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Data quality: a major pillar of Data Governance

Data quality and value creation

In the digital age, data is often compared to gold. But just as raw gold must be refined to reveal its true value, data requires rigorous governance if it is to be fully and securely exploited. Let's start with a simple definition of Data Governance Data Governance: a policy at the heart of corporate strategy, it is a set of processes, policies and standards designed to ensure the effective management of data within an organization. Its main pillars include data security, metadata management, data architecture and, above all, data quality. In this article, we'll focus on the latter.

Data quality is a central pillar of data governance in organizations, and one that is too often underestimated. often underestimated. According to Gartner's 2023 study "Data Quality: The State of the Art", "70% of companies consider data quality to be a major challenge, and that 80% of enterprise data may be inaccurate or out of date". These data quality issues can hamper performance and decision-making. Yet, by focusing on data quality, companies can not only overcome these specific problems, but also reduce costs by 20% and increase revenues by 10%.

In this article, we delve into the world of Data Governancefocusing on the crucial importance of data quality for value creation. Join us on this exploration and discover how Data Governancecan transform the raw gold of data into information, the real treasure of today's organizations.


I. Data quality: an often underestimated challenge

The construction analogy

Imagine you're building a house. You'd want solid, reliable materials, wouldn't you? Data governance works the same way. If data are the bricks of our digital house, then their quality is the integrity of those bricks. A house built with fragile or defective bricks won't last long, just as a strategy based on poor-quality data is doomed to failure.

Construction analogy

Alarming figures

Several studies illustrate the direct impact of data quality on business performance:

  • Forrester (2022): In their report "The Value of Data Quality: A Global Benchmark", it is revealed that companies that neglect data quality as part of their data governance lose an average of 12% of their revenues.

  • McKinsey (2021): their study "Data Quality: The Hidden Risk" highlights that companies who integrate data quality as a core element of their data governance can reduce costs by 30%.

  • IBM (2020): According to "The Cost of Poor Data Quality", companies that neglect data quality in their data governance strategy are more likely to suffer data breaches.

These figures highlight the crucial importance of data quality within data governance. Ignoring this aspect can not only lead to financial losses, but also compromise the security and integrity of information.

Why negligence?

But why is data quality so often overlooked? The answer is simple: data quality, while essential, is not always tangible or immediately visible. It doesn't make people "dream" in the way that other technological innovations can. And yet, neglecting data quality can lead to erroneous decisions, loss of confidence and, ultimately, financial loss.

The need for awareness

In the digital age, where data is often referred to as the "new gold", it's essential to be aware of the importance of its quality. Just as you can't create valuable jewelry with impure gold, you can't get valuable insights from unreliable data. So it's time to put data quality at the heart of your data governance strategy.

II. TGV and data: the path to added value

The Paris-Bordeaux TGV: unsuspected added value

Before the creation of the Paris-Bordeaux TGV line, it was hard to imagine all the benefits it would bring. Of course, the time savings were obvious, but who could have foreseen the creation of new jobs, the economic and tourist development of Bordeaux, or the growing attractiveness of the region for businesses and investors?

According to a study by IAURIF, the TGV Paris-Bordeaux has generated 1.2 billion euros in added value per year for the Nouvelle-Aquitaine region. It has also contributed to the creation of 10,000 direct and indirect jobs.

It's much the same with data governance. While its implementation may seem complex and its ROI difficult to measure at the outset, once operational, it generates a multitude of unsuspected benefits. Just as the TGV transformed Bordeaux, solid data governance, backed by impeccable data quality, can transform a business, creating value where none was expected.

Reliable data vs. unreliable data

To continue the railway metaphor, sharing unreliable data is like giving a passenger the wrong train timetable: it leads to confusion, dissatisfaction and loss of confidence. Reliable data, on the other hand, provides accurate information that can be relied upon with confidence to make informed decisions. But reliability is not enough. For data to be truly effective, it must respect the 3U principle:

  • Useful : They must be useful, have a role and add value for the user. They must help answer specific questions or solve concrete problems.

  • Usable: Data must be structured, cleaned and ready to use. It must be accessible and understandable to those who need it.

  • Used: Even the best data is of no value if it is not actively used in decision-making processes, analyses or business operations.

Effective data diagram

Data quality, the driving force behind value creation

Data quality, in line with the 3U principle, is not just a guarantee of security. It is also a powerful lever for innovation and value creation. Reliable, relevant and actionable data enables us to anticipate trends, optimize processes and propel the performance of modern businesses, making things possible that weren't before. By focusing on rigorous data governance, organizations maximize their value creation potential.

III. Tale of Data: guaranteeing data quality to maximize value creation

An all-in-one solution to guarantee data quality

Tale of Data is more than just software:

Complete solution: Tale of Data is not just a tool, it's a holistic platform that integrates perfectly with any data governance policy. It encompasses a multitude of functionalities, from auditing to rectification and data enrichment. Each step is designed to guarantee optimal use of data, thus ensuring its reliability.

Accessible to all: Where Tale of Data really stands out is in its ability to democratize access to advanced technologies. Thanks to built-in artificial intelligence, the solution offers unrivalled power, while remaining intuitive enough to be used by technical experts and novices alike. It's the perfect combination of technological sophistication and ease of use.

Why choose Tale of Data?

  • Speed of execution: unlike traditional programming, Tale of Data offers rapid implementation, enabling employees to be more responsive.

  • Trust: By guaranteeing quality data, Tale of Data strengthens the confidence of internal staff, enabling them to rely on reliable data for their day-to-day tasks.

  • User autonomy: after just one day's training, users can get to grips with Tale of Data, unlike traditional tools which take weeks.

  • No-code approach: Tale of Data incorporates a no-code philosophy, enabling anyone, even without specific technical skills, to manipulate, process and analyze data. This democratizes access to advanced functionalities and encourages wider adoption throughout the enterprise.

Features to suit every need

  • Audit : Tale of Data allows you to perform an in-depth data audit to identify errors. Thanks to this approach, companies can better understand the gaps in their data.

Audit Tale of Data

  • Enrichment : The solution natively enables the aggregation of data from a variety of sources, including the cloud, ERP, internal corporate infrastructures and Open Data. Using repositories, joins and fuzzy logic functions functions, Tale of Data makes it possible to combine information without a common key, opening up a whole new world of possibilities, optimizing the relevance and reliability of data for decision-making.

Tale of Data enrichment

  • Correction : Beyond simply identifying anomalies, Tale of Data suggests appropriate corrections. Users then have the freedom to accept or reject these corrections, guaranteeing reliable data that meets their needs. What's more, these corrections can be automated and scheduled on a regular basis, ensuring that data quality remains constant over time.

Correcting with Tale of Data

  • Visualization : the solution offers the possibility of viewing data in real time, enabling immediate immediate of reliable data. In this way, you can go from simple raw raw data to information qualified, accurate, relevant and trustworthy. This transformation of "data" into "information" ensures that the 3U criteria are met, guaranteeing usable, used and useful data.

Visualize with Tale of Data

Numerous companies from a wide range of industries rely on Tale of Data to manage and ensure the quality of their data. These companies testify to the effectiveness of the Tale of Data solution in improving the quality of their data, which has a direct impact on their performance and value creation.

ManutanEurope's largest supplier of business products and services, has identified data quality challenges among its 700,000 references.

Aude Poorjabar, Head of Data Quality and Governance at Manutan, emphasized the need to industrialize their data quality processes.

After an in-depth evaluation, Tale of Data was chosen for its ability to industrialize data quality processes, drastically reduce manual tasks and easily combine different data sources.

A data quality analyst at Manutan testified to Tale of Data's efficiency, noting that tasks that took a week with Python scripts were completed in two hours with Tale of Data.

IV. Conclusion: data quality, the central pillar of governance

Over the course of this article, we've explored the crucial importance of data quality in data governance. Like the solid rails on which the high-speed train travels, reliable data is the foundation on which effective data governance rests.

When it comes to machine learning, it's imperative to work on the right data. Learning from bad data is the worst possible scenario, as it leads to distorted knowledge that can never result in correct predictions. Investing in data quality is therefore an innovative approach. At the outset of such a project, it is difficult to quantify a precise ROI, just as with an infrastructure project. However, this investment is essential for future value creation. It opens the door to new opportunities and enables companies to carry out projects that they would not have been able to consider before, that were not accessible.

In short, focusing on data quality means building the solid foundations of a forward-looking company, guaranteeing informed decision-making, increased innovation and outstanding value creation.

Tale of Data is an essential solution for all companies wishing to ensure the quality of their data. It offers a comprehensive and innovative approach, tailored to the specific needs of each organization. Designed to be accessible to all, this solution is particularly appreciated by non-technical staff, for its ease of use, and by technical staff, who find it a considerable time-saver in their daily tasks.

For those of you who wish to delve deeper into the subject of data governance and discover the best practices to adopt, I invite you to consult this article on our website. After all, successful data governance starts with good data, but it doesn't stop there.

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