Data governance forms the foundation for company-wide data management and enables the effective use of trusted data. Effective management of data is an important task and requires centralized control mechanisms.
To help end users better understand this complex topic, the following points are presented:
• What is data governance? • Why is data governance important • How seriously do companies take this issue? • Challenge • Best practices
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** What is data governance? 阿鲁纳恰尔邦
Data governance includes the people, processes, and technologies needed to manage and protect a company’s data assets to ensure universally understood, correct, complete, reliable, secure, and discoverable corporate data.
Data governance covers the following topics:
At its core, data governance is about establishing methodology and an organization with clear responsibilities and processes to standardize, integrate, protect, and store corporate data. The main objectives are:
• Reduce risk • Establish internal rules for data use • implement compliance requirements • improve internal and external communication • add value to data • facilitate the above management • Reduce costs • Help ensure the continued viability of the company through risk management and optimization
Data governance initiatives always affect the strategy, tactics, and level of operations of the enterprise (see figure below). To effectively organize and use data across the company and in coordination with other data projects, the data governance process must be viewed as an ongoing iterative process.
Data Governance Level
In addition to responsibilities, the following aspects of any data governance program must be clarified (see figure below).
Data governance
• Organization (the “where” and “who”) • Business (the “what”) • Technology (the “How”)
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**_ Why is data governance important? _ * *
Most companies already provide some form of data governance for individual applications or lines of business, although it is not necessarily fully institutionalized. As a result, systematic introductions to data governance often evolve from informal rules to formal controls.
Typically, formal data governance is implemented once a company reaches a size where cross-functional tasks cannot be effectively implemented.
Data governance is a prerequisite for many tasks or projects and has many obvious benefits:
• Consistent, consistent data and processes across the organization are a prerequisite for better, more comprehensive decision support. • Improve IT scalability at the technical, business and organizational levels through clear rules for changing processes and data; • Central control mechanisms have the potential to optimize data management costs (increasingly important in the era of data set explosions); • Improve efficiency by using synergies (for example, by reusing processes and data); • Greater confidence in data through quality assurance and certification of data and complete records of data processes; • Meeting compliance standards, such as Basel III and Solvency II; • Protect internal and external data by monitoring and viewing privacy policies; • Improve process efficiency by reducing lengthy coordination processes (e.g., through clear requirements management); • Clear and transparent communication through standardization. This is the premise of enterprise-wide data-centric planning; • In addition, the special nature of each data governance initiative brings special benefits.
More than ever, data governance is essential for an enterprise to remain responsive. It is also important to explore new and innovative business areas, such as through big data analytics, which does not allow for sustained backward thinking and overhauling of structures.
Currently, the most important drivers that are causing companies to rethink their current approach are:
, establish a data-centric views to support digital business model, enterprise data quality and master data management, data in a large data environment manageability, setting standards in order to enhance the external impact (such as mergers and acquisitions) reactions, self-service BI (SSBI) : a user wants to independent of compliance, IT carries on the analysis. Transparent and easy to understand data processes to comply with legal requirements
In addition to these drivers, there are many other developments and requirements that make data governance increasingly important.
Examples include actionable BI, advanced analytics, social media, 360-degree customer view, BI in the cloud or as a service, information policies, and compliance with data protection guidelines (SCM, CRM) for internal and external use of data.
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**_** HOW BI professionals view data governance **_**
The data in BARC’s BI Trend Monitor confirms the importance of data governance
Data governance is most relevant for large enterprises, the financial sector, and the UK and Ireland.
It is less popular with enterprise users and small and medium-sized companies.
Data governance is most relevant for large enterprises, the financial sector, and the UK and Ireland.
It is less popular with enterprise users and small and medium-sized companies.
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**_**_** Data governance challenges **_** **
The relevance of data governance is obvious. Still, despite the advantages, many companies are afraid to implement data governance programs, perhaps because of the complexity of the assumptions or the overall uncertainty.
Implementing a data governance plan is never easy. Here are some of the biggest obstacles to implementation:
organization
Data governance requires an open corporate culture where, for example, organizational change can be implemented, even if it only means naming roles and assigning responsibilities. As a result, data governance becomes a political issue because it ultimately means assigning, delegating, and revoking responsibilities and capabilities. A sensitive approach is needed here.
Acceptance and Communication
Data governance needs to be embraced by the right staff in the right place through effective communication between the parties. In particular, the project manager needs to understand technical and business terminology, terminology, and preferably the overall concept map of the company.
Budget and stakeholders
It is still often difficult to convince stakeholders in an organization of the need for and budget for a data governance program. In addition, change is often blocked by deep roots, but resources that are not directly visible in the business can make up for the lack of well-functioning processes and information processing.
Standardization and Flexibility
Companies need to be flexible to respond to rapidly changing demands. However, it is critical to strike the right balance between flexibility and data governance standards based on each company’s business needs.
A balance between chaos and repression
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**_**_**_ data governance best practices and success factors _**_**_**
Implement a data governance plan
Data governance is not a big initiative, and it does not work this way. In contrast, global plans are highly complex and long-term projects. As a result, they run the risk that participants may lose trust and interest over time.
Therefore, it is recommended to start with manageable or application-specific prototype projects and iterate. In this way, the project will remain manageable and the lessons learned can be applied to more complex projects or to extend data governance initiatives in the company.
Typical project steps are:
• Identify goals and understand the benefits; • Analyze current status and incremental analysis; • Come up with a roadmap; • Convince stakeholders and budget projects; • Develop and plan data governance plans; • Implement data governance plan; • monitoring.
Not only do these steps have to be repeated for each new program, but they also need to be repeated if changes are made.
Before starting any data governance process, always answer questions about the cause of the project to avoid unnecessary additional work. Similarly, existing processes should be evaluated to determine whether they can adapt to new requirements within the framework of a data governance plan, rather than starting with new process development that may not be necessary.
The following tools aid in the implementation of the data governance program:
Data Management (DAMA) framework
The DAMA framework provides direction for identifying disciplinary and functional groups – see www.dama.org.
BARC 9 field matrix
BARC’s “9-field Matrix” aims to determine the current state of an organization’s approach to data management and derive a road map from it.
The three layers of the company (strategic, tactical and operational) and its organizational, business and technical aspects form the basis of the matrix. With its structure, data management projects can be fleshed out with specifications of the topics, processes, roles, and tasks involved.
It should be noted that level, organizational, business, and technical projections and roles in the company should also be very specific. However, the matrix applies to any topic in the data management world.
BARC 9 field matrix
The DAMA framework provides written standards for all relevant data management topics. They are assigned to a field in the BARC 9 field matrix.
This allows the current state of each field to be compared to the target state in a structured manner. In this way, increments can be identified, priorities can be set, and a road map with specific actions can be drawn.
An example
Roles are essential to every data governance program. Today, software tools provide data governance templates for metadata management, data quality, master data management, and data integration.
The roles are slightly different, but the core roles are always the following:
• Data Governance Board (Steering committee/Strategic level) • Data Governance Board (tactical level) • Data Manager • Data owner • Data manager • Data user
The template and the library
Templates go one step further than role models. They include, among other things, best practice processes, decision rules, data quality rules, key metrics, and task types.
“Data Governance” platform
The data governance platform provides different functional blocks for data quality, master data management, data integration, metadata management and data protection.
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The following tips will help you implement a data governance plan or program:
• Do not initiate a big bang plan, but understand data governance as a continuous, iterative process of sub-projects; • Start with small pilot projects and bring those lessons into the company; • Data governance programs can run for years. However, the duration of a single project shall not exceed 3 months; • Set clear, well-thought-out goals; • Winning recognition is a top priority. Stakeholder engagement and process transparency are key. Recommend open and transparent communication with all stakeholders and don’t hide the agenda; • Do not reinvent the wheel, but use existing templates, models and best practices in the market, whether through software tools, frameworks and libraries, or through consultants; • Correctly identify roles in the company. The communication skills of the program manager are particularly critical, and he must take political issues and sensitivities into account when introducing data governance initiatives into the organization. • Examine and consider the reasons why established processes and solutions are not sufficiently simplified; • Evaluate the data governance platform; • Establish clear structures and responsibilities;
• Establish a comprehensive approach to documenting organizational best practices.
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