Clear, owned outputs.
- Analysis plan for each question
- Analysis notebooks or workbooks
- Findings report with recommendations
- Presentation to stakeholders
- Reusable queries and datasets
Data analytics answers specific business questions with evidence: why sales dropped in a region, which customers are likely to leave, where operations lose time. We combine solid data work with clear communication, so the output is a recommendation people can act on, not just a chart.
The problems Data Analytics solves.
Exploratory, diagnostic and customer analytics turned into clear recommendations.
Important questions go unanswered because nobody has time to analyse.
Analysis describes what happened but not why.
Findings are presented in ways decision-makers struggle to use.
Experiments are run without proper design.
What Data Analytics covers.
Exploratory analysis
Understanding patterns, trends and anomalies in your data.
Diagnostic analysis
Finding drivers behind changes in performance.
Customer analytics
Segmentation, retention, lifetime value and behaviour.
Operational analytics
Efficiency, throughput, quality and cost analysis.
Experimentation
Designing and analysing tests properly.
Storytelling
Clear write-ups and presentations with recommendations.
Analysis that answers a specific question.
Good analysis starts with a decision someone needs to make, not with the data that happens to be available.
- Descriptive
- What happened, segmented properly: by cohort, channel, region or product line, with seasonality taken into account.
- Diagnostic
- Why it happened: funnel breakdowns, contribution analysis and controlled comparisons rather than correlation alone.
- Experiment analysis
- A/B test design, sample sizes and readouts that account for multiple comparisons and novelty effects.
- Forecasting
- Baseline forecasts for demand or revenue, with the uncertainty shown rather than a single confident line.
- Communicating results
- A short written summary of the finding, the evidence and the recommended action, with the method in an appendix.
Why it's worth it.
Evidence-based decisions
Choices backed by data rather than assumptions.
Understanding causes
Know why things changed, not just that they did.
Actionable output
Recommendations people can take forward.
When Data Analytics is the right move.
- Understanding a drop in conversion or sales.
- Customer segmentation for marketing and product.
- Finding bottlenecks in operations.
Technologies & platforms
- SQL
- Python
- pandas
- Jupyter
- R
- BigQuery
- Snowflake
- Power BI
How Data Analytics work runs.
- 01
Discover
Sources, consumers, data quality, volumes and the decisions the data must support.
- 02
Design
Architecture, data model, pipelines and governance agreed in writing.
- 03
Build
Pipelines, models and tests delivered in increments.
- 04
Validate
Reconciliation against source systems and sign-off by data owners.
- 05
Operate
Monitoring, data quality alerts, documentation and handover.
Data Analytics questions.
Do we need a data warehouse first?
Not always. Analysis can start with exports or existing databases. If the same questions come up repeatedly, a warehouse becomes worth it.
How is analytics different from BI?
BI provides ongoing reporting on known measures. Analytics digs into specific questions, often leading to new measures or actions.
Can analytics lead to predictive models?
Yes. When analysis shows that predicting something would help, our AI/ML team can build and deploy models.
Talk to us about data analytics.
Describe the environment and what needs to change. We'll reply with the questions we would answer first and a sensible first step.