Solutions
Data Infrastructure
Automation, AI and real-time reporting all depend on one thing: data you can trust. When information is spread across ERP systems, machines, cloud apps and spreadsheets, teams spend their time finding, fixing and reconciling data instead of using it. We build scalable, reliable data foundations, with clean, unified and governed data that lets your organisation operate with confidence.
What is Data Infrastructure
Data infrastructure is the foundation that lets data move reliably between the systems, people and processes that need it. It covers how data is captured, connected, cleansed and governed, so the right information reaches the right people without anyone pulling it together by hand.
It is also the first step in many of our engagements. A single source of truth makes automation more effective, and it is a prerequisite for reliable AI and genuine real-time intelligence. Without it, dashboards and AI tools have nothing solid to draw from.
We work with manufacturers, suppliers and logistics companies from consultancy through to implementation, system integration and ongoing support. Because data infrastructure sits alongside our Document AI, Process Automation and Real-Time Intelligence work, the data we connect doesn't stop at a dashboard. It feeds the workflows and AI tools that act on it.
Why this matters now
Most organisations didn't design their data landscape. They accumulated it. The cost shows up everywhere: reports that take days to prepare, automation that stalls on inconsistent inputs, and AI projects that struggle because the data underneath isn't fit for purpose. Three problems show up again and again.
Talk to an expertInformation silos
Critical knowledge is spread across different systems, teams and repositories, so people spend far too much time searching for the information they need.
Data integrity issues
Nobody has full confidence in the numbers, because there is no single source of truth to check them against.
Integration challenges
Legacy platforms, cloud apps and departmental tools have built up over time, and none of them talk to each other cleanly. The result is duplication, delays and high manual effort just to keep day-to-day work moving.
How Data Infrastructure works
- 01
Map your data landscape
We start by mapping how information moves today across ERP and non-ERP systems, machines, cloud apps and spreadsheets. This shows where data originates, where it is duplicated and where manual workarounds fill the gaps.
- 02
Build robust data pipelines
We connect your sources with automated data ingestion, so data flows reliably without anyone extracting, copying or re-keying it. That includes ERP and finance data, operational systems and machine data from the shop floor.
- 03
Cleanse and govern it in a central data layer
Raw data is brought into one place, then standardised, validated and cleansed. Quality issues are identified and fixed at the source, and governance rules keep the data accurate as volumes grow.
- 04
Translate data into something meaningful
We model your data around how your business actually works, with agreed definitions for the measures that matter. This becomes your single source of truth.
- 05
Deliver live insights and a foundation for what's next
Clean, connected data feeds live dashboards and reports that show what is happening now. It also becomes the reliable base for process automation and AI.
Data Infrastructure in practice
A reliable data foundation is what makes everything built on top of it work. Many of our engagements start with a focused proof of concept that maps your data flows, tests a first set of reports and builds the business case before a full rollout. Here is how organisations are building theirs.
- What Is a Scalable Data Infrastructure?
What a data foundation needs to look like to support growth, automation and AI.
- What Is a Data Historian?
Capture, store and retrieve time-series data from machines, sensors and control systems.
- How Do Organisations Fix Data Quality Issues at the Source?
Find and fix the errors that make people distrust the numbers.
- How Do Manufacturers Turn Machine Data into Live Insights?
Capture machine data, cleanse it and turn it into real-time operational insight.
- What Is a Unified Namespace (UNS) and Why Do Manufacturers Need One?
One consistent structure for operational data across sites, lines and systems.
- Why Is Clean Data a Prerequisite for Reliable AI?
Why AI is only as good as the data behind it, and what to fix first.
What results look like
See our client success storiesLess paperwork
We take data directly from Repligen's machines through robust pipelines, cleanse it in a central data layer and deliver live insights, replacing the manual paperwork behind quality reporting.
Less Downtime
Live machine data powers predictive maintenance at Repligen, so issues are spotted and fixed before they stop the line.
Higher OEE
Clean, connected production data gave the Repligen team the visibility to change work practices on site, lifting overall equipment effectiveness by 20%.
medical report delivery for Alliance Medical Group
A stable, scalable medical report delivery system, described by their IT Manager as delivering exactly what they were looking for.
Our Data Infrastructure technology partners
We work with leading data and industrial technology platforms, matched to the systems you already run and the industry you operate in.
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Frequently asked questions
Data infrastructure is the set of systems, pipelines and processes that capture, connect, cleanse and govern an organisation's data, so it can be used reliably for reporting, automation and AI.
Data infrastructure is the foundation: clean, connected, governed data. Real-time intelligence is what you build on top of it, such as dashboards and reporting that show what is happening now. One depends on the other.
ERP, finance and other business systems, cloud applications, spreadsheets and operational technology, including machines, PLCs, SCADA systems and data historians such as AVEVA PI, Canary and Ignition.
With a data audit. We map how data moves across your ERP and non-ERP systems, machines and spreadsheets, assess its quality and identify where automated ingestion and remediation will make the biggest difference. That gives you a clear, prioritised roadmap before anything is built.
AI tools are only as reliable as the data behind them. Clean, governed and connected data is what makes AI results trustworthy instead of confident but wrong.
Both. Alongside ERP, finance and cloud applications, we capture data directly from machines, historians and production systems, so shop floor data and business data sit in the same trusted foundation.
Ready to build a data foundation you can trust?
Talk to us about a proof of concept and see what your data could do.
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