Most data leaders already know their data isn't good enough. What far fewer can answer is the harder question: what would good actually look like, and what are the steps to get there? It's common for an organisation to score itself two or three out of ten on data quality and still have no shared definition of the ten they're aiming for.

This guide gives you that definition. Good data quality isn't perfect data - perfection is neither achievable nor worth paying for. Good data quality means data that is fit for its purpose, trusted by the people who use it, and supported by clear ownership and monitoring. Below, we set out the six dimensions that make data fit for purpose, a five-stage maturity model so you can locate yourself honestly, and a sequenced roadmap to move from unreliable data to data your business can decide on.

What "good" data quality really means

The most useful shift a leader can make is from perfect to fit for purpose. A customer record that's missing a fax number is still excellent if no one needs the fax number. The same record missing a delivery address is a serious defect if it breaks fulfilment. Quality is always relative to use.

That gives us three practical tests for "good":

• Fit for purpose - the data supports the decisions and processes that depend on it, at the level of accuracy those uses genuinely require.

• Trusted - the people consuming the data believe it, because it's measured, transparent, and consistent over time. Trust, not perfection, is what drives adoption.

• Governed - someone owns each critical data domain, quality is monitored, and issues are found and fixed through a defined process rather than by accident.

Hit all three and your teams stop second-guessing reports, stop maintaining private spreadsheets, and start making faster decisions on shared numbers.

The six dimensions of data quality

"Good" becomes measurable when you break it into dimensions. These six are the widely accepted core, and together they give you a vocabulary for diagnosis and a basis for scoring.

1. Accuracy - does the data correctly describe the real-world thing it represents? An address that doesn't match the customer's actual location is inaccurate, however neatly it's formatted.

2. Completeness - are the values that matter actually present? Missing prices, blank categories, or absent identifiers all erode completeness.

3. Consistency - does the same fact agree across systems? When the CRM, ERP, and warehouse disagree on a customer's status, no one can be sure which is right.

4. Timeliness - is the data current enough for the decision? Yesterday's stock position may be fine for a monthly review and useless for a same-day order.

5. Validity - does the data conform to the rules and formats it should - valid currency codes, dates in range, values from the permitted list?

6. Uniqueness - is each real-world entity represented once? Duplicate customer or product records quietly distort every count, sum, and segmentation built on top of them.

The point of the dimensions isn't to chase a perfect score on all six everywhere. It's to decide which dimensions matter most for your highest-value data and to measure those deliberately.

The data quality maturity model

Before you can plan a route, you need to know where you're standing. This five-stage maturity model describes how organisations typically progress. Most enterprises that feel stuck sit somewhere between stages one and two - aware of the problem, but without the ownership or measurement to move.

• Stage 1 - Unaware / Ad hoc. Quality issues surface as firefights. There's no measurement, no ownership, and fixes happen in spreadsheets after something breaks. Trust in reporting is low.

• Stage 2 - Reactive. Problems are acknowledged and individuals patch them, but effort is uncoordinated. There may be a small data quality team that's overloaded and unsure how to prioritise.

• Stage 3 - Defined. Data ownership is assigned, quality rules and dimensions are agreed, and issues are logged and triaged through a known process. Quality starts to be measured rather than guessed.

• Stage 4 - Managed. Quality is monitored continuously against agreed thresholds, with dashboards and clear accountability. Issues are caught early, often before they reach a report.

• Stage 5 - Optimised. Quality is largely automated and built into pipelines by design. Data is trusted enough to power self-service analytics and AI with confidence, and the organisation improves proactively rather than reactively.

Locating yourself honestly is the single most valuable diagnostic step. If you want a structured way to score each dimension across your critical domains, work through a data quality maturity assessment before committing to a plan.

A roadmap from "2 out of 10" to trusted data

Moving up the model is a sequenced programme, not a single project. Trying to fix everything at once is the most common reason these efforts stall. The following six steps keep effort focused on what creates value first.

1. Baseline and prioritise. Measure current quality across your key dimensions for the data domains that matter most - typically customer, product, supplier, and transactions. Don't boil the ocean. Establish the score, name the highest-impact defects, and quantify what they cost the business in rework, lost sales, and poor decisions.

2. Assign ownership. Quality fails without accountability. Appoint data owners for each critical domain and define what they're responsible for. This is also where you decide how to structure and grow the team - many organisations discover their function is too small, and need a deliberate plan for building a data quality capability rather than simply adding headcount.

3. Agree the rules and the target. For each priority domain, define the quality rules, the dimensions that matter, and the threshold that counts as "good enough." Now "good" is a number people can be held to, not an opinion.

4. Remediate the priorities. Fix the highest-value problems first - de-duplicate the customer master, complete the product attributes that drive cross-sell, reconcile the systems that disagree. Tackle root causes at the point of entry, not just the symptoms downstream.

5. Monitor continuously. Stand up dashboards and automated checks so quality is measured against your thresholds on an ongoing basis. This is the shift from reactive to managed: issues are caught and routed to owners before they reach the business.

6. Embed and automate. Build quality controls into your data pipelines and platform so good data becomes the default state, not a clean-up exercise. This is where a modern governed platform and cataloguing - for example, implementing Microsoft Purview properly - turns one-off effort into a durable operating model.

Done in this order, you'll see trust improve long before the work is finished, because the most painful defects are addressed first.

Why this matters now: data quality is the foundation for AI

There's a new and urgent reason to take quality seriously. The move to self-service analytics and AI assistants like Copilot is only as trustworthy as the data underneath. Point a conversational AI tool at poor data and you don't get insight - you get confident, fluent wrong answers, at scale. Governed, quality data is the prerequisite for AI you can actually rely on. Organisations that fix the foundation first are the ones whose data is genuinely ready for AI; those who skip it find their AI ambitions stall on mistrust.

Move from knowing to doing

If you can see your organisation in the early stages of the maturity model, you're not behind - you're in good company, and the path forward is well understood. The leaders who pull ahead are simply the ones who move from acknowledging the problem to sequencing the fix.

Want to know exactly where you stand? Take our data quality maturity assessment to score your organisation across all six dimensions and get a recommended next step - or talk to our team about building your roadmap.