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We define the questions, metrics, data foundations and reporting rhythm required to give leaders a reliable view of performance, before any dashboard gets chosen or built.
These rarely arrive alone. Most leadership teams that land here recognise three or four at once. Open any symptom for what it usually signals.
Every cycle this continues, budgets, hiring and pricing are set on numbers nobody fully believes, and the organisation's best judgement runs uncalibrated.
The instinctive fix for a visibility problem is a better dashboard. It is usually the wrong first move. A dashboard is the last step in a chain: it can only display what the layers beneath it provide. If the decisions it should serve were never defined, if two teams hold different definitions of the same metric, or if the lineage from source system to displayed figure is broken, a new dashboard renders the same confusion with better charts. The work starts further back: with the decisions leadership needs to make, the questions those decisions raise, and the definitions and data required to answer them. Choose the dashboard last.
Work back from decisions to questions, from questions to metrics, and pin one agreed definition on each.
Trace each metric from source system through every transformation to the view it appears in, and repair the lineage where it breaks.
Give every metric an owner, every view a review cadence, and every movement a defined response.
Illustrative view structures showing what each audience needs answered. These describe the shape of the views we design, not any client's data.
Is the business on plan, where is it off, and which of the handful of levers we track needs attention this month?
What is each channel contributing at what cost, and where should next month's budget move?
Is the pipeline healthy enough for the target, where are deals stalling, and is follow-up happening on time?
Where is work queuing, is delivery on commitment, and which capacity limit will bite first?
How do margin, cash and cost trends look against plan, early enough in the cycle to respond?
Are customers being retained and growing, where is churn risk concentrating, and what is it costing?
Each layer only proceeds if the one before it shows it's needed. Some organisations stop after the definitions are fixed, others go all the way to a governed platform.
The Data Visibility & Decision Systems Audit establishes why the current numbers can't be trusted: which decisions matter, where definitions conflict, where lineage breaks and where ownership is missing.
Data & Decision Making consulting turns the findings into the KPI framework, metric dictionary, reporting structure and review cadence: the definitions everything downstream is built against.
Where the data itself can't support the framework, Data Engineering builds the pipelines, transformations and reporting-ready datasets that make the agreed definitions computable and current.
Decision views are built to the requirements: in Kepler, our reporting platform, where it fits, or in the BI platform you already run. The tool is chosen last, against defined requirements.
Technology Managed Services keeps the system governed after handover: definitions maintained, pipelines monitored, new questions absorbed without the trust decaying back to where it started.
Data Visibility is the problem; these are the pathways that address it: from diagnosis, through definitions and foundations, to governed delivery.
Owns: conflicting numbers · late reports · unused dashboards · unclear attribution · decisions made on instinct.
You don't yet know why the numbers can't be trusted.
Fixed-scope diagnosis of decisions, definitions, lineage, quality, ownership and cadence.
The entry point. It locates where the decision system is broken before anything is designed or built.
Only partly. And if the current dashboards are unused or distrusted, a rebuild alone usually repeats the failure. The work here fixes what feeds the dashboards: decisions, definitions, lineage and ownership. If you genuinely only want existing views restyled with no change underneath, we're not the right fit for that.
It's less about volume and more about decisions. If leadership makes recurring calls (budgets, pricing, hiring, capacity) and the numbers behind them are conflicting, late or distrusted, there's enough at stake. Organisations with a single system and a handful of simple reports usually don't need this yet.
No. And wholesale cleansing before defining decisions is usually wasted effort. The decision map tells us which data actually matters; quality work is then scoped only where a priority decision depends on it. Poor quality in data nobody decides with can stay poor.
No, and please don't. The tool is chosen last, against dashboard requirements produced by the earlier layers. Many organisations discover their existing platform is adequate once the definitions and pipelines beneath it are fixed.
Either. Kepler is our reporting platform and we use it where it fits, but the decision map, metric dictionary and dashboard requirements are platform-agnostic. They can be delivered in the BI tooling you already run, by us or by your own team.
An analyst answers questions inside the current system. This work rebuilds the system itself (the definitions, lineage, ownership and cadence) so that analysts, and leadership, work from numbers that hold. Many clients pair the two: the decision system makes their analysts far more effective.
With the Data Visibility & Decision Systems Audit: a fixed-scope diagnosis of decisions, definitions, sources, quality, ownership and cadence. If you want a read on the problem before any conversation, the Data Visibility and Decision Quality Scorecard is a self-serve starting point, anonymous until you ask for your results.
Standalone artefacts: a decision map, KPI framework, metric dictionary, source map and prioritised roadmap. They're written so your own team (or another partner) can implement from them. Each layer is a separate decision; nothing commits you to the next one.
Typically the manual assembly burden falls once pipelines replace hand-collation, and the report count falls once views without a decision attached are retired. We won't put a number on that for your organisation until the audit has measured where your reporting time actually goes.