Effective data governance: why and how optimize the quality of your data?

12 juillet 2023

Pauline Lagarde

Cybersécurité Data cleaning Stratégie Data Governance

Data quality: why and how to optimize it for better performance?

Mastering the quality of the data in your data assets is a crucial issue. To make informed decisions, you need data that is correct, complete, up-to-date, consistent and understandable to all. But how do you manage large quantities of data? Which tools are best suited to your needs? What approach should you adopt?

Data governance is essential to organizations

In today's constantly evolving digital world, effective data management has become an essential issue for organizations. Data, whether structured or unstructured, is the driving force behind decision-making, innovation and digital transformation. However, with the explosion in data volumes, it is becoming increasingly difficult to organize, secure and exploit it optimally. So how do you make the most of it? That's where data governance comes in, and more specifically, data quality.

What does data volume mean?

Before diving behind the scenes of quality data, it's important to understand its nature. Structured data, such as traditional databases, are organized according to a pre-established pattern, whereas unstructured data, such as e-mails, social media and multimedia files, do not follow a predefined schema. This distinction is essential for developing appropriate governance strategies.

In figures, it is estimated that by 2025, the total volume of data worldwide will reach 175 zettabytes (175 billion terabytes). However, only 0.5% of this data is currently processed, leaving a huge untapped potential. What's more, around 80% of this data is unstructured, adding a further level of complexity to its management and use.

Data has become an invaluable resource for today's organizations. It plays an essential role in strategic decision-making, performance analysis, customer understanding, innovation and process optimization.

Why is data quality essential to organizations?

Each company or organization has its own challenges, depending on its strategy, environment and objectives. Data comes into play in many cases.

For informed decision-making and performance analysis

The primary utility of data for organizations lies in its ability to provide crucial information for informed decision-making. Data can be used to assess past performance, understand current trends and predict future developments. Thanks to in-depth analysis, managers can make more informed strategic decisions, based on facts rather than conjecture.

To understand and manage customer relationships

Customer data is a goldmine for organizations. It provides invaluable information on customer behavior, preferences, purchasing habits and needs. By analyzing this data, organizations can better understand their target market, adapt their products and services accordingly, and deliver a personalized and satisfying customer experience.

To facilitate innovation and the development of new products/services

Data can drive innovation by providing information on market trends, popular products, customer feedback and unmet needs. Organizations can use this data to develop new products, services or enhancements that meet market expectations and stand out from the competition. In this way, data facilitates adaptation to changing consumer needs.

For forecasting and planning

Real-time and historical data enable organizations to make accurate forecasts and plan future activities.this includes inventory management, demand planning, budgeting and sales forecasting.based on data, organizations can better anticipate market fluctuations, adjust their strategies and optimize their performance. This is essential if they are to be competitive. That's why data is so important for organizations.

Why and how to ensure data quality?

Beyond data ownership, it's important to manipulate quality data in order to extract its full potential. But how can we guarantee the quality of our data assets?

Data reliability: setting up and maintaining solid governance to guarantee data quality and accuracy strengthens users' confidence in the information they consult and use. One of the key aspects of data governance is data quality.

The expression "garbage in, garbage out" expresses the importance of guaranteeing data quality right from the start. Data quality concerns the accuracy, reliability, consistency and relevance of the information it contains.

As part of data governance, it is essential to define policies and processes for identifying, cleansing, and removing non-compliant or unnecessary data.

Which criteria define good data quality?

Data quality refers to the extent to which data is relevant and accurate for its intended use by its users. Good data quality is essential to ensure the credibility of analyses, reports and decision-making based on these data. Below are some key characteristics of data quality:

Criteria for a good data quality

What are the best practices and tools for ensuring good data quality?

Data quality must be a cross-functional issue within the company

Who is responsible for ensuring data quality? We are convinced that data quality should not be the sole concern of data or it teams. It should be everyone's business!

To ensure data quality when implementing data governance, a data culture needs to be formed within the company or organization, to raise awareness of various topics such as overstocking, sources, classification of recorded data, etc. But also, and of course, to ensure that data quality is a priority. But also, the quality of recorded data.

For example, it could be the entry of contacts at a trade show (sales team), specific customer requests (sales administration team), stock updates (production team), and so on. Data generation is carried out at all levels, in the different professions, i.e.

Quality control must be applied throughout the data lifecycle.

Technically, here's an example of how to implement data quality monitoring. It requires an approach involving analysis, classification and continuous processing throughout the data lifecycle. All existing data needs to be carefully examined, catalogued and processed, while new data requires initial assessment, appropriate classification and processing according to its nature.

It is crucial to recognize that data is not fixed once it has been processed. It can become obsolete, vulnerable, non-compliant or sensitive over time. Consequently, constant or recurring vigilance is required to ensure the security, compliance and relevance of data as it evolves.

By adopting operational data governance, organizations can ensure that every piece of data is assessed on a regular basis, while implementing appropriate measures to maintain its value. This iterative process ensures robust data governance and promotes informed decision-making based on reliable information.

Adopting an iterative approach

The implementation of an iterative approach is designed to ensure continuous analysis, observation and improvement. For an overview of how to maintain data quality, here's an example of an iterative approach to monitoring data quality, with the different stages and the many solutions/tools available for each.

data quality monitoring approach

Using the right tools at the right time

Knowing the different stages in the data lifecycle and the continuous improvement approach (above) makes it easier to match the right tools to each stage.

Data catalog solutions

This is a tool for centralized cataloguing and organization of a company's data. It provides a consolidated view of the various data sources available, making it easier to find and access information. In addition, it provides detailed metadata on data, such as its origin, structure and quality, helping users to understand and assess its relevance before exploiting it.

Data lineage solutions

A data lineage solution is a tool for tracking and documenting the path of data from its initial source to its final use. It provides complete visibility of the transformations and intermediate stages undergone by data, enabling understanding of its origin, quality and traceability. With data lineage, users can make informed decisions and have confidence in the integrity of the data they use.

Data discovery solutions

A data discovery solution is a tool that facilitates the exploration and identification of relevant data within an it environment. It enables users to search, browse and understand the various data sources available, highlighting patterns, relationships and models that exist between data sets. This helps to uncover new perspectives and make informed decisions based on the information found.

Data quality (dq) analysis tools

A data quality analysis tool detects which data in all or part of a data set is valid and usable. If this is not the case, the data is exposed for completion, deletion or updating.

Master data management systems

Master data management (mdm) aims to ensure that an organization uses an accurate, up-to-date and high-quality version of its data. The aim is to ensure that all the organization's decisions are based on this reliable version of the data.

Data sharing solutions

A data-sharing solution centralizes apified data (datasets) to facilitate data sharing between users and re-use within innovative digital products.

Data transformation tools

A data transformation tool is a software program or platform that enables companies to solve compatibility problems and improve the consistency of their data. By performing various functions such as data aggregation, sorting and cleansing, these tools convert data into a format compatible with the destination system. This optimizes and integrates the data, making it ready for processing.

Data visualization and reporting tools

A data visualization and reporting tool is software or a platform that enables users to explore, analyze and visually present data in a clear and intuitive way.

In short, data governance implies a responsibility shared by all players in an organization; it is effectively a cross-functional approach that aims to better understand the lifecycle of data and its usage.

But while data governance is indeed first and foremost a corporate/organizational culture, technology and tools have key and complementary roles to play, as they facilitate various actions around data.

Do you have 100% confidence in your data? Dawizz can support you in various stages of your data governance, including quality control.

Ask for a demo