Fewer numbers, better ones
KPIs chosen because someone acts on them. A dashboard with six meaningful metrics beats one with forty that nobody can prioritise.
Service
Data & Analytics: BI Dashboards & Reporting
Reporting that answers the question you actually asked.
Most dashboards are built to display everything available rather than to answer anything specific. They get opened enthusiastically for a fortnight and then ignored, because reading them takes more effort than the decision they were meant to inform.
We build the other kind: a small number of metrics that map to decisions someone actually makes, delivered where they will be seen, with enough self-service underneath that teams can follow up their own questions without joining a queue.
BI Dashboards
Dashboards in Power BI, Tableau or Looker, built around the decisions people actually make rather than around everything the warehouse happens to contain.
Executive views answer a different question from operational ones and are designed separately, because a board pack and a daily working screen have almost nothing in common.
Includes- Power BI
- Tableau
- Looker
- Executive dashboards
Reporting Design
Choosing the small number of metrics somebody acts on, and defining them precisely enough that two teams cannot compute them differently.
Then delivering them without anyone having to go and look: automated reports on a schedule, into the inbox or channel where the decision actually gets taken.
Includes- KPI design
- Automated reports
- Scheduled delivery
Self Service Analytics
A semantic layer over the warehouse, so a follow-up question does not need a ticket. That second question is usually where the real insight was.
Governed rather than open: consistent definitions and appropriate access, so self-service produces answers that agree with each other.
Includes- Data models
- Semantic layers
- Governed access
Insight & Visualization
Charts chosen to make a comparison legible rather than to look impressive. The right visualisation makes the conclusion obvious; the wrong one hides it in plain sight.
Cohort analysis and metric modelling show whether a trend is real or whether a total is being carried by one unusual month, which is the difference between a signal and a coincidence.
Includes- Data visualization
- Metric modeling
- Cohort analysis
Why it matters
A dashboard nobody opens is a report nobody reads with extra steps.
The measure of analytics is not how much is visible but how many decisions changed. That means starting from the decision (who makes it, how often, and what would make them act differently) and working backwards to the number, which is the opposite of how most reporting gets built.
Here’s what useful analytics returns:
It arrives without being fetched
Scheduled delivery into the inbox or channel where the decision gets made, so the reporting reaches people who were never going to log in to look.
Teams answer their own questions
A semantic layer and governed models mean the follow-up question does not need a ticket, which is usually where the real insight was hiding.
Everyone sees their own view
Role-based dashboards, so an executive gets the shape of the business and a team lead gets the handful of numbers they can personally move.
Trends, not just totals
Cohort analysis and metric modelling show whether something is genuinely improving or whether the total is being carried by one unusual month.
Charts that argue clearly
Visualisation chosen to make the comparison legible rather than to look impressive. The right chart makes the conclusion obvious; the wrong one hides it in plain sight.
Good analytics is not a wall of screens. It is a small number of things you check, trust, and change your mind because of.
Why Data & Analytics with Zefract
One team, one goal, nothing lost in the handoff.
The people designing your reporting are the same people who built the pipelines feeding it, so a number that looks wrong can be traced to its source in the same conversation rather than across two suppliers.
Dashboards nobody opens?
Start with a reporting reviewFAQ
Frequently asked questions
Usually because they were built to display what was available rather than to answer a question someone has. A screen with forty metrics offers no way to prioritise, so reading it takes more effort than the decision it was meant to inform. Fewer, well-chosen numbers get looked at.
The ones somebody would act differently because of. We start from the decision (who makes it, how often, and what would change their mind) and work backwards to the number. Metrics that nobody can influence are interesting rather than useful, and they crowd out the ones that are.
Largely a question of what your team already uses and what your data sits in. All three produce good results in capable hands. Switching tools rarely fixes a reporting problem, because the common causes are undefined metrics and untrustworthy data, and both follow you to the new platform.
That is the aim, through a governed semantic layer rather than open access to raw tables. Defined metrics and appropriate permissions mean people can answer their own follow-up questions, which is usually where the real insight was, without producing five versions of the same number.
Not always. If the numbers live in one system and are trusted, reporting can be built directly. The warehouse becomes necessary when questions span sources or when querying production starts affecting it. We will tell you honestly which situation you are in rather than selling the larger project.
Next step
Ready when you have a brief.Or half of one.
Send whatever you have. You get scope, a timeline and a number back within three working days.
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