloud warehouse. We'll start there unless your volume genuinely requires BigQuery or ClickHouse, and we'll tell you where that threshold is for your data rather than defaulting upward.
Metrics live as code, reviewed like code, with history. When a definition changes you can see who changed it, when, and why, and every number that used it changes with it.
Tables shaped around how the business thinks, not around how the source system stores. Named in business language, joins pre-resolved, so exploring doesn't require knowing your CRM's schema.
Every model monitored, every dashboard stamped with when its data last loaded. A stale number that announces itself is survivable; a stale number that doesn't is what kills trust permanently.
Standard tools, documented, in your repository, running in your accounts. Your own analyst or a future hire can pick it up. Nothing proprietary and nothing requiring us specifically.
If what you actually want is to ask questions of your data in plain language, that's closer to a RAG chatbot over a modelled layer than a BI tool, and it only works if the modelling underneath is right first. Where a step needs genuine judgement rather than a query, that's an AI agent.
Not “what data do you have”, what decisions are being made badly for lack of a number. We work backwards from decisions to metrics to sources, and we settle the contested definitions in the room. You get the written definition set whether or not you hire us; for many teams it's the first time these exist on paper.
We connect to each system and check what's genuinely available, how clean it is, and how far back history goes. Then a written scope, a fixed price, and a date. If a source can't support a metric you need, you find out here.
Warehouse stood up, the highest-value three or four metrics modelled and live. Deliberately early: you validate against numbers you already know are right, and you see value before the bulk of the spend. Written update every Friday plus a short Loom walkthrough.
The rest of the metric layer, the BI tool connected, access and governance configured, and your team trained on your own data. Enablement is scoped in, not an optional add-on, an untrained team is the shelfware mechanism.
Freshness monitoring and alerting, source API changes handled, new metrics added as the questions change, and documentation kept current. A named engineer stays on it. If you hire an internal analyst later, we hand over properly rather than leaving them to reverse-engineer it.
We hold no reseller or partner commission on any tool listed here. If Looker Studio on a Postgres warehouse does what you need for a fraction of the cost, that's what we'll recommend, and we'll say so before you've spent anything.
You need to answer questions you haven't thought of yet, repeatedly, without an analyst in the loop.
You want to know what's likely to happen next, not just what's true now, churn, demand, revenue, with an error bar attached.
You need to see a known set of things for a known audience. If you can list your questions on one hand, buy a dashboard, not a warehouse.
The financial and operational system of record. BI reads from it; it isn't BI.
The stock number being right across channels. An accuracy problem, not an analysis one.
A place work gets recorded. If your data is bad because nobody enters it consistently, this comes first.
Something happens without anyone opening anything. If a number should trigger an action rather than be looked at, automate it.
The engineering underneath all of these. If you're evaluating who can build custom software at all, not just this shape of it.
Honest answer: a good share of BI enquiries are one dashboard and a fixed definition of two metrics. That's weeks and a fraction of the cost, and we'd rather scope it that way in week one than sell a warehouse to a company that needs a chart.
Fixed price, quoted in writing before we start. No hourly billing. In a category built on day rates and open-ended discovery, this is the point.
Warehouse, ingestion from your core sources, the metric layer for your core definitions, one BI tool connected, and your team trained on it. Includes the first month of monitoring
Scoped and quoted after the source audit
Good for: Rung three, dashboards exist and every new question has become a project
Everything above plus the modelling work that needs actual analysis: cohorts, retention, margin by segment, inventory ageing, built as reusable models rather than one-off reports. Forecasting and lifetime value prediction are scoped separately, under predictive analytics
Priced once we know the analysis you need
Good for: Teams who've hit what a dashboard can express and need the questions modelled, not charted
Freshness monitoring, source changes handled, new metrics and models as questions evolve, and a set amount of analysis work each month
A monthly plan sized to your data
Good for: Teams without an internal data hire yet, and a cheaper way to find out what that hire would need to be
Every build includes monitoring for the first month. After that it's a monthly fee, see what that covers, and you can stop any time, you keep the warehouse, the models, the definitions, and the documentation either way. If you hire an analyst, we hand over to them properly.
These are ingestion and pipeline projects, the layer underneath a BI foundation, not warehouse-and-metrics case studies. We'd rather say that plainly than imply otherwise.
Book a 30-minute call. We'll work out which rung you're on, tell you honestly whether you need a warehouse or just a dashboard, and give you a fixed price if a foundation is genuinely the right answer.
