Data Quality: Challenges and Best Practices in Data Quality Management

Essential for businesses, Data Quality is a major issue for operational processes across departments such as marketing, finance, and production. But what exactly is Data Quality, and how can it be guaranteed over time?

At a glance

What is Data Quality?

The impact of bad data quality

Data Governance & Data Management

AI & Data Quality

Data Quality at a Glance

Data quality directly impacts performance

Reliable data is crucial for informed decision-making and effective management across all departments of a company.

AI, a catalyst for data management

By learning and adapting its actions, AI improves data quality, particularly in terms of control and enrichment.

Data governance, a fundamental pillar

Well-defined governance ensures data consistency and security throughout the company, thereby guaranteeing its reliability.

What is Data Quality?

Data Quality refers to the implementation of actions aimed at ensuring that the data within an information system is accurate and reliable. Data is a company's most valuable asset for guaranteeing the efficiency of its processes, as well as the relevance of the decisions and actions taken. This is why it is important to have full confidence in the quality of one's data before using or distributing it. The more data is "consumed" — that is, used by processes — the higher its quality must be.

How is data quality measured?

Quality is measured through "technical" rules and "business" rules. Technical rules represent criteria intrinsic to the data itself:

Data completeness: is the data mandatory?
Data validity: has the data been entered in the correct format?
Data freshness: when was this data last entered?

Business rules complement data qualification through a set of conditional rules based on its type and use. They assign a functional value to the data, through criteria such as consistency and accessibility, for example. Together, these rules make it possible to define the true quality of the data in question.

The Impact of Bad Data Quality

Data quality challenges are intrinsically linked to a company's business challenges. Indeed, data quality is now a critical issue for companies, as it can present a major risk when neglected — potentially affecting decision-making, operational processes, compliance, and digital openness.

Bad Data Quality in decision-making processes

Most decisions made within an organisation are based on figures derived from a more or less significant volume of data. If that data is not sufficiently reliable, projects or investments may be launched on the basis of incorrect information. Conversely, a dashboard built on accurate and reliable information enables sound thinking and confident decision-making.

bad Data Quality in operational processes

Bad Data Quality undermines the productivity and efficiency of companies: incorrect invoicing due to a data entry error on a customer record, inadequate traceability that prevents tracing a manufacturing problem back to its source... Operations become time-consuming, as there is a constant need to identify the source of the issue and correct the information.

Bad Data Quality and corporate compliance

In certain sectors, regulations are becoming increasingly strict regarding the communication of specific information to customers, consumers, and partners. Take the example of the Anti-Waste Law for a Circular Economy (AGEC): the law now governs environmental claims made by brands and makes it mandatory to inform consumers about certain characteristics of the products they purchase. A company with poor-quality data may struggle to comply — whether due to inaccurate information being communicated, or difficulty in tracing that information within its information system.

Bad Data Quality in the digital world

The rise of digital has forced companies to open up to the outside world — no longer containing their information within their internal organisation alone: e-commerce, marketplaces, institutional websites, and so on. The use of poorly qualified data can then have damaging effects on a company's reputation. Conversely, digital channels have also made it possible to gather data on customers more frequently — such as web pages visited or orders placed — enabling a better understanding of the customer, greater personalisation of their brand experience, and ultimately an improved brand image.

Data Governance and Data Management in the Service of Data Quality

Data Governance and Data Management are closely linked concepts, as they are two sides of the same coin in the data lifecycle.

What is Data Governance?

Data Governance encompasses all the practices and roles related to the acquisition, management, and use of data. It confirms the quality and security of data throughout the company, and defines the roles and uses of that data. The desire to implement a Data Governance approach most often arises after becoming aware of the impacts associated with data quality. Implementing data governance is not intended to change a company's organisational model, but rather to define a policy based on processes that are already functional. This is why three governance models are defined according to the processes involved.

Centralised governance model: applies in the context of product data, where information flows top-down. The governance model is fairly straightforward, but involves highly collaborative workflows in which many stakeholders and departments participate. Data processing is then centralised.
Decentralised governance model: applies in the context of customer data, where information flows bottom-up. This model is more complex, as it involves information that comes from the field and therefore from multiple entry points. The objective of this governance model is to identify the "Golden Record" — the version of a data record considered to be the "Single Source of Truth" — in order to feed systems back with the correct information. Data processing is then decentralised.
Federated governance model: this model combines centralised processing of high-value data with decentralised processing of low-value but high-volume data. The model redistributes roles, with a central team coordinating all departments, while each department manages its own governance.

What is Data Management?

Data Management refers to the technical and operational management of data. This management covers the entire data lifecycle, including collection, validation, storage, protection, processing, and enrichment of data. The objective is to ensure its reliability and accessibility.

There are three essential elements to Data Management:

A suitable data architecture: this involves modelling the data lifecycle, in particular the collection and use of data. It is also within this architecture that data quality rules are integrated.
A high-performing storage system: data must be recorded and stored so that it can be used. The goal of storing data — and storing it well — is to make it accessible to the right people within the company.
Strong data security: data security is essential, as in addition to being a resource, securing it may also be a legal requirement. Data Management must therefore ensure that data is protected against cyberattacks and compliant with the legal requirements imposed by regulations.

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The Relationship Between AI and Data Quality

AI has a great deal to offer. It can learn, observe, and watch what we do with our data, and thus become an essential tool in improving data quality. Whether it involves control, validation, or enrichment — any task that can be replicated, AI can support the data stakeholders involved.

Bypassing deterministic rules

In the demanding field of Data Quality, where deterministic rules often dictate standards, the introduction of AI represents a major evolution. Traditionally, quality control processes are limited by predefined rules. AI, on the other hand, offers a less deterministic approach, capable of learning new rules as it goes. This adaptability makes it possible to significantly improve data quality, particularly when processing large volumes. The true value lies in AI's ability to adjust dynamically and learn from past errors, providing an agile solution to the challenges of validation and quality control.

The stakes related to data quality and control are considerable. Integrating AI represents a strategic response to these challenges. By enabling AI to learn and apply new rules, companies can guarantee a higher level of quality — particularly in the context of processing large data volumes, such as in a PIM solution. This approach not only promotes operational efficiency, but also drives continuous improvement in data quality, thereby reinforcing the reliability of information and facilitating more informed decision-making.

Data enrichment through AI

Data enrichment plays a crucial role in improving and complementing existing information, which is particularly important in the field of product data. Comprehensive data is not only essential for optimal product management, but also for an enriching customer experience. Qualifying thousands of products with attributes such as dimensions, colour, and weight can become a tedious and error-prone task. Nevertheless, advances in generative AI offer a solution by facilitating the implementation of this process — generating product descriptions or marketing arguments from a solid database.

The importance of good data quality for AI

The connection between AI and data quality works both ways: AI can contribute to maintaining high data quality, while also highlighting that data quality is crucial for effective collaboration with AI. Whether for enrichment or information generation, it remains essential to recognise that AI requires an input of information in order to learn and understand its tasks. It is important to regard AI as a tool — an algorithm made available to professionals. Consequently, its effectiveness depends on being properly fed: if incorrect information is provided to AI, it will logically generate lower-quality outputs.

Data Quality Stakeholders

Awareness of data quality within a company rests with those who use it on a daily basis. Marketing teams responsible for campaigns, production teams focused on manufacturing, digital teams dedicated to information distribution, and finance teams oriented towards performance management are all key stakeholders responsible for the quality of the data they feed into information systems.

For these business teams, data quality can be perceived as a subjective aspect that only becomes significant at the moment a specific process consumes it. It is only at that stage that the question of data quality — or the lack thereof — becomes concrete, underscoring the critical importance of ensuring reliable and accurate data for effective operations within the company.

The Evolution of Mindsets Around Data Quality

The evolution of mindsets regarding data quality has undergone a significant shift. When a company grows empirically — often through the merging of several entities — each entity may hold good-quality data, but the absence of sharing and links between reference systems can lead to poor cross-functional quality, resulting in the presence of duplicates.

As companies grow, the empirical approach — often based on tools such as Excel — reaches its limits. What was manageable at the outset quickly becomes unmanageable as the volume of data increases, highlighting the need for a more systematic approach.

Compliance challenges too, such as the General Data Protection Regulation (GDPR), have pushed companies to question how they document and measure data quality. It has become crucial to go beyond simple compliance and to consider data as an essential asset for the overall efficiency of the company.

Change Management to Support the Implementation of Data Quality

Change Management plays an essential role in supporting the implementation of Data Quality within a company. The success of any data quality initiative depends largely on how employees adopt and integrate these changes into their daily practices.

It is imperative to raise team awareness of the benefits of Data Quality, highlighting how reliable data contributes to more efficient processes, informed decision-making, and improved overall company performance.

Employee buy-in is a central aspect of Change Management in the context of Data Quality. This involves making teams aware of the positive impacts of data quality on their day-to-day activities and actively involving them in the process. Listening to concerns, creating an environment conducive to learning, and recognising the efforts made are key elements in achieving a successful transition towards a company culture where data quality is seen as a strategic priority.

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They trust us

Data Governance & Management Director

Pascal Anthoine

"Data quality is the fundamental foundation of operational processes. In a constantly evolving digital world, Data Governance and Data Management are the guardians of this quality, defining rules and processes that transcend organisational boundaries. By strategically integrating AI, we push the limits of traditional deterministic rules, offering unparalleled agility in the management and continuous improvement of data quality."

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In Summary

Data quality is essential to business success, impacting key areas such as marketing, finance, and production. "Data Quality" aims to ensure the reliability of data within information systems, measured through technical and business rules. Poor data quality can have significant repercussions on decision-making, operational processes, compliance, and the digital reputation and effectiveness of a company.

Integrating AI into data quality management represents a major advancement, making it possible to bypass traditional deterministic rules. AI can enrich data, improving product management and the customer experience. The relationship between AI and data quality is bidirectional, requiring correct input of quality data to ensure effective use of AI.

The key stakeholders responsible for data quality within a company are those who consume it — such as marketing, finance, production, and digital teams. The evolution of mindsets around data quality has become crucial, particularly given increasingly strict compliance requirements. Change Management plays an essential role in securing stakeholder buy-in and building understanding of the impact of a Data Quality approach.

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Data Quality: Challenges and Best Practices in Data Quality Management