Data Engineering & Analytics / BI

Service description

Data engineering and analytics builds the systems that collect data from a business's tools, clean it, store it in one place such as a warehouse, and present it in dashboards and reports. Work typically includes pipelines from source systems, a data model, quality checks, and business intelligence dashboards. The goal is reporting people trust without manual spreadsheets.

Common industries

Applies across industries with data spread over multiple systems.

ROI

Trustworthy data and self-serve dashboards speed decisions and cut manual reporting; foundational for anything analytics- or AI-driven.

Benefit

Build the pipelines, warehouse, and dashboards that turn scattered data into reliable reporting and analytics.

Why get it

Businesses engage it when reports are built by hand, numbers differ between systems, or leaders can't get timely answers from data spread across tools.

When you benefit

An initial build, followed by ongoing maintenance as sources and reporting needs change.

What it costs

Project fee or retainer.

When you pay

Providers typically quote a fixed fee for an initial build with defined sources and dashboards, or bill hourly or by retainer for ongoing work. Payment is usually tied to milestones or billed monthly.

Other costs

Data warehouse and BI tool subscriptions, cloud storage and compute charges, and connectors for source systems are usually separate from the provider's fee.

Risks to know

Dashboards built on unreliable data lead to wrong decisions and lost trust, so data quality checks matter as much as the visuals. Unclear metric definitions produce conflicting numbers, pipelines can break silently when a source system changes, and sensitive data needs access controls.

When risks arise

Data quality and definition problems appear when the first reports are compared with numbers the business already trusts. Pipeline failures and cost growth tend to appear later, as source systems change and data volumes grow.

The process

The provider inventories the data sources and agrees on the metrics and reports needed. It builds pipelines into a central store, models and checks the data, and builds dashboards. The business validates results against known figures, and the provider trains users and hands over documentation.

Your commitment

The business names the questions it needs answered, gives access to its source systems and data, and agrees on how key metrics are defined. A knowledgeable staff member should review outputs against known figures, and someone should own the data and dashboards after handover.

Documents to gather

• List of source systems and tools holding business data
• Existing reports or spreadsheets leaders rely on
• Definitions for key metrics such as revenue or customer count
• Data access and security policies

Helpful reading

• The Government Data Quality Framework — GOV.UK (Government Data Quality Hub)
• What is data quality? — GOV.UK (Government Data Quality Hub)