Data governance has become a major issue for companies, which are concerned about fully exploiting their data while also fulfilling various quality and compliance criteria. But what does data governance entail? How do you implement a data governance strategy? This is what we invite you to discover.
Data management and data governance: what is the difference?
People often confuse data governance and data management. But although they are closely related, these two concepts are quite different.
What is data management?
Data management refers to the creation and implementation of architecture, policies, and procedures to manage the entire life cycle of an organization’s data. The implementation of such measures is essential to analyse increasingly complex and large amounts of data. If data is considered a key business asset, it must also be managed as such.
In concrete terms, data management combines various elements:
- Data preparation: the process of cleaning and transforming raw data to ensure accurate data analysis. The importance of this first step is sometimes underestimated, as companies are too quick to produce reports and analyses, and thus, end up making the wrong decisions using the wrong data.
- Data pipelines: used to automatically transfer data from one system to another.
- Data warehouses: facilitate data analysis by consolidating all data sources.
- Extract Transform Load (ETL) processes: transform data and upload it to an organization’s data warehouse. ETL processes are often automated and require pipelines.
- Data catalogues: help you manage metadata more effectively, while also making it easier to search and track data.
- Data architecture: the data flow management structure.
- Data security: refers to the processes in place to protect data from unauthorized access and the risk of corruption.
- Data governance: consists in defining policies and procedures to maintain data security and compliance. Data governance is therefore a sub-area of data management.
What is data governance?
Data governance helps you answer questions, such as:
- Who owns the data?
- Who can access what data?
- What security measures are in place to protect data and privacy?
- Does the data comply with the latest regulations, such as the General Data Protection Regulation (GDPR)?
- What data sources can be used?
It should also be noted that data governance concerns both the data itself and “content”, i.e. dashboards, graphics and other forms of analysis used to make the data speak. Data governance therefore consists in implementing models in different areas, ranging from content management to data source management, including data security.
In general, data governance is based on four main principles:
- Data quality: accurate, complete and reliable data is the cornerstone of a data-driven company. Data quality management is therefore a key pillar in any governance policy.
- Data security and compliance: data sources must be defined and classified according to their respective risk levels. You must then create secure access points, striking the right balance between security and the user experience.
- Data stewardship: involves monitoring how teams use data sources. “Stewards” must lead by example to ensure data access, security and quality.
- Data transparency: all procedures in place must be fully transparent. As a result, analysts and business users must be able to easily determine where their data has come from and whether it has any special characteristics.
What are the benefits of data governance?
Now that we have clearly explained the concept of data governance, we must ask ourselves the most important question of all: what’s its purpose?
Implementing a data governance strategy can bring many benefits to a company:
- Higher-quality, comprehensive and consistent data for thorough analyses and informed decision-making.
- An overview of the data, allowing company players, who may speak a common language, to understand it more easily.
- Data mapping, so that users can access each piece of data instantly and to help them find exactly what they are looking for.
- Data that complies with regulatory requirements regarding confidentiality and privacy at all levels. Having a data governance policy in place is therefore essential to ensure compliance with the GDPR (General Data Protection Regulation).
- Better data management: data governance is an integral part of data management, thereby contributing to the implementation of best practices, in order to manage data more efficiently on a daily basis.
4 steps to ensuring successful data governance
1) Set out your data governance objectives
A data governance strategy must be aligned with your company’s overall strategy and ultimately deliver tangible benefits. Data isn’t an end in itself, but rather a resource that organizations can exploit to create value and become more competitive.
To be truly effective and suitable, a data governance policy must therefore be focused on business needs and processes, with clearly defined objectives.
2) Carry out data mapping
To implement a data governance policy, you must compile an inventory of all the data owned by the company, then map it. This key step will help you answer a number of questions:
- What are the different types of data available?
- Who are their users?
- How is the data processed and transformed?
- Where is the data stored?
- Are there any data quality, security, or privacy concerns?
This allows you to obtain a clear and accurate picture of the data at your disposal, as well as its nature and quantity. You will therefore be able to effectively structure your data governance strategy and better define its objectives and scope.
At the end of this mapping work, you will have a large amount of information (metadata) that will help you define and characterize your data. This metadata must be perfectly managed and structured to guarantee a successful data governance policy.
To achieve this, you must use a data catalogue, allowing you to gather all the metadata and consult it easily. This gives the company quick access to any of its data, as well as all the corresponding information: origin, transformations, uses, etc.
Through data mapping, all users speak a common language, greatly facilitating data access.
3) Define a specific framework to ensure more effective data governance
Data governance is a global strategy that concerns all company departments: you must therefore define a solid framework to support its deployment.
The rules in place to ensure data compliance and security must be clearly stated. New ways of working must also be established to ensure quality and effective data management. It is also essential to determine the rights and responsibilities of different users with respect to the company’s data.
The roles of employees concerned by the data governance policy must also be clearly defined, even if that means updating their job descriptions. This will not only allow you to clearly explain the duties of each person, but also to transmit your data culture throughout the organization.
All teams are therefore encouraged to use data in some way or another and to adopt the best data practices. You may wish to hold meetings or workshops dedicated to data governance to raise awareness of this topic among employees.
4) Integrate efficient data governance tools
To carry out a data governance program on a large scale, companies must be able to count on efficient tools adapted to their needs. A Business Intelligence solution, in particular, is not only essential to guarantee data quality, security and transparency, but also to fully exploit data.
If companies use a BI tool, they are guaranteed access to reliable, comprehensive and exploitable data, which they can then use to perform highly accurate analyses. The result: better day-to-day decision-making and a clearer future vision.
The main aim is to have access to qualitative, secure and compliant data, but implementing a data governance policy is a complex process, especially since it is often supported by a Business Intelligence project, hence the importance of following these four key steps.
