Use case · Data & BI
Data pipelines and Power BI dashboards
How to replace manual exports and spreadsheets with a nightly pipeline, one data warehouse and dashboards built around the questions each team asks.
Collected overnight
Tills, web shop and warehouse data pulled in automatically, with nobody exporting anything.
Checked before it is shown
Duplicates removed, names matched, and bad rows held back and flagged.
One source of truth
Sales, deliveries, returns and customers linked by order in one warehouse.
One screen per question
Each dashboard answers what one team checks first thing in the morning.
Who it's for
Retailers with stores and a web shop
One picture of sales, deliveries and returns across every channel.
Finance teams
Margins and profit per customer without a week of spreadsheet work.
Operations and logistics
Late orders and the carrier or warehouse behind them, before customers call.
Leadership
The same numbers for everyone, fresh before the working day starts.
- 1
Collect
From every system that holds a number.
- 2
Check
Quality checks, with alerts when a source is late or looks wrong.
- 3
Organise
A star schema around orders in a central database.
- 4
Prepare
Data marts with ready-made answers, so dashboards stay fast.
- 5
Refresh
Dashboards updated before the first store opens.
The details thatdecide adoption.
Ask each team what they check first every morning, then build one screen per answer.
A wrong number in a dashboard costs more trust than a late one.
Then the next question is a new view on existing data, not another project.
With returns, delivery and discounts next to sales, profit per customer appears for the first time.
Questions to answer before the first dashboard
Retail Insights started with questions, not charts. Each team said what it checks first every morning, and each answer became one screen.
- What do you check first every day?. That question becomes the first dashboard.
- Where does each number live?. Tills, web shop, warehouse, finance system, and who owns each one.
- Which report do people actually trust?. The one they argue about least is the one to rebuild first.
- What should never reach a dashboard?. Duplicates, unmatched names and late batches, held back and flagged.
- Who needs to see what?. Store managers see their own store first; finance sees everything.
What it coststo build.
Published price ranges, fixed before work starts. Use the estimator for your own project, or see all pricing →
| Option | Price | Timeline |
|---|---|---|
| Single Power BI dashboard | From $1,900 | 1–3 weeks |
| Departmental suite on a shared data model | From $4,900 | Fixed price |
| Full pipeline and data warehouse | Quoted per scope | Quoted per scope |
One AI feature, live in your business.
Step 2 · Add partsPrices from our published list. We confirm the final, fixed price on one call. Running costs such as AI models, hosting and phone minutes are paid to those providers directly.
Common questions
Which tools did you use?
Apache Airflow for the nightly pipeline, PostgreSQL as the warehouse with a star schema around orders, data marts for sales, products, customers and stores, and Power BI on Azure for the dashboards.
How fresh is the data?
In Retail Insights the pipeline runs overnight from 02:00 and the dashboards are refreshed by 06:00, before the first store opens.
What happens when a source sends bad data?
Duplicates are removed, store and product names are matched, and anything that looks wrong is held back and flagged instead of reaching a report. The team gets an alert when a source is late.
Do we need a new project for every new question?
No. Because all the data already sits in one organised warehouse, a new question such as margins by supplier is a new view, not another round of exports.