Data Science

Data doesn't come with the answer built in. Someone still has to know what to ask.

Most organisations sit on more data than they ever get an answer from. Some questions just need proper analysis: working through what you've already got to find out what's actually going on. Some need a solution applying, a model or system that keeps producing outputs. Our Data Science service does both, starting with the question, not a default answer.

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Problem
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CEOs/CTOs

Why does this matter and what strategic job does it do for me?

Most organisations have more questions about their business than answers in their data. Why a segment of customers stopped buying. Whether a process change actually worked, or just felt like it did. What's really driving a cost or a delay somewhere in the business. The data to answer questions like these is usually already there. What's missing is the time, the statistical skill, or a clear place to start looking. So the question doesn't get answered. It gets guessed at, and the guess becomes the decision.

Solution

Answers built from your questions, not a default response

We start with the question, not a method we default to regardless of fit. Sometimes that means proper analysis: working through the data you already have to find a clear, specific answer. Sometimes it means something built or put in place, a model, a system, or the right off-the-shelf tool properly implemented, so the answer keeps coming long after the project ends. Whichever it is, the decision comes from what your data and your business actually need, not from what we'd rather sell you.

Who this is for

Organisations with data and no way to interrogate it

You've got years of transaction history, support logs, sensor readings or transcripts, and a strong suspicion there's something useful in there. Nobody's had the analytical skill or the time to go and find it, so it just sits there, collected but unused.

Organisations ready to put something into place

You already know the answer needs to be built into something ongoing, not a one-off report. That might mean a bespoke model, or it might mean implementing an off-the-shelf tool properly, whichever actually fits your data and your question. What you need is a straight answer on which one that is, and someone who can deliver either.

The challenges we help organisations solve

The challenges we help organisations solve

Across both starting points, the challenges are consistent:

  • Questions that could be answered from data you already have, but aren't, because nobody has the time or statistical skill to go and find the answer

  • Decisions made on gut feel or whoever argues loudest, because getting a proper answer from the data takes too long, or nobody trusts it enough to settle the argument

  • Data spread across systems and spreadsheets, so even a simple question means hours of manual digging before anyone can answer it

  • No visibility of what's coming, only what's already happened, so problems get spotted after the fact rather than before it

  • Manual processes, scheduling, triage, monitoring, that could run algorithmically instead, so time and capacity go into decisions a system could make in seconds

  • Sensor or IoT data streams too large and fast for a person to check by hand, so anomalies go unnoticed until they've already cost time or money

What data science covers

Data analysis and insight

Sometimes the most valuable thing we can do with your data isn't build anything at all. It's proper analysis: working through what you've already got to answer a specific question, why something happened, what's really driving a number, which factors matter and which don't. A clear answer, not a black box.

Predictive modelling

Models trained on your historical data to forecast outcomes, whether that's demand, churn, risk or another metric specific to your business, so you're planning ahead instead of finding out too late. Built to your data, not a generic training set.

Natural language processing

Extracting structure from unstructured text: support tickets, chat transcripts, reviews, documents. Key phrase extraction, entity recognition and classification, trained to understand the specific language and context of your domain rather than a general-purpose vocabulary.

Optimisation

Algorithmic approaches to processes currently run on manual judgement, such as scheduling, resource allocation or routing, freeing up the time and capacity currently spent on decisions a model can make faster. Designed to work within your existing systems rather than replacing them.

Anomaly detection

Machine learning models that monitor high-volume data streams, sensor readings or transaction data in real time, catching what manual checks would miss or catch too late. Flagging what's genuinely unusual rather than burying it in noise.

What sets The Curve apart

We start with the question and work out what actually answers it, not the other way round. Sometimes that's a piece of analysis you can act on this week. Sometimes it's a model or system built to keep working long after we've left. And when something does need building, we're not selling a single tool, so we'll tell you plainly when off-the-shelf is the better answer, and when bespoke actually earns its place.

Because we also build software and engineer data platforms, a model doesn't stop at a proof of concept in a notebook. We take it into production, inside the systems you already run, on a foundation solid enough to support whatever you build next, which is where most pure data science work quietly stalls.

Our case studies

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Reimagining Service Triage Across Shelter’s National Support Services
How The Curve helped Shelter explore new ways to prioritise vulnerable users, coordinate support services and protect frontline advisory capacity under growing demand pressure
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Enhancing Scheduling Efficiency for Doctor Care Anywhere
Optimising appointment scheduling for 15 and 20 minute durations using an efficient algorithm.
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Urban Flows Observatory - Air Quality Data Visualisation Portal
Building a responsive portal to provide an accessible means for comparing and analysing data of the air quality sensors.
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Taking over a critical employee benefits platform and rebuilding it as: Aspire Hub
Taking over a critical employee benefits platform and rebuilding it as Aspire Hub, giving Foster Denovo's employer clients and their staff a modern, mobile-first benefits experience.
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