Data Analytics & Machine Learning
Which products sell best? When will demand rise? Where are costs changing? We start with the decision your business needs to make and assess the data available. Our Data Science work can include reporting, business intelligence and machine learning, with the approach chosen to fit the question, data quality and budget.
What we deliver
Dashboards & business intelligence
Bring sales, costs, bookings or stock into clear reports with agreed metrics, filters and update schedules.
Data preparation & integration
Combine spreadsheets, databases, CRM and ERP data; check missing values, duplicates and inconsistent definitions before analysis.
Sales & demand forecasting
Use suitable historical data to estimate demand for products, bookings or staffing, with uncertainty and seasonal patterns made visible.
Customer segmentation
Explore customer groups by purchase frequency, value or behaviour to support relevant campaigns and service planning.
Anomaly detection
Flag unusual changes in sales, expenses or operational data for your team to review, with thresholds adapted to the business.
Model evaluation & monitoring
Compare models with a simple baseline, test on unseen data and monitor performance and data changes after deployment.
A useful answer starts with a clear question
A shop may need a weekly view of its best-selling products. A hotel may need to plan for seasonal demand. A service business may want to understand repeat customers. We choose the simplest approach that supports that decision, and assess whether machine learning adds enough value to justify its cost.
- A business question and an agreed measure of success
- A review of data quality, history and permitted use
- A report or simple baseline before a more complex model
- Documented assumptions, limitations and ownership
From a business question to a tested solution
Each stage gives us evidence for the next investment.
If the data is not suitable for machine learning, the first deliverable can be a dashboard or a plan to improve data collection.
- 1
Define the decision
Agree what the team needs to decide, how the result will be used and which metric would make the work useful.
- 2
Assess the data
Review relevant sources, access permissions, coverage and quality. Identify gaps and prepare a feasible scope.
- 3
Build and compare
Start with a report, rule or simple forecast. Where justified, compare a model against it on held-out data, using time-based testing for forecasts.
- 4
Put it to work
Connect the agreed output to a dashboard or workflow, document its limits and set a monitoring and review plan.
Business value
A shared view of performance
Defined metrics and traceable sources give the team a common basis for discussion.
Better-informed planning
Patterns and forecasts help inform stock, staffing and campaign decisions.
Focused investigation
Reports and alerts help the team find changes that deserve a closer look.
Evidence before expansion
A scoped pilot shows whether the approach is useful before a wider rollout.
Frequently asked questions
Do we need a large dataset to get started?
Not necessarily. A useful dashboard can start with a few well-structured sources. Machine learning needs enough relevant examples and, for forecasting, sufficient history. We assess suitability before proposing a model; there is no single minimum that fits every problem.
Can we start with Excel files or our existing software?
Yes. We can assess spreadsheets, database exports and data from your existing systems. Automated connections depend on the access and interfaces those systems provide.
Does every analytics project need machine learning?
No. A clear report, a business rule or a simple statistical forecast may be enough. We consider machine learning when the data and evaluation show a useful improvement over that baseline.
How do you assess whether a model is useful?
We agree a relevant metric, compare against a simple baseline and test on data that was not used for training. For forecasts, we preserve time order. We discuss errors, uncertainty and operating costs with your team, then agree monitoring if the model is deployed. Accuracy and business results depend on the data and use case.
Software • AI • Automation
Let’s talk about your project
Share a few details and we’ll get back to you with next steps — no obligation.