Dashboards & Business Intelligence
An editor decides what doesn't run
Dashboards nobody opens have the same fault: they plot everything and answer nothing. A front page runs one lead, a couple of supports, and spikes the rest, and the judgment is entirely in what got cut. We build dashboards the same way, around the decisions you actually make.
The Monday Edition
wk 41
Lead
Revenue — the number the week turns on
Orders
3,120 +8%
Conversion
3.4% +0.4
Spiked · 44
One lead, two supports, 44 spiked. Every one of those spiked numbers is true. None of them changes what you do on Monday.
One lead
Not every metric
One definition
Per metric, everywhere
Self-serve
No queue for the analyst
It tells you
Alerts, not month-end
The brief
23 metrics on the dashboard. 3 decisions in the meeting.
Every grey one is a real number, correctly calculated, and completely irrelevant to what you'll do on Monday. Plotting them feels generous. It's also the reason nobody opens the page.
So we start from the decision and work back to the number, define that number once so it can't mean two things, and make it fast enough to become a habit. Then we keep editing, because dashboards accumulate unless someone spikes things.
What changes
01
People open it, because it answers something
A dashboard gets used when it answers a question someone genuinely has. Designing backwards from the decision is the whole difference between a tool and a wallchart.
02
The numbers stop disagreeing
One definition per metric, held in a metrics layer and reused everywhere, so revenue, active users, and conversion mean the same thing in every report and every meeting.
03
The queue for the data person disappears
Self-serve exploration with guardrails means your team slices and answers their own questions instead of filing a request and waiting three days for a number.
04
You hear about it when it moves
Alerts and anomaly detection reach the right person the moment a number goes the wrong way, long before a monthly review would have spotted the problem.
What we build
The page, the desk, and the archive
Executive dashboards
The handful of numbers leadership runs the business by, live and always current. The front page of the business.
Operational dashboards
Real-time views of the queues, jobs, and metrics your teams manage day to day, built to be read at a glance.
Self-serve BI
Tools that let non-technical staff slice the data and answer their own questions, with guardrails so they can't accidentally mislead themselves.
A metrics layer
One place metrics are defined, so every dashboard and report agrees on the numbers. The style guide that ends the argument.
Alerts & anomalies
Get told when a number moves the wrong way, instead of finding out at month-end when the quarter is already shaped.
Reports & scorecards
The recurring reports and scorecards your teams need, generated and delivered automatically so nobody rebuilds them by hand each cycle.
The engagement
From what you decide to a page people open
Ask what you decide
We start from the decisions you need to make and the questions behind them. The inventory of data you happen to have comes second.
Settle the definitions
One clear definition per metric, agreed and written down, so nothing means two things in two rooms. This is the meeting nobody enjoys and everybody needs.
Design and build
Dashboards designed for clarity and speed on your data and tools, with the lead obvious and the noise cut.
Roll out and prune
We launch to your teams, train them, and keep editing: what nobody opens gets spiked, and what people keep asking for gets promoted.
How we work
Four rules that keep a dashboard worth opening
Start from the decision, not the data
The question is never "what can we plot?" It's "what will you do differently depending on this number?" Anything that fails that test doesn't earn a tile.
Define the metric once
Every tool defines metrics its own way: different filters, timezones, rules. A metrics layer makes one definition and reuses it, which is the single biggest fix for numbers that don't agree.
Fast, or nobody uses it
A dashboard that takes eight seconds to load gets opened once. Speed isn't polish here; it's the difference between a habit and a bookmark nobody clicks.
Edit it forever
Dashboards rot. We prune what nobody opens and promote what people keep asking for, because a page that accumulates tiles ends up answering nothing again.
One version of the truth
Six things that decide whether it gets opened twice
None of them are about which chart type you picked. All of them are why a page becomes a habit instead of a screenshot in someone's deck.
01Metrics defined once, used everywhere
The definition lives in one place and every dashboard inherits it, so revenue means the same thing in the board pack and the ops view. Without this, every tool is quietly right about a different question.
02Built around real decisions
Each view exists because someone makes a call with it. That constraint is what keeps a dashboard to a readable handful of numbers instead of a wall that gets ignored.
03Fast, so people actually use them
Query and render speed decide adoption more than layout does. A view that answers in under a second becomes part of the morning; one that spins becomes a screenshot in a deck.
04Drill-down from summary to detail
The lead answers the question; the inside pages answer the follow-up. Being able to go from a number to the rows behind it is what makes people trust the number at all.
05Access and permissions per team
Each team sees the surface that's theirs, so sensitive figures stay contained and nobody wades through eleven views to find the one that concerns them.
06Documented, so numbers can be trusted
What a metric includes, excludes, and when it refreshes, written down. Trust is what turns a dashboard into a decision instead of the opening move in a debate.
Why it pays
The meeting starts at the decision
When everyone reads the same lead and trusts it, the first twenty minutes stop being an argument about whose figure is right.
Analysts do analysis again
Self-serve takes the routine 'can you pull me a number' requests off the data team, so their time goes to the questions that actually need them.
Problems surface while they're small
An alert on a moving number reaches someone the same day, well before month-end, when the quarter has already absorbed it.
The dashboard stays worth opening
Because it's pruned as it grows, it keeps answering questions instead of slowly becoming another wall nobody reads.
Selected work
Two pages people actually opened
What you get
A page worth opening, and the rules that keep it that way
- 01Dashboards built around your real decisions
- 02A metrics layer with one definition per metric
- 03Self-serve exploration for your team
- 04Alerts when numbers move the wrong way
- 05Fast, clear, drill-down-ready views
- 06Documentation and training for adoption
Industry expertise
Where the lead changes what happens today
E-commerce & retail
Trading views where the lead is today's revenue and the follow-up is always which channel moved.
Financial services
Board and risk reporting where a metric's definition is a governance question.
Logistics & distribution
Operational boards glanced at hourly, where a late number is the same as no number.
Professional services
Utilization and pipeline views that answer whether the quarter lands, without a week of spreadsheet work.
Healthcare & clinical
Operational dashboards where access control and an auditable definition both matter as much as the chart.
Travel & hospitality
Demand and occupancy views where the decision is a price change today, and next month's report is too late.
Late edition
Tell us the one number your Monday turns on
If it takes more than a sentence, that's the problem worth solving. We'll work back from the decision to the metric, settle what it means, and build the page that leads with it.
Why us for this
We ask what you'd do differently
Every tile has to earn its place by changing a decision. It's a blunt question, and it's why our dashboards stay small enough to read.
We fix the definitions under the charts
Most "our dashboards are wrong" problems are definition problems. We build the metrics layer that ends them, so nobody's just restyling the symptom.
We prune, and we'll say what to cut
A dashboard is edited, not accumulated. We'll tell you which views nobody opens, including ones we built, because a page that answers nothing is worse than no page.
Working with Flaidex
01
Tool chosen for your team
We work with the major BI tools and build custom where they don't fit. The goal is something your people actually open, which is rarely the most powerful thing on the market.
02
We can build the pipes underneath
A live dashboard on stale data is worse than none. If the data isn't flowing dependably yet, we'll build that groundwork first instead of painting over it.
03
Adoption is part of the job
We roll out with your teams and train them, because a dashboard nobody was shown is a dashboard nobody uses, however good the design.
Questions
What people ask before they rebuild the dashboard
We already have dashboards nobody looks at. What's different?
Usually those dashboards plot everything and answer nothing. We design around the actual decisions people make, cut the noise, make them fast, and define the metrics clearly so they're trusted. A dashboard gets used when it answers a question someone genuinely has, and that's what we build toward.
Why do our numbers never match between tools?
Because each tool defines metrics its own way: different filters, timezones, and rules. We build a metrics layer where each metric is defined once and reused everywhere, so revenue, active users, and conversion mean the same thing in every report. It's the single biggest fix for "the numbers don't agree."
Which BI tool do you use?
We work with the major BI tools and can build custom dashboards where those don't fit. We'll recommend based on your team, budget, and how self-serve you want to be. The goal is something your people will actually use, which beats the most powerful tool nobody opens.
Can non-technical people use it?
That's often the point. We design self-serve BI so your team can filter, slice, and answer their own questions without writing SQL or waiting on an analyst, with guardrails so they can't accidentally mislead themselves.
Will the dashboards stay current?
Yes. They sit on top of reliable data pipelines, so they refresh automatically. We can build those pipelines too if your data isn't yet flowing dependably, since a live dashboard on stale data is worse than none.
Can it alert us instead of us checking?
Absolutely. We set up alerts and anomaly detection so a number moving the wrong way reaches the right person immediately, instead of turning up in a monthly review when it's already a problem.
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