Data quality: a major pillar of Data Governance
In the digital age, data is often compared to gold. But just as raw gold needs to be refined to reveal its true value, data requires rigorous governance if it is to be fully and securely exploited. Let's start by simply defining what Data Governance is: 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 all too 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"70% of companies consider data quality to be a major challenge, and that 80% of enterprise data may be inaccurate or out of date".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 dive into the world of Data Governance, focusing on the crucial importance of data quality for value creation. Embark with us on this exploration and discover how well-orchestrated Data Governance can turn the raw gold of data into information, a real treasure trove for organizations today.
Contents
I. Data quality: an often underestimated challenge
II. HSR and data: the path to added value
III. Tale of Data: guaranteeing data quality to maximize value creation
IV. Conclusion: data quality, the central pillar of Governance
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 its 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.
Alarming figures
Several studies illustrate the direct impact of data quality on business performance:
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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.
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McKinsey (2021): their study "Data Quality: The Hidden Risk" underlines that companies that integrate data quality as a core element of their data governance can reduce their costs by 30%.
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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 therefore not only lead to financial losses, but also compromise the security and integrity of information.
Why neglect?
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 it 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 is 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 difficult to imagine all the benefits it could 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?
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 trust. Conversely, reliable data provides accurate information, which 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:
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Useful: it must be useful, have a role and add value for the user. It must help answer specific questions or solve concrete problems.
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Usable: data must be structured, clean and ready to be used. It must be accessible and understandable to those who need it.
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Used: even the best data is of no value if it is not actively used in the company's decision-making processes, analyses or operations.
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 were not possible 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:
Why choose Tale of Data?
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Speed of execution: unlike traditional programming, Tale of Data offers rapid implementation, enabling staff to be more responsive.
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Confidence: by guaranteeing quality data, Tale of Data strengthens the confidence of in-house staff, enabling them to rely on reliable data for their day-to-day tasks.
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User autonomy: after just one day's training, users can get to grips with Tale of Data, unlike traditional tools which require weeks of training.
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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 across the enterprise.
Functionalities adapted to every need
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Audit: Tale of Data enables in-depth auditing of data to identify errors. This enables companies to better understand the gaps in their data.
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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, Tale of Data makes it possible to combine information without a common key, opening up the field of possibilities and optimizing the relevance and reliability of data for decision-making.
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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.
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Visualization: the solution enables data to be visualized in real time, for immediate and optimal use of reliable data. This transforms raw data into qualified, accurate, relevant and trusted information. This transformation from "data" to "information" ensures that the 3U criteria are met, guaranteeing usable, utilized and useful data.
Numerous companies from a wide range of sectors trust 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.
Manutan, Europe'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, highlighted 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
Throughout this article, we have 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.
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