Business Intelligence & Data Visualization
Three reports. Three different numbers. We build the one everyone trusts.
Governed metrics, decision-ready dashboards and self-service that people actually adopt.
The people who scope it are the senior people who build it.
One question, three answersIllustrative
How many active customers do we have?
- Finance pack41,200
- Sales dashboard46,850
- Operations export38,900
One definition
43,100
- Counts
- Bought in the last 90 days
- Owner
- Customer operations
- Refreshed
- Daily, before 6 AM
What we build
Four ways people meet a number. All four agree.
Executive dashboards
“What do I need to know this morning?”
A handful of measures, exceptions first, each tied to a decision someone owns.
Operational reporting
“Is it on time, and the same as last week’s?”
Recurring reports automated and reconciled, laid out for the person who acts on them.
Self-service analytics
“Can I answer this without raising a ticket?”
Governed data people can explore, or question in plain language, with guardrails.
Embedded analytics
“Can I see it where I already work?”
Numbers inside the application, shown at the moment of the decision.
Underneath all four: governed metric definitions and semantic models reused across reporting, so a metric is defined once. We work in the BI tool you already run, including Power BI, Tableau and Looker.
Why reports fail
Dashboards rarely fail on design. They fail on trust.
No shared definition
Each team computes the number its own way, and the meeting is spent reconciling.
Built for the builder
Forty charts, and no decision any of them is for.
Nobody owns it
A source changes, the number drifts, and people quietly go back to spreadsheets.
Where we start
Reporting is an estate, not a dashboard.
We do not begin by asking how many dashboards you want. We begin with which decisions matter, which numbers support them, what reporting already exists, and what can be retired.
- Keep
- Used, owned, and in line with the agreed definition.
- Consolidate
- Several reports answering the same question.
- Automate
- Still assembled by hand every cycle.
- Retire
- Nobody opens it, or nobody owns it.
Reporting inventoryIllustrative
| Report | Opened, last 90 days | Owner | Call |
|---|---|---|---|
| Regional sales dashboardUsed in the Monday review | 31 of 40 readers | Sales operations | Keep |
| Margin by productTwo versions, two figures | 14 of 40 readers | Finance and Sales | Consolidate |
| Month-end close packAssembled by hand each month | 22 of 40 readers | Finance | Automate |
| Weekly sales packEmailed every Monday | 3 of 40 readers | No owner | Retire |
| Churn exportBuilt once for one question | 0 of 40 readers | No owner | Retire |
What the estate costs today. In the first phase we put a figure on five things, where your records allow it.
- Manual hoursTime spent assembling recurring reports by hand.
- DuplicatesDashboards and reports that answer the same question.
- ReconciliationTime spent agreeing the number before the meeting.
- Decision delayHow long a decision waits for its report.
- Licences and capacityWhat is paid for reports nobody opens.
How we are different
Define the number once. Then design the dashboard.
Every metric gets a written definition, a source and an owner before anything is drawn. The dashboard is the last step, not the first.
One definition, used everywhereExample
- Orders
- Billing
- Customer records
Semantic layer
Active customers
- Counts
- A purchase in the last 90 days
- Excludes
- Test accounts, internal orders
- Owner
- Customer operations
- Changes
- Reviewed, versioned, announced
- Executive dashboard43,100
- Sales view43,100
- Finance pack43,100
Ask in plain language. The same definition answers.
Questions asked in plain language work well on governed definitions and badly without them. On a governed layer the answer matches every dashboard, and where no definition exists the tool says so instead of guessing.
We set this up in the BI tool you already run, where it earns its place.
A question, answered from the definitionIllustrative
-
How many active customers do we have?
- Matched to
- Active customers
- Counts
- A purchase in the last 90 days
- Owner
- Customer operations
43,100The same figure as every dashboard.
-
And how many of those are in the East?
- Matched to
- Active customers
- Filter
- Region is East
- Owner
- Customer operations
7,340The same definition, with one filter.
-
What is our revenue per head?
- Matched to
- No agreed definition
- Closest
- Revenue per employee, revenue per customer
- Owner
- None yet
No figure givenIt asks which one you mean instead of guessing.
Start from the decisions
Who decides what, how often, and which existing reports already serve them.
Agree the definitions
Each metric written down, with a source and a named owner.
Model once
A governed semantic layer behind dashboards, reports and exports: one model, or several by domain.
Design, then measure use
Built with its readers, and judged by whether they open it.
Information design
Good design is mostly what we take out.
Once the numbers agree, design decides whether anyone acts on them. Each view is built for one reader, one decision and one next step.
AfterIllustrative
Customer retention: weekly review
For the head of customer operations. One decision: where the retention team works this week.
2 of 5 regions are below the 88% retention target
Retention by region, against the 88% target
- East82%, 6 points below
- South84%, 4 points below
- Central88%, on target
- West89%
- North91%
Active customer: bought in the last 90 days. Owner: Customer operations. Refreshed daily, before 6 AM.
Open the 74 at-risk accounts
EmphasizedWhat is off target is read first. Everything on target stays quiet.
RemovedNineteen measures nobody acted on. Four remain, each one part of the decision.
AddedThe definition, the owner and the next step, on the view itself.
Proof
Leaders working from live data, not weeks-old reports.
Healthcare · People and workforce reporting
Scattered spreadsheets replaced by one analytics layer over the core people system.
A multi-site care provider. Leaders stopped waiting for reports to be assembled and started working from the same live numbers.
Read the case study- 30%reporting efficiency gained
- 45%fewer reporting errors
- 70%of staff found it easier to use
Food manufacturing · Supply-chain planning
Planner dashboards showing plan against actual, by customer and location.
A large food manufacturer and distributor. The dashboards were one part of an analytics layer on its existing planning system; planners used them to tune each cycle.
Read the case studyAdoption
A dashboard nobody opens is not finished.
We treat use as the deliverable. After launch we track it, where usage data is available and as agreed in scope:
- Who opens it, and how often, by role
- Which decisions cite it in the meetings it was built for
- Which old reports were retired because this one replaced them
- How many questions people now answer themselves
- Who owns each metric when a source changes
Reports in circulationIllustrative
Not sure the data underneath is sound? A free self-assessment, about six minutes, no email required.
How to engage
Three ways in, depending on where the problem starts.
Is the data underneath sound?
Data Platform Accelerator
Diagnose the estate, prove the target pattern on a bounded scope, and leave with a working foundation. Two to four weeks. Fixed scope. Fixed fee agreed in writing first.
- A keep, fix or build call on every in-scope component
- A governed, costed foundation
- First pipelines live
- A pattern your team can run
Need numbers people trust? · The main engagement
Reporting & Analytics Modernization
One set of governed definitions. Reporting and dashboards built around named decisions.
- Starts with
- One decision area, scoped in a written proposal
- Proceeds when
- Its definitions are agreed and each has an owner
- Ends with
- Reporting in use, old reports retired, handed over
- The reporting estate reviewed: keep, consolidate, automate or retire
- Metric definitions, each with a source and an owner
- A governed semantic layer behind reporting
- Dashboards designed with the people who will use them
- A team that can run and extend it without us
Want the numbers inside your product?
Embedded analytics
Governed measures placed inside the application your staff or customers already use.
- The same definitions as your dashboards
- Access by role and by customer
- Performance sized for your volumes
- A design your team can extend
How we work. Every engagement follows our Data IQ™ methodology, from diagnosis through implementation and handover. See how we work
Before you commit
Questions we get every week.
Which BI tools do you work with?
Usually the one you already run. We build in Power BI, Tableau and Looker, and in embedded analytics platforms. We do not resell licences, so the recommendation is not tied to a product.
Do we need a new data platform first?
Usually not. Many reporting problems are definition and ownership problems, and can be fixed on the platform you have. Where the data underneath is not sound, we say so early, and the Data Platform Accelerator exists for that case.
How do you get people to actually use a dashboard?
By building it for a decision someone already makes, with that person in the room, and by retiring the report it replaces. After launch, where usage data is available, we track who opens it and whether the meetings it was built for rely on it.
Can self-service work without turning into chaos?
Yes, when people explore governed measures rather than raw tables. The semantic layer does the hard part once, so two people asking the same question get the same answer.
Can we ask questions of our data in plain language?
That works well on top of governed definitions and badly without them. The definitions are what stop a natural-language tool from answering confidently and wrongly. We build the semantic layer first and add plain-language questions where they earn their place.
How many dashboards will we end up with?
Usually fewer than you have now. A modernization consolidates overlapping reports into a smaller set, each with an owner and a purpose.
The people who scope it are the people who build it
Bring us the number nobody agrees on. We’ll show you where it splits.
Thirty minutes with the senior people who would do the work. If a rebuild isn’t the right next step, we’ll say so.
Talk to the people who would design itGo deeper.
- Case studyA people-data platform for a multi-site care provider
- Case studyPlanner dashboards on an existing planning system
- ArticleWhy dashboards go unused
- ArticleSelf-service analytics without the chaos
- FrameworkThe Data IQ™ framework and maturity model
- AssessmentsFree AI and data assessments
A number is only as good as what feeds it and who reads it.
Have a number nobody trusts? Thirty minutes with the people who would rebuild it.
