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Data Governance as a prerequisite for your holistic data management

Berlin, April 2, 2020 | In order to increase the data quality of company data and to secure it in the long term, the introduction of data governance is necessary. But what is part of the well sounding term and how does data governance offer added value for organizations and companies?

What is Data Governance? 

“Data Governance is the framework for data quality management (DQM) and defines which roles with which responsibilities take over the tasks of data quality management.”

Standards and responsibilities for handling important (master) data in a company should be anchored in the form of data governance guidelines. The guideline defines which areas of responsibility take over the tasks in data management and how the identified standards are adapted (e.g. for data formats used). As a framework for data quality management, data governance can contribute to the targeted use of the resource “data” in value chains and to the continuous strengthening of data quality in the company.

Is Data Governance of interest to your company?

Do you have a full overview of your data and are you already able to get the maximum potential out of your company data?
With data governance, you can answer this question with a decisive YES. Data Governance is not just a project, it changes the culture of your company as part of the digital transformation towards a data-driven enterprise. The economic benefits of efficiently managing your company data will be visible and measurable in data quality management.
This is why it is also necessary to anchor it in your corporate strategy. Work with a clear roadmap and convince all departments and decision makers.
Although there are different approaches to the form in which data governance can be introduced and implemented in companies, it should always be integrated into your daily business. This also means that the implementation of data governance must be individually worked out and adapted to the company structure and goals.

Data Governance contains three basic design elements –

  1. The designation of necessary tasks within the data quality management
  2. The identification of roles and the definition of the responsibilities of each role
  3. The company-wide implementation of processes for the fulfilment of DQM tasks

The overview shows the different dimensions of data governance:

 data governance       Architecture and tools: Technological support of data governance processes   Assets: Definition and identification of data and data quality standards   Security: Definition of standards for data security; access determination and procedures in case of violations   Roles and responsibilities: Definition of roles and responsibilities for data-driven processes   Compliance: Ensuring compliance with data management requirements and guidelines   Processes: Monitoring of data processes and decisions about management and use of data  Copyright innoscale AG    Data governance processes with DataRocket  Real-time transmission Data sets are transferred to the leading target systems in a very performant and efficient way.   Web-based input forms The use of existing value lists from source systems and upload of own value lists is possible.   System open To integrate data governance into the existing system landscape, workflows can be accessed via a link on the Web.   User-friendly Users receive automated notifications when workflows are assigned to them.   Quality management DataRocket acts as a quality gateway in the data governance process.   Clarity The process cockpit provides an overview of the workflows to be processed. Workflows can be assigned and monitored there.  Copyright innoscale AG
 data governance       Architecture and tools: Technological support of data governance processes   Assets: Definition and identification of data and data quality standards   Security: Definition of standards for data security; access determination and procedures in case of violations   Roles and responsibilities: Definition of roles and responsibilities for data-driven processes   Compliance: Ensuring compliance with data management requirements and guidelines   Processes: Monitoring of data processes and decisions about management and use of data  Copyright innoscale AG    Data governance processes with DataRocket  Real-time transmission Data sets are transferred to the leading target systems in a very performant and efficient way.   Web-based input forms The use of existing value lists from source systems and upload of own value lists is possible.   System open To integrate data governance into the existing system landscape, workflows can be accessed via a link on the Web.   User-friendly Users receive automated notifications when workflows are assigned to them.   Quality management DataRocket acts as a quality gateway in the data governance process.   Clarity The process cockpit provides an overview of the workflows to be processed. Workflows can be assigned and monitored there.  Copyright innoscale AG
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1. Naming necessary tasks within the DQM

For the success of a DQM on the one hand the formulation of clear tasks and on the other hand the definition of goals to measure success is necessary.
First, it must be clearly described which data, systems, applications or business processes are to be included. Practical experience in the development of data quality management shows that only rarely can all data quality problems be addressed directly. Here, prioritization on the most important and business-relevant data is necessary.
Tasks to be defined in data quality management can be, for example, the development of a data quality strategy or the definition of data maintenance processes.
Agreement on objectives is necessary to measure the success of cleansing and monitoring and to demonstrate the direct benefits for the company.

2. Identifying roles and defining the responsibilities of the individual roles

A data governance strategy is characterized by different roles. These are positions that are held by employees to perform specifically defined tasks. The definition of roles and responsibilities is a guarantee for the establishment of helpful processes and the anchoring of data governance in active day-to-day business.
To ensure the quality of data throughout its entire life cycle, the interaction of strategic and operational implementation in all areas of the company as well as data governance and data stewardship is necessary. Data governance as a framework for data quality management must be anchored in the strategic corporate objectives. Data stewards ensure the operational implementation.
Typical roles in data governance can be:

  • Data Stakeholder – responsible for problem solving
  • Data Governance Officers (DGO) – specify data quality standards
  • Data Stewards – supervise and implement data quality standards

3. The company-wide implementation of processes for the fulfilment of data quality management

Company-wide data governance processes define the responsibilities for the identified data quality management tasks. Clearly defined responsibilities allow the DQM to be successfully advanced.

Data quality management ensures the success of the company

The introduction and implementation of data governance offers companies the great advantage of a permanently anchored culture for handling data as a valuable resource. If the implementation is anchored by roles and responsibilities in all relevant departments, data governance can make a decisive contribution to the digital transformation of companies.

Conclusion:

Data Governance offers these advantages for the data quality in your company:

  • The proactive approach by introducing data governance: Data errors are avoided from the outset.
  • Long-term implementation: The modification and establishment of processes and the appointment of roles and responsibilities shows long-term and sustainable success.
  • Golden Records: By creating clear and high-quality data records, incorrect data and duplicates are eliminated.

The right product for your data governance processes:

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