The transition to a data centric approach is a major challenge faced by companies today. The use of data no longer concerns only certain technical teams but all of a company’s businesses. On a daily basis, each employee handles data of varying complexity and sensitivity. In addition, the emergence of big data has led to a dramatic increase in the volume and complexity of data. To succeed in this transition, we must first and foremost consider data as an asset that we must know how to take advantage of. Valuing your data assets requires, above all, knowing them, securing them, and guaranteeing their reliability and quality. Organizing this work is data governance.
The governance of (those working with) the data includes the organizational structure the procedures and tools that enable organizations to manage their data as an asset to increase revenue and productivity, while reducing their security risks and regulatory compliance.
Data as an asset of the company
Assets are everything that a company owns and brings value to it. These assets can be real estate, machinery, vehicles, technological equipment, patents, but also financial assets such as stocks or bonds that provide a longer term return.
Similarly, data is an asset because it generates value immediately or potentially later if the opportunity arises. For example, collecting as much data as possible on its customers – in compliance with the GDPR or CCPA – allows it to better respond to its present or future needs. This data is therefore an asset that will generate value for the company, in the same way as the other assets usually recorded.
Benefits of data governance
Globally, data governance allows to increase the performance of a company following these levers:
- Increase in turnover
- Cost reduction
- Risk reduction
Increased sales and productivity
Data governance helps increase revenue by improving data management, which leads to better overall business productivity. Here are the axes that seem to us the most important:
Better knowledge of data and its life cycle
Its essential features are:
- Documentary repository of all data assets
- Glossary of terms and data meanings
- Data lineage through the different bricks of the information system.
You will learn more in our dedicated article.
Better roles and responsibilities
Data governance also allows for greater accountability by assigning permissions and roles, making it much easier to determine who is responsible for what data.
We generally distinguish between data owners, who make decisions about the data in their business domain, and data stewards, who are responsible for the day-to-day documentation, quality and, more generally, compliance with the rules of corporate data governance.
Better data quality
Data is of good quality when it represents reality accurately. And only in this case, its use is reliable.
Data governance facilitates data quality management within the organization. It is a discipline that establishes quality standards and rules to control it: measurements, prevention, data cleansing…
Having good quality data increases operational efficiency.
Improved operational efficiency
If you have well-managed data and the ability to analyze it, you can improve operational efficiency in many areas.
- By accurately measuring which customers or products are best for the company, you can better target your investments.
- The analysis of a company’s processes(process mining) reveals the processes for which the company is losing money and allows for optimization. However, this analysis requires looking at all data, including application histories.
Better decision making
Through its ability to orchestrate data management at a global level, data governance facilitates and improves the use of data, leading to better decisions with greater confidence.
A l’inverse, une analyse qui se base sur des données erronées, incohérentes ou incomplètes implique une prise de décision biaisée qui contribue à des erreurs stratégiques et des pertes financières parfois importantes.
Improving the use of data thus contributes to reducing costs:
- The data catalog can save tens or hundreds of man-days – depending on the size of the company – in searching for data needed to develop applications or reports.
- Data quality management reduces the amount of time wasted and therefore the cost of unreliable data.
- Finally, data governance helps control data debt, which is the cost of addressing data issues.
Data governance also reduces at least three types of risk:
- Operational risks: interruption of service, loss of data, loss of customer confidence, etc.
- Cybersecurity risks: data leakage, unauthorized access to confidential or even strategic data.
- Regulatory risks: non-compliance with data protection laws that may result in fines, penalties or litigation costs related to private or civil lawsuits
Disadvantages and difficulties to consider
Implementing a data governance strategy or program also means challenges and drawbacks to consider. Thus, the following points should be considered:
- Scope. In order to be implemented properly, data governance must take place throughout the enterprise. That’s why it’s a major project to launch and manage.
- Resources. Successful data governance requires attention, time and resources.
- Coordination. Data governance is part of a broader policy of IT governance, or even corporate governance. That is why coordination between the two must be put in place.
- Commitment. Implementing data governance can be difficult because of employees. Indeed, it can be difficult to interest them. To do this, they must be encouraged, motivated, and shown that they have important roles, objectives, and that they are the main players in this strategy.
- Flexibility. Data governance efforts must be able to adapt to different team needs and must be simple for users. In itself, data governance should not interfere with the work of the company’s employees.
- Implementation. Choosing the right technology and management tools to succeed in your data governance strategy can be a difficult task.
However, all of these issues can be overcome and should in no way deter you from deploying data governance in your organization.
Difference between data governance and data management
Data governance is to be differentiated from data management.
Data management is the set of activities that enable the management of data within an enterprise. It includes the following activities:
- Project management
- Data architecture (functional, application and technical layers)
- Data modeling
- Database management
- Storage management
- Master and reference data management
- Data security
- Data quality management
- Business Intelligence, also known as BI
Data governance is the link between the company’s strategy and the management of its data, which it supports without constraining.
Here is a first approach to data governance, which presents the general concepts without going into details.
To date, many frameworks have been proposed by major strategy consulting firms. They are usually complex and expensive to implement.
What you need to remember is that there is no simple framework that can be adapted to all types of companies. Each organization must therefore implement its own data governance.
At Data Éclosion, we advocate a simple, agile and modular approach that meets the client’s priority needs.