Data Science & Analytics
Most businesses do not have a data problem. They have a data-in-six-places problem. Analytics work is mostly plumbing: getting numbers into one place, agreeing what each one means, and putting them somewhere people actually look.
How we approach it
Start from the decision
Which decision is being made badly for lack of a number? Dashboards built without that question get opened twice.
Agree the definitions
Two departments counting "active customer" differently is a business problem before it is a technical one. It gets settled in writing.
Build the pipeline to be boring
Scheduled, monitored, and loud when it fails. Silent pipelines are how organisations end up trusting stale numbers.
Put it where the work happens
The best dashboard is the one already open. Often that means Slack or email, not another login.
Common questions
What is the difference between analytics and business intelligence?
Business intelligence reports what happened — revenue, volume, conversion. Analytics asks why, and what is likely next. Most organisations need BI working reliably first; analysis on numbers nobody trusts convinces no one.
Do we need a data warehouse?
If your reporting lives in one system, no. The moment you are joining data across two or more sources — say a CRM and a billing system — a warehouse stops being overhead and starts being the only sane way to keep the numbers consistent.
Can you work with our existing tools?
Usually. Most stacks already have a database, a CRM and a spreadsheet habit. We would rather connect what you have than migrate you onto something new that gives the same answer.
Often paired with
Talk it through
Tell us what you're trying to do and we'll tell you honestly whether this is the right way to do it.