Data and Analytics

Dashboards and reports

A dashboard is worth building when it changes what someone does on Monday morning. We design views around the decisions you make, not around every metric a tool can export.

Most dashboards fail for the same two reasons

The first is that nobody trusts the numbers. Someone spots a figure that contradicts what they know from the source system, raises it once, gets a vague answer, and quietly stops opening the dashboard. Trust is lost quickly and rebuilt slowly, which is why validation against a known period matters more than any design decision.

The second is that the dashboard answers questions nobody asked. It shows what the tool exports easily rather than what the business argues about. Sessions and impressions are simple to display and rarely change anyone's plans. Which channel is producing customers who stay is harder to compute and actually affects where next quarter's budget goes.

Both failures are avoidable, and both are cheaper to avoid at the sketch stage than after launch.

Designing around decisions

We start every dashboard by asking a slightly awkward question: if this number were higher or lower than you expect, what would you do? If there is no answer, the metric is context at best and clutter at worst.

That question tends to reorganise the whole layout. A sales director who needs to know where deals are stalling wants pipeline by stage with time-in-stage attached, not a revenue total they already know from the finance report. A marketing lead comparing channels needs cost per acquisition next to retention, because the cheapest channel to acquire from is frequently the worst one to keep.

Once the decisions are clear, the visual work is largely restraint. One primary number, a few that explain it, comparison against target and against the same period last year, and a way down into the detail when the headline looks wrong.

Readable without a briefing

The audience for most business dashboards includes people who do not think in data. They will glance at it in a meeting, form an impression in a few seconds, and move on. Design has to survive that.

In practice this means labelling in business language rather than column names, showing change rather than raw values where change is what matters, and choosing conventional chart types. A bar chart that everyone reads correctly beats a more elegant visualisation that half the room misinterprets. Colour carries meaning sparingly, usually only to mark whether something is above or below where it should be.

We also try to keep one screen as the entry point. Scrolling is where attention goes to die.

What we build reports in

For most companies an established BI tool is the right answer. Looker Studio, Metabase and Power BI are mature, your team can adjust them without a developer, and there is no additional application for anyone to maintain. The engineering effort goes into the data model underneath, which is where it belongs.

Custom-built dashboards are worth it in a narrower set of cases: when reporting has to sit inside your own product, when external customers will see it, or when the view needs to combine numbers with actions the user can take from the same screen. Those are real requirements, but they are less common than the desire to build something bespoke suggests.

Reports that get read

A dashboard is a place you go. A report is something that finds you. Both have their place, and the difference matters for adoption.

Weekly and monthly summaries delivered by email reach people who will never open a BI tool, particularly executives and external stakeholders. They work best when they are short, contain the same handful of figures each time, and lead with what changed rather than with everything that stayed the same. Where a threshold is crossed, an alert beats a report entirely.

The measure of success is boring but real. Six months in, the numbers are quoted in meetings without anyone reopening the source system to check them.

What you get

Sales dashboard

Revenue, conversion rate and pipeline stages in one view, split the way your business is actually organised: by product, channel, region or rep.

Marketing reporting

Campaign performance, cost per acquisition, lead sources and return on ad spend, combined across platforms instead of read one ad account at a time.

KPI monitoring

The small set of numbers that describe whether the business is on track, shown against target and against the same period last year.

At-a-glance summary views

A single screen for people who need the state of things in ten seconds and will not scroll. Detail stays available one click below.

Drill-down paths

Every headline number can be opened to the level that explains it, down to the individual orders or sessions behind the figure.

Access rules

Regional managers see their region, the board sees the whole picture, and nobody has to maintain a separate copy of the report to make that work.

How we work

  1. 01

    Start from the decisions

    We ask what you do differently depending on the number. A metric that would not change any action is a metric we leave out, regardless of how easy it is to display.

  2. 02

    Sketch before building

    Layout, groupings and comparisons get agreed on a wireframe first. Rearranging a sketch takes minutes, rearranging a finished dashboard takes a day.

  3. 03

    Model the data behind it

    Each metric gets a single defined calculation in the reporting layer, so the same figure means the same thing wherever it appears.

  4. 04

    Build and validate

    We build the views, then check them against a period you already know the answer for. Anything that fails that check gets traced before launch.

  5. 05

    Roll out and revise

    After a few weeks of real use we look at what people actually open, cut what nobody looks at and add what they went back to a spreadsheet for.

Tools and technology

Where a solid open-source tool exists, we choose it over a closed one. No lock-in to a single vendor, and costs you can actually predict.

  • Metabase
  • Apache Superset
  • Grafana
  • PostgreSQL
  • dbt
  • Next.js
  • PostHog
  • Looker Studio
  • Power BI
  • Google BigQuery
  • Google Analytics 4
  • Google Sheets

Frequently asked questions

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Which numbers should be waiting for you on Monday morning?

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Tell us which questions you keep answering by exporting spreadsheets. We will sketch the views that would answer them at a glance and suggest where to start.

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