E-commerce
Without it
Platform ROAS, store revenue and bank deposits that never agree.
With it
One revenue definition, net of returns and COD failures, with spend joined against it by channel.
Blended profitability by channel becomes visible weekly.
BI dashboard development where ad spend and revenue finally meet
Every business past a certain size has the same meeting: marketing's number, sales' number and finance's number, none of which agree, and an hour spent reconciling instead of deciding. The fix is not a better spreadsheet. It is one place where the definitions are agreed and applied consistently.
The short answer
Business intelligence work consolidates data from ad platforms, CRM, your product and your accounting system into one warehouse, with agreed metric definitions, and presents it as dashboards. The purpose is that everyone in the meeting is looking at the same number.
0
Definition per metric
Written down, agreed, and applied everywhere. Most reporting disputes are definition disputes in disguise.
0
Manual report assembly
Dashboards refresh themselves. A report that needs a person to build it is a report that arrives late.
0
Sources joined, typically
Ad platforms, CRM, product or store, and accounting — the minimum to see spend through to revenue.
0h
Data freshness
Daily refresh for most businesses. Real-time is rarely needed and always more expensive.
- 1Decisions first
Decision and metric list
- 2Define the metrics
Metric dictionary
- 3Build pipelines
Running data pipelines
- 4Model
Modelled warehouse layer
- 5Visualise
Live dashboards
- 6Adopt
Dashboards in weekly use
The problem
Three numbers, one meeting, no decision
Marketing reports leads from the ad platform. Sales reports opportunities from the CRM. Finance reports revenue from the accounting system. All three are correct within their own system and none of them can be joined, so the meeting becomes an argument about arithmetic.
- 01The monthly report takes a person two days to assemble from exports.
- 02Two departments define 'qualified lead' differently and neither knows it.
- 03Nobody can attribute closed revenue to a campaign without a manual reconstruction.
- 04Dashboards exist but nobody trusts them, so decisions are made from a spreadsheet instead.
- 05By the time the report circulates, it describes a month that has already ended.
Why businesses need it
What a single source of truth is worth
The value is not the chart. It is the meeting that stops being about the data.
Lead-generation businesses
Without it
Cost per lead reported by marketing and closed revenue reported by sales, never in the same table.
With it
Attribution carried through the CRM to closed revenue, reported by campaign and creative.
Budget decisions are made on revenue rather than on lead volume.
SaaS
Without it
MRR, churn and CAC calculated differently by every person who has ever produced a board deck.
With it
Agreed definitions in the warehouse, with cohort retention and payback computed the same way every month.
Board reporting stops being reconstructed each quarter.
Multi-location businesses
Without it
Branch performance in eleven separate systems with no comparison.
With it
Consolidated reporting per location, normalised so branches are genuinely comparable.
Underperforming locations are visible in weeks rather than at year end.
Agencies
Without it
Client reporting assembled by hand across dozens of accounts every month.
With it
Automated client dashboards from one pipeline, branded per client.
Reporting days become reporting minutes.
Manufacturing & distribution
Without it
Production, inventory and sales data in an ERP nobody can report from usefully.
With it
A warehouse alongside the ERP, with dashboards that do not require an ERP consultant to change.
Operational questions get answered the same day they are asked.
Why software, not headcount
Why definitions matter more than tools
Dashboard projects fail on governance rather than on technology. The tooling is largely solved; the agreement is not.
One definition per metric, written down
What counts as a lead, when revenue is recognised, whether returns are netted. Most disagreements about numbers are disagreements about definitions that nobody has articulated.
Model once, report many times
Transformations belong in the warehouse, not in each dashboard. Otherwise every chart implements its own slightly different version of the same calculation.
Automate the refresh
A dashboard that requires someone to update it is a report. Reports are late by construction; dashboards are not.
Build for the decision
Start from the decisions the business makes weekly and build the smallest thing that informs them. Dashboards built to display everything get looked at once.
Scope
What a data & bi dashboards engagement includes
01
Metric dictionary
Every metric that matters, defined in a sentence, agreed by the people who use it. The most valuable and least exciting deliverable here.
02
Data pipelines
Automated extraction from ad platforms, CRM, store, product and accounting systems, with failure alerting.
03
Warehouse
A modelled layer where the joins and transformations live once, so every dashboard reads the same numbers.
04
Dashboards
Executive, marketing, sales and operations views, each built around the decisions that audience actually makes.
05
Attribution model
Spend joined to leads joined to closed revenue, with the model's limitations stated rather than hidden.
06
Automated distribution
Scheduled summaries to email or Slack, so the numbers reach people who will not open a dashboard.
07
Alerting
Thresholds on the metrics that matter, so a problem announces itself rather than waiting for the monthly review.
08
Documentation and training
How the data flows, what each metric means, and how to add a chart without breaking anything.
In depth
BI dashboard development India: how it works and what it is worth
Every business past a certain size has the same meeting: marketing's number, sales' number and finance's number, none of which agree, and an hour spent reconciling instead of deciding.
The problem is definitions, not tools
Dashboard projects fail on governance rather than technology. What counts as a lead, when revenue is recognised, whether returns are netted — most disagreements about numbers are disagreements about definitions nobody has articulated. Write the definitions down before building anything, and get the people who use them to agree.
Build for the decision
Start from the decisions the business makes weekly and build the smallest thing that informs them. Dashboards built to display everything get opened once, admired, and never used to decide anything. The structure that works is in marketing dashboard setup.
Do you need a warehouse
- One or two sources — a direct connection into Looker Studio is fine and much cheaper.
- Four or more sources, or transformations complex enough that each dashboard would implement its own version — build the warehouse.
- Model once, report many times. Joins and transformations belong in the warehouse, not repeated in every chart.
Daily is fresh enough
For almost every business, a daily refresh matches the decisions anyone actually makes. Hourly encourages reacting to noise and real-time is considerably more expensive — worth it far less often than people expect when they ask for it.
This only works downstream of trustworthy tracking
A dashboard cannot fix a conversion event that counts page views. If the underlying measurement is wrong, a BI project produces a faster, prettier route to the wrong conclusion — which is why we usually do Conversion & Analytics first and the dashboard afterwards.
How it runs
Our data & bi dashboards process, week by week
Every stage ends in something you can hold — a document, a build, a live account. If a stage cannot name its output, it is a meeting, not a stage.
01
Decisions first
What decisions are made weekly and monthly, and what would need to be visible to make them well.
Output: Decision and metric list
02
Define the metrics
Agree definitions across departments and write them down before building anything.
Output: Metric dictionary
03
Build pipelines
Automated extraction from each source, with monitoring and failure alerts.
Output: Running data pipelines
04
Model
Joins and transformations in the warehouse, tested against known figures from each source system.
Output: Modelled warehouse layer
05
Visualise
Dashboards per audience, built around decisions rather than around available fields.
Output: Live dashboards
06
Adopt
Training, scheduled distribution and a review after a month of real use.
Output: Dashboards in weekly use
Included, not invoiced
Included free with every data & bi dashboards engagement
Everything here is part of the engagement at no extra cost. We do not itemise them on an invoice and we do not withhold them if you leave.
✓
A metric dictionary
Every metric that matters, defined in a sentence and agreed across departments. The least exciting and most valuable thing we produce, and it is yours.
✓
Dashboards that refresh themselves
No one assembles a report by hand. A report that needs a person is late by construction.
✓
A scheduled summary to email or Slack
The top numbers with their comparisons, pushed to people who will never open a dashboard link.
✓
Alerting on the metrics that matter
So a problem announces itself instead of waiting for the monthly review to discover it.
✓
Documentation of how the data flows
What comes from where, what each metric means, and how to add a chart without breaking anything.
Guides
Go deeper
Longer answers to the questions people ask before they hire anyone for data & bi dashboards.
Free tools
Use these before you hire anyone
Built by us, free, no signup, nothing uploaded to a server. Take them whether or not you ever become a client.
Proof
Where we have done this
Further reading
Written on this, at length
Sold alongside
What usually comes with it
Data & BI Dashboards questions
A marketing dashboard joining ad platforms, GA4 and your CRM is a defined project. A warehouse-backed build across marketing, sales, product and finance is larger, and is only worth it once you are joining four or more sources. Ongoing analysis is monthly. The metric dictionary comes with all of them.
Want a straight answer on data & bi dashboards?
Tell us what you have now and what you are trying to reach. We will audit it and tell you what we would do, what it would cost and whether you need us at all. The audit is free and yours to keep.