KPI and metric definition
Agree a small set of measures that genuinely reflect business performance, define each one precisely enough to implement, and resist the pull toward tracking everything measurable.
Data Analytics
Turn business data into decisions your team will actually make
Ufanzo builds reporting around the decisions you need to make — clear KPIs, dashboards with a purpose, and analysis that explains what changed and what to do about it.
Why it matters
The problem before the solution.
Most organisations do not lack dashboards. They lack dashboards anyone opens twice. Reporting gets built around what is easy to chart rather than what decision it informs, so it accumulates numbers nobody acts on and loses the trust of the people it was built for. Ufanzo works the other way around: start from the decision, identify the small number of measures that would actually change it, define those measures precisely, and build reporting that makes the answer obvious — including when the answer is that nothing needs to change.
Problems addressed
Signs this is the work you need.
If several of these describe your situation, there is usually a structural cause worth addressing rather than a series of coincidences.
- Dashboards that display a lot of numbers but do not inform a decision
- Nobody trusting the reporting enough to act on it without checking manually
- Leaders finding out about a problem a month after it started
- Analysis that describes what happened but not why or what to do
- The same question answered differently by different teams
- Reporting that takes days of manual assembly each period
What Ufanzo does
Data Analytics, in practice.
Dashboards and operational reporting
Build reporting aimed at a specific audience and decision — an operational view for the team running the process, a summary view for leadership — rather than one dashboard attempting to serve both.
Trend and cohort analysis
Look at how measures move over time and across segments, so a change can be attributed to a cause rather than reacted to as noise.
Decision support and interpretation
Deliver analysis with a written interpretation: what moved, what plausibly drove it, how confident the conclusion is, and what the options are. A chart without a reading is unfinished work.
Self-service enablement
Structure the underlying models and document the definitions so the team can answer follow-up questions without waiting in a queue for every request.
Automated refresh and distribution
Replace manual period-end assembly with scheduled refreshes and delivery, so reporting arrives on time without someone's calendar reminder.
What is inside
The disciplines this covers.
Data Analytics is not one activity. These are the sub-practices it breaks into, and an engagement usually draws on several rather than all of them.
Metric definition and semantic modelling
Each measure implemented once in a shared layer and consumed from there. When the same calculation is rebuilt inside three reports they will eventually disagree, usually in front of the person least able to tolerate it.
Operational reporting
Views for the team running a process day to day - throughput, backlog, ageing, exceptions - refreshed fast enough that someone can still intervene.
Leadership and performance reporting
A short recurring view of the measures the business actually steers by, with written commentary on what moved and why, rather than a wall of charts left to interpretation.
Trend, cohort and funnel analysis
Separating real movement from noise and mix effects: how measures shift over time, across segments, and between the steps of a multi-stage process.
Decision and change analysis
Focused work on a specific question - why a metric moved, what a proposed change is worth, whether an intervention did anything - with the confidence level stated plainly.
Data reliability and freshness
Reconciliation against source, row-count and total checks, and visible last-refresh state. A stale dashboard looks identical to a genuine decline, which is how an outage becomes a bad decision.
Business benefits
What changes for the business.
Stated as directions rather than figures. The size of the change depends on your starting point, and anyone quoting a percentage before looking at your environment is quoting a sales number.
- A shared, agreed view of how the business is performing
- Problems visible while there is still time to respond
- Less time spent assembling reports each period
- Decisions grounded in evidence rather than the loudest opinion
- Fewer disputes about whose number is correct
- Reporting people open because it tells them something
Where it fits
Typical engagements.
Leadership performance reporting
A concise recurring view of the measures leadership actually steers by, with commentary on what changed since the last period.
Operational monitoring
Reporting for the team running a process day to day — throughput, backlog, exceptions and cycle time — at a refresh rate that supports intervening.
Funnel and conversion analysis
Understanding where prospects or applications drop out of a multi-step process, and quantifying what improving a given step would be worth.
Investigating a change
A focused analysis of why a metric moved — segmenting the change, ruling out mix effects and data issues, and testing the plausible explanations.
How it works
How reporting is built to stay trustworthy
For readers evaluating whether this is done properly.
Metrics are implemented once, in a shared modelling layer, and consumed from there. When the same calculation is reimplemented inside three separate reports, they will eventually disagree — usually at the worst possible moment. Centralising the definition means a change to it propagates everywhere at once, and the change is reviewable.
Reporting logic is version-controlled and tested like any other code. Tests cover the things that silently corrupt a number: a join that fans out and double-counts revenue, a filter that drops rows with a null dimension, a date boundary that moves with the viewer's timezone. Row counts and totals are reconciled against the source as part of the refresh.
Freshness and completeness are surfaced in the reporting itself. A dashboard showing a stale or partial load looks identical to one showing a genuine decline, which is how a data outage becomes a bad business decision. Showing the last successful refresh and failing visibly is cheaper than the alternative.
Related work
What this connects to.
These areas overlap in practice, and work in one often depends on work in another.
Analytics depends entirely on data management: agreed definitions and trustworthy inputs are what separate reporting people act on from reporting people quietly re-derive in a spreadsheet. Measurement is also how automation and FinOps prove their value - the before-and-after on cycle time or cost per transaction is an analytics deliverable.
Data Management
Data modelling, quality, governance and integration that turn scattered records into a dependable foundation for reporting.
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Workflow automation and AI-assisted processes that cut repetitive manual work and keep people in control of decisions.
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Architecture and modernisation assessments, technology strategy, vendor evaluation and implementation planning you can act on.
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Let's talk
What decision are you making without good numbers?
Tell us the decision you need to make and what you currently look at to make it. We will work out what reporting would genuinely change the answer.