The 9 dimensions of data quality: why you should keep a close eye on your customer data
Data quality is recognised as an essential building block for any data-driven project. Even so, it is still too often only partially approached. Deployments don’t always deliver the expected results – why are your campaigns off-target? Why are your return rates soaring? Why do your tools produce conflicting figures? To avoid these pitfalls, 9 dimensions of data quality have been defined, notably by Gartner. They provide a roadmap for applying data quality principles with precision. Within each dimension, data quality tools help translate intentions into tangible business outcomes, enhancing marketing, sales, financial, and operational performance.
1. Accessibility: data that can’t be seen is data that can’t be used
If data isn’t accessible at the right time, by the right person, it’s of no use. For example, when a representative can’t call a customer back because their number is stored only in the e-commerce platform, or when an invoice doesn’t go out because the address in the CRM system isn’t replicated in the ERP. These issues come from information silos that block access for those who need it.
The solution lies in creating a unique customer repository (UCR) that feeds into the company’s operational systems. However, this UCR will only be effective if it contains verified and deduplicated information. If it does not, it will propagate inaccurate or misleading data across all connected tools. This is why data quality is an indispensable complement to a UCR: to ensure that data is both accessible and usable, without loss of information, or time wasted searching.
This is what our clients have shared:
“With DQE, we are getting peace of mind and we have confidence in our customer data. We know that we are collecting reliable data that then feeds into our CRM tool, and that helps us carry out our relational campaigns without worrying about data quality.”
Sébastien Courtin, CEO – Wamiz
Sébastien Courtin, CEO – Wamiz
2. Accuracy: data must reflect reality
The accuracy of data refers to how correct and true to reality it is. In practice, the smallest details matter: an email missing the “@”, a transposed digit in a post code… One typo or mis-filled field can be enough to prevent delivery or a payment reminder. To prevent this, data quality processes intervene at the point of entry, using validation rules, automated checks, and real-time form correction. This prevents basic errors that can become costly downstream.
This is what our clients have shared:
“By using DQE to qualify postal addresses as soon as they are entered, we see a marked improvement in deliveries and a reduction in complaints received by our customer service department. This of course has a positive effect on customer experience, who get their orders delivered quickly, at the right address.”
Philippe Tonnellier, IS Manager – Au Forum du Bâtiment
Philippe Tonnellier, IS Manager – Au Forum du Bâtiment
In sectors where verifying customer identity is critical – finance in particular – accurate data is essential for a robust KYC process. KYC requires certainty that an individual really is who they claim to be. One way to achieve this is to cross-check pairs of data, such as name and mobile number, against a trusted repository like a telecoms operator database. If a mismatch is detected, an alert is sent. Data quality tools can automate this verification via API connections between collected data and third-party repositories used for verification. The bank/insurance/financial sector is at the forefront of users of data quality solutions in its use cases.
This is what our clients have shared:
“By using [DQE’s] Match ID solution, we reduced the need for supporting documents for address verification for 33% of candidates, which shortened integration delays and improved user satisfaction, all while fighting fraud.”
Loïc Leblé, Product Manager – Fortuneo
Loïc Leblé, Product Manager – Fortuneo
3. Completeness: incomplete data slows everything down
Incomplete data restricts what a company can do. Without a phone number, there’s no SMS reminder. Without a post code, there’s no local targeting. Without a title, it’s impossible to personalise a campaign. Completeness ensures that all essential fields are not only filled in, but also usable for business purposes.
Data quality solutions identify missing information and can automatically enrich records. They turn a fragmented, low-value database into an actionable foundation for segmentation, customer engagement, and invoicing. This is particularly useful for B2B data: entering a company’s registration number or name can automatically populate every related field. The result: time saved and data accuracy guaranteed.
This is what our clients have shared:
“DQE’s company identification number solution ensures accurate identification of professionals and helps keep their information up to date over time (company closure, relocation of the registered office, etc.). This approach helps guarantee the reliability of Pages Jaunes content, which increases user confidence in our media.”
Florian Bazin, IS Project Manager – Solocal
Florian Bazin, IS Project Manager – Solocal
4. Consistency: contradictory data undermines credibility
When the same customer appears as “Ms” in the CRM system and “Mr” in the marketing tool, the brand’s credibility takes a hit. Customer relationships suffer from mis-addressed or impersonal communication – a clear sign of inconsistent data.
Consistency means keeping data aligned across every system and channel. The same field should never differ from one application to another. Data quality tools detect discrepancies through comparison rules and reconciliation routines. This ensures a seamless customer history and guarantees better KYC. The outcome is a coherent, fluid experience – both internally and for customers, free from visible or hidden contradictions.
This is what our clients have shared:
“DQE (…) maintains our unified customer repository, including integration of new databases. This is how Harvest centralises KYC as the company grows through acquisitions.”
Cédric Letellier, CRM Manager – Harvest
Cédric Letellier, CRM Manager – Harvest
5. Precision: vague data is weak data
An address like “Rue des Lilas, Paris” may be correct but is still inadequate for reliable delivery or accurate geolocation. Key details such as the street number, floor, or post code are missing – in other words, the data lacks precision.
Precision refers to the dataset’s ability to provide information that is detailed enough and relevant enough for effective company action. In a B2B context, that may include the contact’s exact job title, VAT number, or date of last purchase. Data quality solutions enrich customer records, standardise formats, and automatically complete secondary fields, improving both efficiency and relevance. In doing so, they directly enhance the granularity of the insights that can be drawn from the data.
This is what our clients have shared:
“With DQE’s real-time input help, we collect reliable postal address data, standardised in address cleansing formats. This facilitates our mailings. We’ve reduced undelivered packages by half, achieving tangible savings while increasing the opportunity rate.”
Christophe Oudanonh, IT Domain Manager – Belambra Clubs
Christophe Oudanonh, IT Domain Manager – Belambra Clubs
6. Relevance: focus data on its purpose
Too much data can bury what really matters – especially when duplicates add confusion. Relevance ensures that data serves a specific, useful purpose: segmentation, personalisation, scoring, analysis, compliance, and more.
Data quality tools allow companies to merge duplicates according to business-specific rules. For example, they can group by household when several customers share one address, or conversely, isolate each individual record. They also help companies analyse how their data is actually used, refine forms, and adapt collection schemes. The end result is leaner, better-targeted and more efficient databases.
This is what our clients have shared:
“Data quality contributes to highlighting the value of each franchisee’s company. It is even more the case with Fitness Park being based on a subscription model with a significant local dimension: they have to know the customers and also to expertly manage the data associated with them. DQE’s solutions contribute to this.”
Sébastien Loye, Head of Digital – Fitness Park
Sébastien Loye, Head of Digital – Fitness Park
7. Recency: outdated data is an operational risk
If a customer moves house and the record isn’t updated, parcels bounce back, invoices are rejected, and messages go astray. That’s why it’s vital to work with up-to-date data and monitor its recency.
Recency measures how current data is at the moment it is used. A good customer file today can be obsolete in three months. Data quality solutions implement automatic update mechanisms, alerts for dormant records, and connectors that refresh databases. This keeps customer relationships relevant, and limits the costs of error.
This is what our clients have shared:
“With the help of DQE’s solution, 25,000 email addresses that had been marked invalid by ISP servers since they did not give a response were able to be re-qualified as live and reachable email addresses. Bip&Go was able to get back in touch with these customers, let them know about new offers, and to generate additional revenue.”
Michael Bouyer, Project manager in charge of data protection – Bip&Go
Michael Bouyer, Project manager in charge of data protection – Bip&Go
8. Uniqueness: one customer = one record
When a single customer appears twice in a database under different entries (“John Doe” and “Doe J.”), it creates a fragmented view, an inconsistent experience, and can risk multiple mailings or even double billing. The key dimension to watch here is uniqueness.
Uniqueness ensures that each entity (customer, supplier, product) appears only once in the repository. It relies on intelligent matching algorithms capable of detecting even partial duplicates. By restoring a consolidated view, they help make analyses more reliable, avoid business errors, and enable truly personalised customer service.
This is what our clients have shared:
“Using DQE, we keep duplicate records out of our Microsoft Dynamics 365 customer database. We have a consolidated view of our contacts and our B2B accounts. It is a reliable support to maintaining our relationships with businesses, and for monitoring student progress.”
Géraldine Marie, Director of the CRM and Relational Marketing Division, NEOMA Business School
Géraldine Marie, Director of the CRM and Relational Marketing Division, NEOMA Business School
9. Validity: follow the rules, avoid the errors
An email without “@”, a phone number with an extra digit or a non-existent post code are all invalid – and therefore unusable. Validity measures compliance with format rules, business logic, and regulatory requirements. It is essential for preventing processing failures, technical blocks, or compliance penalties – issues that are especially critical under GDPR and in e-invoicing.
Data quality tools validate data automatically at entry, continuously check formats, and apply rules specific to each case. Valid data is reliable data – it flows smoothly through systems and keeps the company in control of compliance.
This is what our clients have shared:
“DQE’s real-time data entry checks ensure that our B2B data collection is verified and up to date. This gives us greater control over the risk of situations that could lead to us not complying with wholesaler status.”
Roland Blanchemain, Data Quality and Governance Manager – METRO
Roland Blanchemain, Data Quality and Governance Manager – METRO
The 9-dimension framework for data quality proves one thing: every detail matters in the data you collect. Applying these 9 principles leaves no weak points in your company’s information assets. And equipping yourself with the right tools is no longer optional – it’s essential to get the most from your data in an increasingly demanding environment. When structured around these dimensions, data quality becomes a genuine lever of performance and competitiveness across every level of the organisation.
About DQE
Because data quality is essential to customer knowledge and the construction of a lasting relationship, since 2008, DQE has provided its clients with innovative and comprehensive solutions that facilitate the collection of reliable data.
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