You know something is wrong with your data — but you don't know how wrong, or what it's worth fixing

Data quality problems are rarely visible from the outside. You see the symptoms, procurement errors, unreliable reporting, blocked automation, but not the underlying scale. Does any of this sound familiar?

Your team has raised data quality concerns, but you don't have the evidence to justify a budget for fixing it.

Previous data projects haven't fixed the underlying problems, and you want to understand why before committing to another one.

You're planning a major system investment — migration, AI, or predictive maintenance — and need to understand your data baseline first.

You need to present a business case to leadership, and 'the data is messy' isn't a sufficient justification for investment.

You suspect there are duplicates, errors, and gaps, but you don't know their scale or cost impact.

WHAT WE DO

A data quality assessment gives you the evidence you need to act

Sharecat analyses your existing records across the dimensions that matter most for industrial operations: completeness, accuracy, consistency, and standards alignment. The output is a clear, prioritised report — not a generic health check, but a specific picture of your data, what it's costing you, and what fixing it is worth.

From experience: organisations that start with an assessment consistently achieve better outcomes from their data projects — because they fix the right things first, in the right order, with a clear business case behind each decision.

WHATS INCLUDED

The service includes:

Statistical overview of data completeness across all key fields

Cost impact analysis — what poor data is costing your procurement and maintenance operations

Duplicate identification and quantification

Gap prioritisation — which problems matter most, and why

Classification and standards compliance check

Actionable recommendations with effort and impact estimates for each priority area

Manufacturer name and part number validation sampling

Our 5-step assessment process

1

Scoping

We agree the scope of the assessment: which system, which data categories, and what questions you need answered. We align on what 'good' looks like for your operations.

2

Data extract

We work with your team to extract a representative sample — or full dataset — from your ERP or CMMS. We handle the technical process and ensure data is handled securely throughout.

3

Analysis

Our tools and specialists analyse every dimension: completeness, accuracy, duplicates, classification gaps, and standards alignment. Every finding is quantified, not estimated.

4

Cost Modelling

We translate data quality gaps into operational impact — procurement failures, excess inventory, maintenance inefficiencies, and blocked automation — so findings are expressed in business terms, not just data metrics.

5

Report and debrief

We deliver a written report and walk your team through the findings, priorities, and recommended next steps. The report is formatted for use with leadership and project sponsors.

WHAT YOU GET