What does data quality mean?

Data quality is not about perfect datasets or theoretical models. It's about whether people can answer basic but critical questions without manual workarounds. Questions like: Can we trust this cost number? Which assets structurally require more ad-hoc maintenance? Are these two systems talking about the same thing? At which supplier are we overpaying? Are we ready to start using AI (and drive value from it?)

Why is data quality important for me?

Poor data quality rarely causes immediate failure. Instead, it creates friction everywhere. Teams spend time reconciling numbers instead of analysing them. Senior people become the manual fix for data issues. Decision-making slows down because no one fully trusts the outputs. Digital and AI initiatives promise a lot but underdeliver. Good data quality does the opposite. It removes friction, reduces risk, and gives teams confidence in their work.

What can I do to improve data quality?

New systems, dashboards, and AI tools often assume the data problem has already been solved. In reality, they inherit the same inconsistencies that existed before. Data quality is not fixed by buying another platform. It is fixed by deliberately structuring, validating, and maintaining the data that flows through those platforms – including the implementation of (automatic) quality checks.

Where to start when improving data quality?

We start with existing data from spreadsheets, exports, and operational systems. We focus on the data that actually drives decisions, such as procurement, asset, and cost data. Automation accelerates the work, while human validation ensures accountability and trust. The result is a clean, reusable data foundation that supports analysis, operations, and future automation without forcing organisations into rigid systems or one-off clean-ups.

What changes once data quality is in place?

Teams gain confidence in their numbers. Analysts move faster. Procurement and operational decisions become more grounded. Digital and AI initiatives become feasible instead of frustrating. Most importantly, the organisation regains control over its data instead of working around it.

Get AI Ready

DESCRIPTION

Getting AI ready is not about new tools. It’s about whether your data is consistent, comparable, and trusted across teams.

clients

Trusted by teams working with complex operational and financial data.

Procurement, operations, finance, asset management, analytics, and transformation teams who need reliable data to make decisions, not just reports that look good.

How

We work with the data you already have to create a foundation that can be trusted. This brings flexibility – no vendor lock-in, ready to use data lakes like Fabric, ready to use AI, and flexibility to work with any software and any tool.

Getting ready to use AI in a smart way is not a single project. It’s a sequence of deliberate steps that build on each other. Skipping steps doesn’t make things faster — it just shifts risk downstream.

Step 1: Know what “your data” actually is

Before AI, organisations need clarity on the basic building blocks they already work with every day: asset lists, procurement lines, invoices, contracts, maintenance records, operational logs. Not abstract “data”, but concrete information that people use to make decisions. If teams don’t agree on what these objects represent, AI has nothing stable to work with.

Step 2: Make information comparable across systems

AI relies on patterns. Patterns only emerge when information is consistent. This means aligning naming, identifiers, formats, and structures across spreadsheets, ERP exports, procurement tools, and client-provided lists. Without this step, AI will confidently analyse differences that are purely artefacts of inconsistency.

Step 3: Create a trusted baseline

AI should not work on raw, unvalidated inputs. A trusted baseline means data has been structured, checked, and reconciled to the point where people are willing to use it for decisions. This is where human validation matters. AI can accelerate structuring and classification, but accountability must sit with the organisation, not the model.

Step 4: Separate descriptive, diagnostic, and predictive use cases

Not all AI does the same thing, and confusion here causes disappointment.

Descriptive analysis answers: what is happening?

This includes summarising spend, asset populations, maintenance activity, or supplier exposure. AI helps by navigating large datasets, generating explanations, and surfacing patterns faster than manual analysis.

Diagnostic analysis answers: why is this happening?

Here AI supports root-cause analysis, comparisons across sites or assets, and linking operational behaviour to cost or reliability outcomes. This only works when the underlying data is consistent enough to compare like with like.

Predictive and prescriptive analysis answers: what is likely to happen next, and what should we do?

This includes failure risk, maintenance planning, inventory trade-offs, or pricing assumptions. These use cases are the most sensitive to data quality. Without a solid foundation, predictions look sophisticated but are unreliable.

Step 5: Decide where AI supports people — and where it doesn’t

Smart AI adoption is not about replacing judgement. It’s about reducing manual effort, surfacing relevant information, and supporting decisions. AI can search historical records, link related data, flag anomalies, and suggest scenarios. Final decisions, trade-offs, and risk acceptance stay with people.

Step 6: Keep flexibility and avoid lock-in

AI tools, models, and platforms will change. A clean, well-structured data foundation ensures organisations can adopt new tools without redoing the groundwork each time. This is what keeps AI initiatives resilient instead of brittle.

When these steps are in place, AI stops being a risky experiment and becomes a practical capability. Analysts spend time on insights instead of preparation. Procurement and operational teams gain confidence in their numbers. Leadership can use AI outputs without second-guessing the inputs. AI becomes a tool that supports the business — not another source of uncertainty.

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