Data & Analytics
Business Intelligence Services
Dashboards, reports and KPIs that leaders use to decide, built on the layer beneath them: data consolidated from every source, modelled cleanly, and turned into a single set of numbers everyone can trust. Because a slow or wrong dashboard is almost always a data problem wearing a chart.
What Business Intelligence means in practice
Who it’s for: Leaders who need one trusted view of how the business is doing, and are tired of conflicting numbers, spreadsheet chaos and reports that take days to produce and change nothing.
Business intelligence is meant to answer a simple-sounding question: how is the business doing, right now, in numbers a leader can act on. In practice most organisations cannot answer it cleanly. The sales figure in one report disagrees with the sales figure in another; a KPI on a dashboard was accurate last quarter and nobody is quite sure whether it still is; a management meeting spends its first twenty minutes arguing about whose spreadsheet is right instead of deciding anything. BI, done properly, ends that argument, not by building a prettier chart, but by building the layer underneath the chart that makes the numbers trustworthy in the first place.
That layer is where the real work lives. A dashboard is the last five per cent; the ninety-five per cent beneath it is consolidating data from the systems where it actually lives (the CRM, the finance system, the operational database, the inevitable spreadsheets), and modelling it into a clean, consistent shape where a metric means one thing and only one thing. On top of that we build self-service reporting in the tool that fits you: Power BI, Tableau or Looker. The tool matters far less than most vendors would have you believe. A world-class visualisation tool sitting on top of an incoherent data model produces world-class-looking numbers that are wrong, and that is worse than no dashboard at all, because people trust it.
We are blunt about how BI fails, because it fails often. It fails when it produces vanity dashboards: screens full of metrics that look impressive, get admired in a launch meeting, and change no decision anyone makes. It fails when two reports disagree because there is no single source of truth, and the disagreement quietly teaches everyone to distrust all of it. The value of business intelligence is never the dashboard; it is the decision the dashboard changes. And a decision is only as good as the number behind it, which is why we start with the data model and the pipeline, and treat the visualisation as the easy part it actually is.
What you get
- Your data sources consolidated. CRM, finance, operational systems and spreadsheets pulled into one place rather than compared by hand
- A clean, documented data model where every metric is defined once, so the same number means the same thing in every report
- A single source of truth for your KPIs, with the definition of each written down and agreed rather than assumed
- Self-service dashboards in the tool that fits you (Power BI, Tableau or Looker), that people can explore, not just stare at
- Reporting that refreshes reliably on a schedule, so nobody is rebuilding the same spreadsheet by hand every Monday
- A ruthless focus on the handful of metrics that actually drive decisions, and the discipline to leave the vanity ones off
- Documentation of where every figure comes from, so when a number is questioned there is an answer rather than a shrug
What Business Intelligence does for you
Faster, more confident decisions
When leaders trust the numbers in front of them, they decide quickly instead of hedging, re-checking or deferring. The value of BI is measured in the decisions it unblocks. A stocking call made on Monday instead of Thursday, a hiring decision made on current data rather than last month’s, not in the number of charts on a screen.
An end to the numbers argument
The most corrosive thing about conflicting reports is not the wasted minutes reconciling them; it is that people quietly stop trusting any of the data. A single source of truth, with each metric defined once and documented, removes the argument at its root, so the conversation moves from "is this right" to "what do we do about it".
Hours a week returned to your team
Manual reporting is a tax paid every week by someone capable of better work: exporting, copying, reconciling and formatting figures that are stale by the time they land. Automated, reliable refresh gives that time back and removes the errors that creep in whenever a human is copying numbers between spreadsheets under deadline.
Why teams choose us for Business Intelligence
- We fix the data problem, not just the chart, because "our dashboards are wrong or slow" is almost always a data model or pipeline problem, and dressing it up in a better visual only hides it
- Senior engineers do the work end to end; the person modelling your data and defining your KPIs is the one who understands both the SQL underneath and the decision on top, not a junior handed a spec
- We are tool-agnostic and honest about it. Power BI, Tableau or Looker chosen on your stack, budget and team, not on which one we happen to resell
- We refuse to build vanity dashboards; if a metric will not change a decision, we would rather leave it off than pad a screen with numbers that make it look busy and teach people to ignore it
What Business Intelligence includes
The concrete pieces of work this covers, scoped to what your problem actually needs.
Data consolidation
Bringing together the data that currently lives in separate systems (CRM, finance, operational databases, the spreadsheets nobody admits to), into one place where it can be reported on coherently, instead of being stitched together by hand each time someone asks a question.
Data modelling for BI
Shaping the consolidated data into a clean model built for analysis and reporting (facts, dimensions and clear relationships), so that a metric is calculated one way, in one place. This is the single biggest determinant of whether your dashboards are fast, consistent and trustworthy, and it is where most BI quietly goes wrong.
KPI definition and a single source of truth
Agreeing what each key number actually means, how revenue is recognised, what counts as an active customer, when a lead becomes qualified, and encoding that definition once so every report inherits it. Most "conflicting numbers" problems are really undefined-metric problems, and this is how they get solved.
Dashboard and report building
Building the dashboards and reports themselves in Power BI, Tableau or Looker, designed around the decisions they support, laid out so the important thing is obvious, and interactive enough that a manager can answer their own follow-up question without waiting on the data team.
Self-service reporting
Setting your teams up to answer their own questions safely (governed datasets, sanctioned metrics and the training to use them), so BI scales beyond a bottleneck of a few analysts, without descending into everyone building their own contradictory version of the truth.
Automated, scheduled refresh
Wiring the reporting to refresh on a reliable schedule from the source systems, with monitoring so a failed load is noticed and fixed rather than silently serving yesterday’s half-loaded numbers as if they were today’s.
Where it fits
An executive dashboard leaders actually open
A management team wants one view of how the business is performing (cash, sales, pipeline, operations), that they trust enough to run meetings from. We agree the handful of KPIs that matter, define each one precisely, model the data behind them and build a dashboard that answers the questions leadership actually asks, rather than a decorative screen that gets shown once and forgotten.
Ending conflicting sales and finance numbers
Sales reports one revenue figure, finance reports another, and both are technically defensible because they are calculated differently on different data. We consolidate the sources, agree a single definition of the metric, and build the reporting on top of one model, so the two functions finally see the same number and can talk about the business instead of reconciling spreadsheets.
Replacing a fragile spreadsheet reporting process
A critical weekly or monthly report is assembled by hand in a chain of spreadsheets that only one person understands, breaks when they are on leave, and is always slightly out of date. We rebuild it as an automated, modelled, self-refreshing dashboard, removing the key-person risk, the manual errors and the hours it consumes every cycle.
Self-service reporting for a growing team
A company has outgrown the point where a couple of analysts can field every data question, and the queue has become a bottleneck. We build governed, well-modelled datasets and set teams up to explore them safely in their BI tool of choice, so people answer their own routine questions without everyone inventing their own conflicting metrics along the way.
How we approach Business Intelligence
We start from the decisions, not the dashboard. Before we design a single chart we want to know what you are actually trying to decide, which numbers, if you could trust them, would change what you do about pricing, staffing, stock, cash or growth. That focus is what separates a dashboard people use from a wall of metrics people ignore, and it is where we push back hardest on a brief that asks for "everything on one screen". Everything on one screen is how nothing gets decided.
Then we build from the bottom up, because that is where the trust comes from. We consolidate the sources, model the data so each metric is defined exactly once, and only then put a visualisation on top. When a client says their dashboards are wrong or slow, the cause is almost never the dashboard: it is a data model that lets the same metric be calculated three different ways, or a pipeline that quietly half-loaded last night. We fix it where it actually breaks, which is underneath.
How we deliver a business intelligence project
We start with the decisions and the metrics, not the tool. We sit with the people who will use the reports and work out what they are actually trying to decide, which numbers would change those decisions, and (crucially), exactly what each of those numbers means. This is where we agree KPI definitions explicitly rather than leaving them to be assumed, because an unagreed definition is the seed of every future "why do these two reports disagree" conversation. It is the least technical part of the work and often the most valuable.
Then we do the unglamorous majority of the engagement: getting to the data and modelling it. We map where each figure lives, consolidate the sources into one place, and build a clean data model where every metric is defined once and calculated one way. This is the part that determines whether the finished dashboards are fast, consistent and trusted, and it is the part that vanity-driven BI skips, which is precisely why so much BI is fast to demo and slow to trust.
Only once the model is sound do we build the dashboards and reports on top, in Power BI, Tableau or Looker as fits you, designed around the decisions we identified at the start. We set up automated refresh, wire in monitoring so a broken load is caught rather than silently served, and hand over documentation of what every number means and where it comes from. We would rather ship a small dashboard people trust and use than a large one they admire once and quietly abandon.
Why a BI tool is only as good as the model and pipeline beneath it
The single most important thing to understand about business intelligence is that the tool on top is the easy part, and almost every serious BI problem is a data problem in disguise. When a client tells us their dashboards are wrong, the cause is virtually never the visualisation: it is a data model that permits the same metric to be computed three different ways, a join that quietly duplicates rows, or a definition that two teams interpreted differently. When they tell us their dashboards are slow, it is almost always a model that makes the tool do heavy work at view time that should have been done once, upstream, in the pipeline.
So the architecture we care about sits underneath the dashboard. Data is extracted from the source systems on a schedule, consolidated, cleaned into a consistent shape, and loaded into a model built for analysis, typically a warehouse-style layer with well-formed fact and dimension tables and clearly defined relationships. Metrics are defined in that layer, once, so every report inherits the same maths rather than each analyst re-deriving revenue in their own way. The BI tool then reads a clean, pre-modelled dataset and its job becomes simple: present numbers that are already correct and already fast.
This is why we push back when a project is framed as "we just need some dashboards". You can build dashboards on a mess, and they will demo beautifully and fall apart the first time someone asks why two of them disagree. Getting the model and pipeline right first is not gold-plating; it is the difference between BI that people come to depend on and BI that quietly loses their trust and gets abandoned within a year. The dashboard is the last thing we build because it is the least of the work.
Governance, row-level access and who can see which numbers
Business intelligence concentrates an organisation’s most sensitive figures (finance, payroll, customer, commercial), onto screens that are meant to be widely seen, which makes access control a design concern from the outset rather than a setting toggled at the end. We work out early who should see what: which teams see company-wide numbers, which see only their own region or department, and which figures are restricted to a small group. Getting this wrong in either direction is costly: too open and you leak sensitive commercial data, too closed and the self-service you were trying to build never materialises.
The BI platforms we work with (Power BI, Tableau and Looker), all support row-level security, where the same dashboard shows a manager only their own team’s data while showing a director the whole picture, driven by who is logged in. We design and implement these rules deliberately, tied to your identity provider so access follows your existing joiners-and-leavers process rather than becoming a separate list someone forgets to update. A governed dataset with enforced access is what makes it safe to give people self-service in the first place.
Governance is broader than access, though. A single source of truth only stays single if there is discipline about who can create new sanctioned metrics and how, otherwise self-service degenerates into everyone inventing their own contradictory definitions and you are back where you started. We set up the guardrails, certified datasets, agreed metric definitions, a clear line between governed reporting and ad hoc exploration, so the trustworthiness you built does not erode the moment more people start using it.
Signs it’s time
- Your reporting lives in a sprawl of spreadsheets that only one or two people fully understand, and everyone else is guessing
- Two reports show different numbers for the same thing, and meetings start with an argument about which is right
- Leadership has no reliable, current view of how the business is performing. The numbers arrive late, if at all
- Someone spends hours or days every week manually pulling figures together into a report by hand, and it is always slightly out of date
Business intelligence, data analytics and data visualisation. The distinction
These three terms get used interchangeably and they should not be, because the confusion leads people to buy the wrong thing. Business intelligence is about the ongoing, operational picture: consolidating your data, modelling it cleanly, and reporting the KPIs and trends that tell leaders how the business is doing so they can run it. It is the standing infrastructure of dashboards and reports that answers known questions reliably, week after week. When people say they want dashboards to run the business from, they want business intelligence.
Data analytics reaches further into the uncertain. Where BI tells you what is happening and how it is trending, deeper analytics asks why it is happening, whether an effect is real, and what is likely to happen next: using statistics, experimentation and modelling to answer questions a dashboard cannot. BI and analytics share the same clean data foundation and often the same people, but the intent differs: BI is the reliable ongoing view, analytics is the deeper investigation into a specific question. A good BI layer frequently surfaces the questions that a piece of analytics then goes and answers properly.
Data visualisation is the presentation craft that sits within both: the discipline of choosing the right chart, laying it out so the important thing is unmistakable, and not misleading the eye with a truncated axis or a misjudged colour scale. It is a genuine skill and it matters, but it is a layer, not the whole. A beautifully visualised dashboard built on an incoherent data model is a beautifully visualised wrong answer. We treat visualisation as important and treat it as what it is: the finish on top of the modelling and pipeline work that actually makes the numbers true.
Technologies we build it with
Chosen per problem, not per fashion. This is the stack we most often reach for on this work.
How we deliver
- 01
Discover
We map the system, the constraints and the business it serves, including the parts nobody documented.
Architecture brief
- 02
Architect
Decisions get made, written down and defended before a line of production code exists.
Decision records
- 03
Build
Short cycles against working software. You see progress in the product, not in a status deck.
Shipping increments
- 04
Operate
Monitoring, incident response and iteration. The system is alive, so the engagement is too.
Runbooks & SLOs
Want a straight answer on Business Intelligence?
A short call with a senior engineer, before you write a brief. If Business Intelligence is the wrong answer for your situation, we will say so and tell you what we think is right.
What changes
One version of the truth
A single, agreed set of numbers that every report draws from, so meetings decide things instead of arguing about whose figure is correct.
Decisions, not decoration
Dashboards built around the handful of metrics that actually change what you do, rather than a screen of vanity numbers nobody acts on.
Reporting that runs itself
Figures that refresh reliably on schedule, freeing the person who currently rebuilds the same spreadsheet by hand every week.
Industries we serve
Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.
How pricing works
- The largest driver, by some distance, is the state and spread of your data. If your figures live in a few well-structured systems with consistent definitions, consolidation and modelling move quickly. If they are scattered across many sources, inconsistent, undocumented and full of the special cases every real business accumulates, the modelling becomes the substantial part of the work, and we would rather scope that honestly, sometimes after a short data assessment, than quote a number against data neither of us has looked at yet.
- The number and complexity of the metrics matters too. A focused executive dashboard covering a handful of well-understood KPIs is a bounded piece of work. A broad reporting suite spanning many functions, each with its own definitions to agree and edge cases to model, is a larger undertaking, and where different teams disagree about what a metric even means, the definition work itself takes real time, because agreeing the number is often harder than calculating it.
- Tool choice affects cost mainly through licensing, and it is worth being clear-eyed about it. Power BI, Tableau and Looker have very different licensing models, and the right one depends on how many people will consume the reports and how. We will help you choose on total cost and fit rather than push whichever we prefer, and we factor the ongoing licence cost into the recommendation rather than surprising you with it after the build.
- We can work to a fixed scope for a clearly-defined dashboard or reporting suite, or on a time-and-materials basis where the data landscape is genuinely unknown until we are into it, which is common when the underlying data has never been consolidated before. Where the honest answer is that a small first piece, such as a data assessment or a single flagship dashboard, should come before a larger commitment, we will say so.
Typical timeline
- 01
Decisions and metric definitions
Working with the people who will use the reports to pin down which decisions the BI must support, which KPIs matter, and exactly what each one means. Short, largely non-technical, and the part that prevents the future "why do these disagree" arguments.
- 02
Consolidation and data modelling
Mapping and pulling together the source data, then building a clean model where each metric is defined once. Usually the largest share of the effort, and the part that determines whether the finished dashboards are fast, consistent and trusted.
- 03
Dashboard build and refresh
Building the dashboards and reports in Power BI, Tableau or Looker, designed around the decisions identified up front, and wiring up automated, monitored refresh so the numbers stay current on their own.
- 04
Rollout, access and handover
Setting up row-level access and governance, training the people who will use and extend the reports, and handing over documentation of what every number means and where it comes from, so the work outlives the engagement.
What working with us actually means
We solve the data problem underneath
When dashboards are wrong or slow, we go to the model and the pipeline, because that is nearly always where the fault lives. A team that only knows the BI tool can make the chart prettier; fixing why the numbers disagree takes someone who understands the data engineering beneath it, and that is what we bring.
We build for decisions, not for show
We are willing to tell you a metric does not belong on the dashboard because it will not change anything you do. That discipline is what makes the difference between reports leaders open every morning and a launch-day showpiece that gathers dust, and it is why we start every engagement from the decisions, not the visuals.
Honest and tool-agnostic
We do not resell a BI platform, so our advice on Power BI versus Tableau versus Looker is about your stack, team and budget rather than our margin. And if the honest answer is that your data needs consolidating before any dashboard is worth building, we will tell you that rather than sell you the dashboard anyway.
Senior throughout, and we operate what we build
The person defining your KPIs and modelling your data is the senior engineer you met, not a junior handed the spec after the sale. And because we often run what we build, we design the pipeline and refresh to be reliable and monitored, since we are the ones who get called when the Monday numbers do not load.
How to engage us
Three ways to work with us on this, chosen to fit the problem, not our margin.
- Dedicated team A standing team that works only on your product, in your rituals and your tooling. Best when the roadmap outlives the project. Ongoing product development
- Staff augmentation Named senior engineers embedded into your existing team, reporting into your leads. Best when you know what to build and need capacity. Filling a capability gap
- Software outsourcing A defined outcome delivered end-to-end by an accountable team. Best when you want the result owned, not just the hours filled. Outcome-owned delivery
Related services
Part of Data Engineering. Other work we do alongside this.
Related terms
Common questions
Our dashboards are wrong or slow. Is that a dashboard problem or something deeper?
Almost always something deeper. In our experience a dashboard that shows wrong numbers is caused by the data model beneath it: the same metric calculated different ways, a join duplicating rows, a definition two teams read differently, and a dashboard that is slow is caused by a model that makes the tool do heavy work at view time that should have been done once, upstream. Rebuilding the chart rarely fixes either. We look at the model and the pipeline first, because that is where the real fault sits, and dressing it up in a nicer visual only hides it for a while.
Which tool is best. Power BI, Tableau or Looker?
There is no universal answer, and anyone who gives you one quickly is probably selling that tool. Power BI is often the pragmatic choice for organisations already in the Microsoft ecosystem and is cost-effective at scale; Tableau is exceptionally strong on interactive visual exploration; Looker fits teams that want governed, code-defined metrics tightly integrated with a cloud warehouse. The right choice depends on your existing stack, how many people will consume the reports, your team’s skills and your budget. We are tool-agnostic and will recommend on fit rather than on what we would prefer to build.
Why do our reports show different numbers for the same thing?
Because there is no single source of truth and, underneath that, no agreed definition of the metric. When two reports calculate revenue, or active customers, or qualified leads on different data or with different rules, both can be technically defensible and still disagree, and once people notice, they quietly stop trusting all of it. The fix is to consolidate the sources, agree exactly what each metric means, and encode that definition once so every report inherits the same maths. Most conflicting-numbers problems are really undefined-metric problems.
What is the difference between business intelligence, data analytics and data visualisation?
Business intelligence is the ongoing operational picture, consolidated data, clean models and KPI dashboards that tell leaders how the business is doing so they can run it. Data analytics reaches further, into why something is happening, whether an effect is real and what will happen next, using statistics and modelling to answer questions a dashboard cannot. Data visualisation is the presentation craft (choosing the right chart and laying it out clearly), that sits within both. They share a data foundation but differ in intent, and matching the right one to your need avoids a lot of wasted effort.
How do you stop us building dashboards nobody actually uses?
By starting from the decision rather than the metric. Before we build anything we work out what you are actually trying to decide and which numbers, if trusted, would change what you do, and we are willing to leave a metric off if it will not change a decision. Vanity dashboards, full of impressive-looking numbers that alter nothing, are one of the two main ways BI fails; the other is conflicting numbers that erode trust. We design against both deliberately, because the value of business intelligence is the decision it changes, never the chart itself.
Thinking about Business Intelligence?
Tell us the problem in your own words, not in requirements. A senior engineer reads it and comes back with a straight view on whether Business Intelligence is the right answer here, or what would be.
- 01A senior engineer reads it. Not a form queue, and not an account manager.
- 02We reply either with questions or with a straight answer that we are not the right fit.
- 03If it looks like a fit, a technical call with the person who would actually run the delivery.
- 04Then scope, effort and risk in writing, before anyone signs anything.