Data Science services
Answers from the data you already have.
Before anyone builds a model, most businesses have a simpler problem: nobody can answer a straight question from their own numbers. Which customers come back, which product actually makes money, what the last quarter really looked like. That is usually where the value is.
Overview
Data Science, the way we do it
Most companies are sitting on years of data in systems that were never designed to be asked questions. The reports take an hour to run, two of them disagree, and the one person who understands the spreadsheet is on holiday. None of that needs machine learning to fix.
We get the data into one place you can trust, build the reporting that answers the questions you actually ask, and say plainly which questions your data cannot answer yet. Often that is the most useful thing in the engagement.
What's included
What you actually get
- Your data pulled into one place, reconciled, and trustworthy
- The questions you actually ask, answered without a person in the loop
- Reporting your team can read without a data analyst beside them
- A straight account of which questions the data cannot answer yet
- Built on the databases you already run, not a new platform to buy
How we work
Five steps, no surprises
- 01
We talk
A call or a WhatsApp thread. You tell us what is not working; we tell you honestly whether we are the right people.
- 02
We scope it
A written plan with what you get, what it costs and how long it takes. Fixed, so there are no surprises later.
- 03
We build it
You see it as it goes, not at the end. Changes are cheap while it is still being built.
- 04
We put it live
On infrastructure we set up and secure, tested before the launch date.
- 05
We keep it running
Updates, monitoring and someone who answers. Most clients stay on a monthly agreement.
Questions
About data science
Is this different from a BI dashboard?
It usually starts before one. A dashboard on unreconciled data just makes wrong numbers easier to look at. The work is often getting the data right first, and then the dashboard is straightforward.
Do we need a data warehouse?
Frequently not. For most businesses of this size, a well-designed database and honest reporting go a very long way, and we will say so rather than selling you a platform.
Has BitBee delivered this?
Not as a named project on this site, though the database and reporting work underneath it is what we do constantly. No case study here is a data science engagement.
Also
Related work
- Quality AssuranceFind it before your customers do.
- UX DesignWork out what it should do before anyone builds it.
- UI DesignScreens people can use without being trained.
- Design SystemsOne set of rules, so the tenth screen looks like the first.
- Mobile ApplicationsApps people keep on the first screen.
- iOSiPhone apps built the way Apple expects.
- AndroidAndroid apps that work on the phones people actually own.
- FlutterFlutter, when one team has to cover both stores.
- AIAI that does one job, properly.
- Machine LearningModels that earn their place in the business.
- LLMsLanguage models, kept on a short leash.
- Generative AIGeneration with a human still holding the pen.
- PythonPython, for the work that has to be read as well as run.
- Back-EndWhere an order becomes an order.
- DatabaseThe part of your system that is hardest to fix later.
- Node.jsNode.js, for the systems that have to answer quickly.
- GoGo, where it has to be fast and stay simple.
- .NET.NET, for the systems a business runs on.
- JavaJava, for systems measured in decades.
- Front-EndThe half of your product people actually see.
- Web DevelopmentCompany sites, customer portals, and web systems that hold up.
- ReactReact, built so the next team can still work on it.
- AngularAngular, for systems that have to last.
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