Dashboards and Reporting
Dashboards and reporting work starts with a metric dictionary: one definition and one owner per number. Two dashboards disagreeing about revenue is a governance problem, and no visualisation tool has ever fixed one.
Agree the numbers before drawing them
The request is usually for a dashboard. The problem is usually that finance and sales define margin differently, that two systems both report order volume, and that nobody can say which figure is authoritative. Building each team a dashboard in that situation makes the disagreement faster to discover, not smaller.
So the first deliverable is a metric dictionary: for every number that matters, one definition, one source of truth, one named owner. It is unglamorous and it is the difference between reporting that gets used and reporting that gets argued about.
The second discipline is building for a decision rather than for a screen. A dashboard that answers no question anyone was going to act on is monitored for three weeks and then ignored, which is the fate of most of them.
What the engagement covers
Six workstreams. The first two are prerequisites, not preliminaries.
Metric dictionary
Every significant number defined once, with its calculation, its source of truth and a named owner — including the awkward ones where two definitions currently coexist and both are in use.
Source-of-truth mapping
Which system owns each field, so that a figure has a single authoritative origin rather than three plausible ones.
Decision-led design
Each dashboard tied to a decision someone actually makes on a stated cadence, because that constraint eliminates most of what usually gets requested.
Build and integration
Implementation in the tool you already own and can staff, with data refresh, latency and transformation logic documented rather than embedded invisibly.
Refresh and failure alerting
Refresh schedules, latency expectations and alerting when a load fails — because a stale dashboard that looks current is worse than one that is visibly broken.
Handover
Documentation and training so your team can extend the reporting, since a reporting layer only you can change becomes a bottleneck within a quarter.
How the engagement runs
Define the metrics
Definitions agreed across the teams that use them, with conflicts surfaced and resolved rather than averaged.
Map the sources
System of record established per field, with transformation logic recorded so a number can be traced back to its origin.
Identify the decisions
Each proposed dashboard tied to a decision, an owner and a cadence — or dropped from scope.
Build
Implementation in your existing tool, with refresh, latency and alerting configured as part of the build.
Hand over
Documentation and training so your team can extend it, plus review ownership per metric.
Reporting layers compared
Where each belongs.
| Layer | Answers | Audience | Refresh |
|---|---|---|---|
| Operational reporting | What is happening now | Operators | Real time to hourly |
| Management dashboard | Are we on track | Managers | Daily to weekly |
| Executive summary | Should we change course | Leadership | Weekly to monthly |
| Ad hoc analysis | Why did that happen | Analysts | On demand |
| Statutory reporting | What must be filed | Finance, auditors | Per period |
What we will insist on first
We will not build competing dashboards on undefined metrics. Where finance and sales genuinely disagree about how margin is calculated, delivering both versions produces two authoritative-looking screens and a longer argument. That definition is a business decision, and we will stop and ask for it rather than encoding both.
Tool choice is also rarely the constraint. Organisations frequently arrive convinced that a different platform will fix their reporting, when the actual problem is definitions and data quality. Migrating those problems to a better-looking tool is an expensive way to keep them.
- One definition, one source, one owner per number — before any dashboard is built.
- Tie every dashboard to a decision, an owner and a cadence, or drop it.
- A stale dashboard that looks current is worse than one visibly broken.
- Tool choice is rarely the constraint; definitions and data quality usually are.
Questions buyers ask about this
Which tool do you recommend?
Whatever you already own and can staff — Power BI, SAP Analytics Cloud, Looker Studio, or SAP standard reporting. Introducing a tool your team cannot maintain creates a dependency on us, which is not a good outcome for you. Tool choice is rarely what is holding your reporting back.
Why start with a metric dictionary?
Because if two teams define margin differently, building each a dashboard makes the disagreement faster rather than smaller. One definition, one source of truth and one named owner per number is what makes reporting something people act on instead of argue about.
How do you decide what goes on a dashboard?
Each one has to be tied to a decision somebody actually makes, on a stated cadence, with a named owner. That constraint removes a surprising share of what gets requested, and what survives it tends to be used a year later.
Can you report across SAP and non-SAP systems?
Yes. The work is establishing which system owns each field and how figures reconcile at the boundaries. Cross-system reporting fails on reconciliation rather than on connectivity, so that is where the design effort goes.
What happens when a data load fails?
Alerting to a named owner, and where possible a visible indicator on the dashboard itself that the data is stale. A dashboard that silently serves yesterday’s figures while looking current has caused more bad decisions than one that is obviously broken.
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Still not sure this is the right service?
Answer four questions and we will tell you which one fits — or that none of them do.
Tell us what you are trying to fix.
A short conversation about the objective, the constraints and the timing. If we are not the right fit, we will say so.