Data Analytics Consulting That Starts With the Question, Not the Tool
Most businesses do not have a data problem; they have a definitions problem. "Revenue", "active customer" and "conversion" mean different things in different tools, so every report starts an argument instead of ending one.
We start with the decisions you need to make, define the handful of numbers that inform them, and build the cleaning, joins and reports that produce those numbers the same way every time. Dashboards, warehouses and automation are tools for that, not the goal.
This is for you if
- ✓Two reports show two different revenue numbers and nobody knows which is right
- ✓Your data sits in a CRM, a store, ad accounts and spreadsheets with no joined-up view
- ✓You want to know which customers, products or channels actually make money
- ✓Someone spends days every month building the same report by hand
This is probably not for you if
- ✕You want a dashboard for its own sake — if no decision changes because of it, it will be ignored within a month
We would rather tell you now than take the project and disappoint you.
Worth knowing: A metric nobody trusts is worse than no metric, because people quietly go back to gut feel while the dashboard keeps running. The cheapest analytics win is usually writing down what each number means, where it comes from and who owns it. It takes a day and settles most report arguments.
What you get
Metric definitions
Each key number written down: what it means, how it is calculated, which system it comes from, and who owns it.
A data audit
What you collect, where it lives, how complete it is, and where it disagrees with itself.
Cleaning and joining
Customers, orders and campaigns matched across systems, so you can follow a customer from first click to repeat order.
Analysis that answers something
Profitability by customer, product or channel, retention and cohort analysis, and forecasts from your own history.
Reports that refresh themselves
The weekly or monthly report rebuilt to update automatically, from SQL you can read.
A written findings report
What the data says, how confident we are, and what we would do about it.
Questions worth answering first
- Which marketing channels bring customers who stay, not just customers who click?
- Which products or services carry the margin once returns, discounts and support time are counted?
- Where do customers drop out between enquiry and sale, and what does each drop cost?
- What will next quarter look like if the last eight quarters are any guide?
- Which customers are about to leave, and what do they have in common?
Tools we use
Chosen to fit what you already run. If your team lives in Excel or Power BI, the work ends up there.
Analysis
SQL, Python, pandas and DuckDB
Warehouses
BigQuery, Postgres, Snowflake, ClickHouse
BI tools
Looker Studio, Power BI, Metabase
Spreadsheets
Google Sheets and Excel, where the team already works
Sources
GA4, Search Console, Stripe, Shopify, CRMs, ad platforms
Automation
Scheduled queries, n8n, Python jobs
Not sure whether analytics help is what you actually need? Send over what you are trying to fix and we will tell you straight — including if a smaller job would do it.
Ask us first →How it works
Decision workshop
An hour on what you need to decide and which numbers would change the decision.
Data audit and quote
We look at the sources and quote a fixed price for the analysis and reporting.
Clean, join, analyze
The data prepared and the questions answered, with work in progress shared as we go.
Findings and handover
A written report, the queries behind it, and automatic reports where they are useful.
Work you can check
Real work that uses the same skills. Every card links to something you can open and inspect yourself.
Our case studies
The clicks and impressions on our case studies come straight from Google Search Console, and numbers too small to mean anything are left off.
Collected and checked dataAnswering services compared
Every provider's prices read from its own website, turned into comparable plans, and dated.
Questions
How much does data analytics consulting cost?
A focused engagement answering a specific question starts at $2,000. Setting up metric definitions, cleaned data and automated reporting across a business usually runs $5,000–$15,000. The quote is fixed after a short look at your data.
Do we need a data warehouse first?
Often not. Many businesses get the answers they need from exports and a well-built spreadsheet or a small database. A warehouse makes sense once you are joining several sources every day, or the data has outgrown spreadsheets.
Which tools will we have to buy?
Usually none. We work in the tools you already have where possible. If something new is needed, it is typically free or cheap at small-business scale, such as Looker Studio, Metabase or BigQuery's free tier.
Can you work with sensitive data?
Yes, under an NDA, with access limited to what the analysis needs, personal details removed or masked where they are not needed, and our copies deleted when the work ends.
What do we get at the end?
A written report with the answers and how confident we are in them, the SQL or scripts that produced them so the work can be repeated, and automated reports for anything you need regularly.
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