Automated Price Prediction for a Large Retail Catalogue
Weekly automated price recommendations across 30,000+ products in 130+ locations, including products with little or no recent purchase history in that store.
Data Science and Analytics
Good data science turns what a business already has into something it can act on. That might mean clearer forecasts, better customer insight, or a more rigorous basis for decisions that currently rely on instinct.
We help you understand what your data contains, build the models and analytical capabilities to act on it, and develop the infrastructure to make that work repeatable rather than one-off.
Overview
Common ways data science and analytics are used
Most data science and analytics projects fall into one of the broad areas below. Each covers a set of specific capabilities listed further down the page.
Forecasting and quantitative decision support
Using historical data to inform decisions about the future. Demand, revenue, risk, customer behaviour. The deliverable is usually a model that produces numbers your team can plan with, paired with the analysis explaining how to read them.
Some examples
- Predictive modelling for business outcomes
- Sales and revenue forecasting
- Customer segmentation and lifetime value analysis
Exploration, statistics, and experimentation
Asking questions of data and answering them rigorously. Exploratory analysis when you do not yet know what is in the data, statistical methods to validate hypotheses, and experiment design for measurable change.
Some examples
- Exploratory data analysis and insight discovery
- Statistical analysis and A/B experimentation
- Anomaly detection in operational and financial data
Analytics infrastructure and custom development
When the bottleneck is the data itself rather than the analysis. Distributed processing, custom analytics development, and the pipelines that make insight repeatable across a large or growing data estate.
Some examples
- Big data analytics and processing
- Real-time analytics pipelines
- Custom ML development for analytical workloads
Our services
What this looks like in practice
Below are the specific capabilities and use cases that sit within those broad areas. Some span more than one. The list is not exhaustive. If your needs are different or more specific, just get in touch.
Working with us
How we work with you
Most data science and analytics work fits one of three modes. Scope and deliverables vary; the examples sketch what each typically involves.
Many engagements use more than one of these.
If it is not clear which of these fits, book a free intro call and we can work it out.
Is this for you?
Who this is for
Data science is most valuable when an organisation has accumulated data over time and wants to extract genuine insight from it, or when data-driven decision-making is a strategic priority but the capability to do it well does not yet exist in-house.
This typically applies to businesses with historical transaction data, customer records, operational logs, or any structured data that reflects how the business actually runs. The data does not need to be perfect. Working with imperfect data is part of the job.
We work with teams at all stages: businesses taking their first steps with data science, those scaling an existing capability, and those who need independent review of analysis or models already in production.
When something else fits better
Data science, AI and machine learning, and NLP and generative AI all overlap, and many engagements draw on more than one. The starting point on the site usually maps to one of the following:
- A predictive system that runs continuously in production, with deployment, monitoring, and integration: AI & Machine Learning
- Anything where the data is text, documents, or conversation: NLP & Generative AI
- Sizing up where data and AI fit at all, without a specific question yet: Getting Started with AI
In practice
See it in practice
A selection of engagements that involved this type of work.
FAQ
Common questions
How is data science and analytics consulting different from AI and machine learning consulting?
The fields overlap. Data science and analytics emphasises statistical analysis, insight discovery, and experimentation. AI and machine learning consulting focuses more on building predictive systems and automating decisions. In practice a project often involves both, and we will tell you which approach best fits what you are trying to achieve.
Does our data need to be clean and well-structured before we start?
No. Imperfect, messy, or partially structured data is the norm. Exploratory analysis and data quality assessment are often how engagements begin. Where significant data work is needed before modelling becomes useful, we will say so clearly upfront.
Will we own the models and analysis produced?
Yes. Everything we build is yours. We document thoroughly, hand over all code and model artefacts, and structure the engagement so your team understands what was built and can operate it going forward.
Can you build on internal analysis or models we already have?
Yes. We are comfortable picking up existing work, reviewing and extending models, and helping teams mature from ad hoc analysis to production-ready systems.
Can you work with data in our existing tools and platforms?
Yes. We work with common data platforms, cloud environments, and databases. If your data lives in a data warehouse, CRM, ERP, or proprietary system, we assess access and integration requirements early in the engagement.
Let's Talk
Need clearer answers from your data?
Let’s talk about the question, the records you have and what analysis would actually help.