Data is becoming the central element of success for companies. Data governance is essential to maximize the value of data assets. Until now, many companies have underestimated the importance of the discipline and thus hindered their own success. We explain what data governance means for business practice, how it differs from related concepts and what is important during implementation.
What is Data Governance?
A uniform definition of data governance does not yet exist. However, there is increasing agreement that data governance (also: governance of data) is not an exclusively technical discipline. In the following, we will use the following definition:
Data governance is the organizational heart of data management. It sets a set of rules and frameworks for all data-related technologies, processes and responsibilities for handling data within the company and across company boundaries.
So data governance answers questions like: What data quality are we aiming for? What is the most important company data and who do employees turn to when they have data requirements?
What Are the Benefits of Data Governance in a Company or Organization?
Data are important assets. It provides information on how processes can be optimized, is the basis for expanding business models and developing new products. Therefore, it is necessary to protect data and make the best possible use of it. The implementation of a data governance strategy enables exactly that.
- Improve data quality and consistency: The central and strategic design of the IT infrastructure makes it easier to implement work processes that ensure the quality and validity of data.
- Create transparency: Decisions in data management and data maintenance can be communicated more easily and are better accepted by employees if they follow a comprehensible, strategically structured data governance framework.
- Ensure legal compliance: The legal requirements for data protection and data security change regularly. A centrally organized governance of data helps to act legally compliant throughout the company at all times and to avoid legal consequences and fines. Data compliance can also be effectively improved with the help of governance structures.
- Simplify data maintenance: Data maintenance efforts can be reduced with a clear data governance strategy and dedicated data governance tools. Splitting responsibility into clearly distributed data governance roles speeds up input processing and reduces costs by eliminating duplication.
- Unlock value creation potential: Ultimately, enterprise-wide governance of data helps increase profits. This is because it helps companies to make the best possible use of knowledge from data - for higher revenues and more cost-efficient processes.
Data Leadership For Experts
Thank You, Managers. From Here On, It Gets Boring…
The experts among you will probably appreciate that we also provide insight here
that goes well beyond the necessary basic understanding. So, let’s go!
What Does a Data Governance Framework Mean and Involve?
Ideally, a company's data governance is derived from the overarching data strategy and is embedded in the corporate strategy. A framework can help to consider all aspects of the governance of data.
A data governance framework defines more than just a data governance strategy. There are varying opinions on what other elements the framework should include. We recommend six building blocks for a data governance framework:
What vision and mission is the company pursuing with its data governance? What specific goals are to be achieved? Are these in line with the general corporate strategy and the corporate goals?
Standards, rules and processes
What data handling policies should apply to employees? Which control instances will be established? Which taxonomies are maintained and which metadata?
Measurement and monitoring
Which tools and processes are used to evaluate the different data? How will progress in implementing the changes decided be measured? How should issue monitoring be organized?
Who is part of the data governance organization? Who assumes which roles and responsibilities? Who assumes data ownership, who assumes data stewardship? What escalation levels and arbitration bodies will be established?
Which data architecture is chosen? How is data security and the desired data flow ensured? Which technological standards ensure high data quality?
Which communication strategy will be rolled out? When should information about changes in the governance of data be provided individually, when company-wide? How will training and knowledge management be organized?
What Are the Typical Data Governance Roles?
Companies need to fill various roles for the governance of data. A consensus on role descriptions and designations does not exist in professional circles, but some responsibilities can be found under different terms in most frameworks. Here is an exemplary overview of the most important roles:
Data Governance Committee
The committee controls the governance of data of a company at the highest management level. It decides on strategies, processes and policies, sets goals and monitors the progress of the defined measures in the organization.
Chief Data Officer
He is often a member of the management or reports directly to it. His tasks include developing the data governance strategy and monitoring its implementation. Unlike the Chief Technical Officer (CTO), he focuses less on IT aspects and more on process-related and strategic issues of data management and data quality.
Heads of department often assume the role of data owner. They bear the content and process-related responsibility for certain data of a specialist and task area (data ownership) and work closely with the data stewards.
A data steward is appointed in each specialist department. The employee ensures the technical provision of data, evaluates requirements and solves technical problems. He ensures the quality of data and data sources on a technical level.
An Example of Data Governance
Decisions that affect corporate strategy also affect corporate data – and should be considered in the governance of data.
If the CEO decides to embark on a growth course and acquire further companies, this means a massive growth of the data basis in a possibly short period of time. The data strategy must address this strategic realignment, for example by defining goals for handling new data. Standards for integrability in turn have implications for data modeling. These changes must also be communicated to internal stakeholders so that the operational managers can develop plans for integrating the new data and adapt their workflows.
Conclusion: Young Discipline With Key Function
Data accumulates in every company. This means that data governance is also a topic for every company. At least it should be, since the importance of data for business success is rapidly increasing. The reason: technical progress, which can process huge amounts of data faster and enables ever more intelligent analyses and evaluations.
So far, a large proportion of German companies are giving away potential that lies in their data. According to a DEMAND study from 2019, only 2 percent of the companies surveyed had organized themselves in such a way that they make the most of their data. Only 4 percent had established company-wide data governance structures whose effectiveness was regularly reviewed.
Although the Corona pandemic has led to a surge in digitization, German companies will probably still have some catching up to do in their governance of data in 2021. Individual data governance processes are often in place, but there is a lack of an overarching and uniform framework, a data governance framework. However, this is necessary in order to use data competitively as a business asset in the future.
Frequently Asked Questions About Date Governance
Data Governance vs. Data Management: What’s the Difference?
Data governance defines technologies, processes and responsibilities for handling corporate data, while data management describes the implementation of the specifications. The governance of data starts at the strategic level, while data management refers to the operational-technical implementation.
What Does a Data Governance Implementation Plan Look Like?
At the beginning, there is always an analysis of the status quo: What data is available and in what quality? Then, a data governance framework should be filled with life, i.e. processes, roles and technologies should be defined. The last step is the successive integration of the measures into the operational business. Parsionate has developed a roadmap that supports companies in a structured way in implementing and optimizing their data governance in a short time.
What Is the Difference Between Data Governance and Master Data Management?
Master Data Management refers to the procedural and technical organization of a specific type of company data, the master data. The aim of master data management is to identify the data throughout the company, to simplify its management and to make it reliably available to all users who can benefit from the knowledge gained from the data. In doing so, the specifications from data governance are taken into account. It sets the strategic framework for master data management.