Services
Data & Analytics
Data pipelines, dashboards and forecasts that give every team one trusted view of the business.
Explore our capabilities
Your systems
- ERP
- CRM
- Web and app events
- Sensors and IoT
- Documents
- Dashboards
- Forecasts
- Reports
- APIs
Common challenges
Challenges we solve
Data you can't trust
Reports disagree because data is copied between systems without validation, and nobody owns data quality.
Reporting that's always behind
Analysts spend their time exporting and cleaning spreadsheets instead of analysing, so reports arrive too late to act on.
Data stuck in silos
Sales, finance and operations data sit in separate systems, so nobody sees the whole picture without stitching spreadsheets together.
Sensitive data without controls
Exports with customer and financial data are emailed around, with no record of who can see what.
How to get started
Three steps to a plan for Data & Analytics
- 1Tell us what you needUse the project form or book a call. A few sentences about the goal is enough to start.
- 2Free technical consultationWe go through your goals, users, existing systems and constraints with you.
- 3Your planA detailed plan covering the right tech stack, architecture, timeline and budget. Then you decide.
Where are you today?
From spreadsheets
to forecasts you act on
Choose the stage closest to where you are today to see the next step and what we deliver for it.
Stage 1 · Foundation
Can we trust our numbers?
- Reports are built by hand from system exports
- Teams quote different figures for the same metric
- Nobody owns data quality
The next step
Bring data from source systems into one warehouse with automated pipelines and data-quality checks.
What we deliver
- Data Engineering
Find your starting point
Which capability do I need?
Choose what you want to do to see the capability that delivers it and what usually comes first.
Table 1. What you want to do, and the capability that does it. Choose a row.
| Goal | Capability |
|---|---|
| Forecast demand, sales or workload | Predictive Analytics |
| Flag fraud, churn or risky cases | Predictive Analytics |
| Get one trusted set of numbers across teams | Data Engineering |
| Dashboards that people actually use | Visualization & Analytics |
Forecast demand, sales or workload
Forecasting models learn from history and drivers such as promotions, and give a range, not just a number.
Needed first
- Two or more years of history at the level you plan at
- Records of promotions, price changes and stock-outs
Overview
How we approach
Data & Analytics
Today's interconnected world generates vast amounts of data every second. Brought together and analyzed, that data reveals insights and trends that would be impossible to spot manually, and acting on them is vital to keep pace.
We build the pipelines and infrastructure to store, analyze and process large datasets efficiently, with an end-to-end approach covering data modeling, preparation, reporting and migration.
What we deliver
Choose a capability to see how it works, what you receive and where it applies.
- Predictive AnalyticsForecasts and risk scores that turn historical data into forward-looking decisions.Explore Predictive Analytics
- Data EngineeringAutomated pipelines and storage that give your teams up-to-the-minute, trustworthy data.Explore Data Engineering
- Visualization & AnalyticsDashboards and reports that make complex data easy to explore and act on in real time.Explore Visualization & Analytics
Deliverables
What you receive
- Data audit and prioritized use cases
- Automated data pipelines with data-quality checks
- Data models in a warehouse or lakehouse
- A semantic layer with each metric defined once
- Dashboards and self-service reporting
- Forecasts and risk scores tested against past data
How the work runs
From first workshop to handover
Assess
- Data audit and access review
- Decisions, metrics and success measures
- Feasibility and baseline
Prepare
- Pipelines and data modeling
- Cleaning and validation
- Data-quality checks
Build
- Semantic layer and metric definitions
- Dashboards and reports
- Forecasts and risk scores
Operate
- Release and training
- Monitoring data freshness and quality
- Ongoing improvements
How we deliver
Six phases, from question to production
Every data and analytics project we run moves through six phases, and each ends with something you can use. Choose a phase.
Phase 1 of 6
Discovery
What we do
- 1.1 Agree the business goal and the decision it supports
- 1.2 Review the current process and data
- 1.3 Define the success measures
- 1.4 Plan the project
You receive
A project plan with success measures
Data and analytics consulting
Your use cases, ranked
We score your use cases for value and feasibility, so you know what to build first and which foundations unlock the rest. Choose a point in this example.
Use case
Demand forecast for purchasing
Value
0.82
Feasibility
0.64
Direct effect on stock and cash; needs two years of clean history and promotion data.
Predictive AnalyticsGovernance and security
Controls built into every data project
What we build into every data platform, so the numbers stay right, secure and under your control. Choose a concern.
Table 3. Common risks, and the control we build in for each. Choose one.
| Concern | What it looks like |
|---|---|
| Numbers that don't match | The same metric showing different values in different reports. |
| Stale data | Dashboards showing yesterday's or last week's figures without saying so. |
| Data leaks | People seeing records they shouldn't, or data leaving your accounts. |
| Personal data | Customer or employee details spreading into reports and exports. |
| Broken pipelines | A source system changes and reports quietly go wrong. |
| Untraceable figures | Nobody can say where a number on a dashboard came from. |
| Rising cost | Warehouse and tool spend growing faster than use. |
What we build in
Numbers that don't match
Each metric defined once in a semantic layer, with tests that catch a break before anyone sees it.
Handover
Delivered with every data project
- A data dictionary and metric definitions
- Pipeline code and tests in your repository
- Access control and audit logging
- Monitoring dashboards and alerts
- Runbooks and documentation in your accounts
How to start
From first call to production
Start where you are. Each step ends with a decision, so you commit to the next one only when it makes sense.
Protocol
How an engagement runs
Each step ends with a decision on whether to continue
Free technical consultation
One or two sessions
Talk through the goal, the data you have and the systems involved.
Outputs
- (a) A shortlist of use cases, ranked by value and feasibility
- (b) A recommended next step
- (c) A plan covering stack, architecture, timeline and budget
By industry
Where it applies
What this service builds in each industry we serve.
RetailDemand forecasting and retail analyticsSell-through, margin and replenishment dashboards, and forecasting models that inform buying and allocation.
Financial ServicesFraud detection and credit risk modelsRisk scores for transaction monitoring, anomaly detection and credit scoring, with documented, explainable outputs.Regulatory reporting and data platformsPipelines and warehouses that consolidate transaction data for reporting, reconciliation and audit.
HealthcareClinical and operational dashboardsData pipelines and dashboards for capacity, patient throughput, referral leakage and quality measures.Predictive AnalyticsModels for no-show prediction, readmission risk and demand forecasting, using de-identified data where possible.
EducationLearning analytics and early alertsDashboards and models combining attendance, LMS activity and grades to flag students for advisor outreach.
Non-ProfitsImpact dashboardsReports that turn program and fundraising data into outcome metrics for funders, boards and annual reports.
MediaAudience analyticsFirst-party data pipelines and dashboards for engagement, churn and advertising performance.
ManufacturingPredictive maintenanceModels on vibration, temperature and runtime data that flag equipment likely to fail, so maintenance can be scheduled.Demand and production planningForecasting and scheduling tools that use order history and capacity to plan production.
Real EstateMarket and portfolio analyticsDashboards and valuation models for pricing, occupancy, rent rolls and portfolio performance.
LogisticsRoute and load optimizationPlanning tools that optimize routes and loads against time windows, vehicle capacity and hours of service.Operations analyticsDashboards for on-time delivery, cost per shipment, dwell time and carrier performance.
Technologies and standards
Chosen for your project
Built on the cloud you already use. Choose yours to see the services involved; we recommend the full stack in the free technical consultation.
Table 2. Managed services for each layer, by cloud. The highlighted column is the one you chose.
| Layer | AWS | Azure | Google Cloud |
|---|---|---|---|
| Storage and warehouse | Amazon S3, Amazon Redshift | OneLake, Microsoft Fabric | Cloud Storage, BigQuery |
| Pipelines | AWS Glue, Amazon Kinesis | Azure Data Factory, Event Hubs | Dataflow, Pub/Sub |
| Dashboards | Amazon QuickSight | Power BI | Looker |
| Governance | AWS Lake Formation | Microsoft Purview | Dataplex |
Also runs on any of the three: Snowflake, Databricks, dbt, Fivetran, Tableau.
Data platforms
- Snowflake
- Databricks
- Google BigQuery
- dbt
- Apache Airflow
- Apache Kafka
Analytics
- Power BI
- Tableau
- Looker
Forecasting and statistics
- Python
- R
- scikit-learn
- statsmodels
Data quality and governance
- Great Expectations
- dbt tests
- Microsoft Purview
- AWS Lake Formation
Questions
Common questions
about Data & Analytics
How do we know if our data is good enough to report on?
We check it in the free technical consultation and confirm it with a data audit of volume, quality, history and access. Where the data isn't ready, we start with Data Engineering.
Can we keep using Power BI, Tableau or Looker?
Yes. We build on the reporting tools your teams already use and connect them to one governed model of your data, so every report uses the same definitions.
Where does our data live?
In your own cloud account or warehouse. We build the pipelines and models there, and hand over access, code and documentation at the end.
Should we start with dashboards or forecasting?
We usually start with data and dashboards: reliable pipelines and reporting give the clean data, and the baseline, that forecasts are later measured against.
How is a project priced?
Well-defined scopes are delivered as fixed-price engagements; when requirements are still evolving, we provide a dedicated team instead. Either way, the free technical consultation ends with a plan covering tech stack, architecture, timeline and budget, so you know the cost before work starts.
Explore more
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Ready to talk about Data & Analytics?
Start with a free technical consultation: a detailed plan covering the right tech stack, architecture, timeline and budget for your project.
Start a project