Data governance: What should be governed and managed?

In the digital age, data has become a core asset of enterprises, but the management and use of data face many challenges. This article deeply explores the core content of data governance - "what to govern" and "what to manage", and comprehensively analyzes the key links of data governance for your reference.

In the wave of digitalization, data has become the core asset of enterprises and organizations. As a key means to unlock the value of data, data governance is receiving more and more attention.

So, what exactly is data governance?

1. What is Data Governance?

Data governance is a management system that ensures data availability, consistency, security, and compliance through strategies, processes, roles, division of labor, technologies, and tools throughout the entire life cycle of data. Its core is to [solve problems and maintain order], that is, to solve data problems (governance) and standardize data management processes (management).

Data governance involves the entire life cycle of data, including data generation, collection, cleaning, storage, processing, application, sharing and destruction.

For example, the customer data of a financial institution is scattered across multiple business systems, with problems such as inconsistent data and repeated entry. This not only leads to inefficient customer service, but also affects the effectiveness of risk management and precision marketing.

By implementing data governance, the institution established unified data standards and master data management systems, integrated scattered data, and achieved consistency and integrity of customer information. This greatly improved customer service response speed, made risk management more accurate, and provided strong support for personalized marketing, effectively improving business competitiveness.

The above cases involve: entering customer information, aggregating customer information from various business systems, cleaning customer information, storing customer information, and issuing customer data as master data to various business systems, to the management of adding, deleting, modifying and checking master data customers, throughout the entire life cycle of customer data.

Data governance is not only a technical issue, but also a management issue, which requires comprehensive management from multiple aspects such as organization, system, process, technology, etc. Through a series of activities such as formulating data management strategies, establishing data standards, standardizing data processes, and ensuring data security, effective management and utilization of data assets can be achieved.

2. What does data governance govern?

The core content of data governance [what to govern] includes: governing data quality issues, governing data security issues, governing data compliance issues, and governing data sharing and circulation issues.

Data quality issues

Ensure data accuracy, completeness, consistency and timeliness.

Data quality is one of the core goals of data governance, and it is related to whether data can truly and completely reflect objective facts. To improve data quality, enterprises need to formulate strict data standards and specifications, and clarify the definition, format, and value range of data. At the same time, use technical means such as data cleaning and data verification to pre-process data, remove noise and erroneous data, fill in missing values, and ensure the accuracy and completeness of data. In addition, establish a data quality monitoring and evaluation mechanism, regularly check and analyze data quality, and promptly discover and solve problems.

For example, in the e-commerce industry, the accuracy of product inventory data directly affects order processing and customer satisfaction. If the inventory data is inaccurate, it may lead to overselling and damage customer trust.

Data security issues

Protect data from unauthorized access, use, disclosure, destruction or alteration.

Data security is an important guarantee for data governance. With the acceleration of digitalization, data security faces increasingly severe challenges, such as hacker attacks, data leaks, and malware.

To ensure data security, enterprises should take a series of measures, such as data encryption, access control, identity authentication, security audit, etc. Data encryption can convert sensitive data into ciphertext to prevent data from being stolen during transmission and storage; access control limits the scope of access to data by setting user permissions; identity authentication ensures that only legitimate users can access data; and security audit records and analyzes data operations to promptly identify potential security risks.

For example, in 2017, the US Equifax credit reporting company suffered a data breach, in which the personal information of about 147 million consumers was leaked, including sensitive information such as names, social security numbers, and dates of birth. This incident not only caused huge losses to consumers, but also caused Equifax to face huge compensation and reputation damage.

Data compliance issues

Ensure that data processing complies with relevant laws, regulations and industry standards.

Data compliance is a basic requirement for data governance. Enterprises must comply with relevant laws, regulations and industry standards during data processing. The Data Security Law, the Personal Information Protection Law and other laws and regulations have made clear provisions for data collection, storage, use, transmission and sharing, aiming to protect personal information security and data sovereignty and promote the legal and orderly use of data.

For example, the EU's General Data Protection Regulation (GDPR) strictly regulates companies' handling of EU citizens' personal data, requiring companies to obtain explicit consent when collecting personal data, encrypt data for protection, and report data leaks in a timely manner. If companies violate GDPR regulations, they will face heavy fines.

Therefore, enterprises should establish a sound data compliance management system, strengthen the study and learning of laws and regulations, and ensure that data processing activities comply with legal requirements. At the same time, they should conduct compliance audits regularly to promptly identify and correct non-compliant behaviors.

Data circulation and sharing issues

Break down data silos and promote the effective use of data within and outside the organization under the premise of compliance.

Data circulation and sharing will provide more value and are the premise of data mining. With the progress of digitalization, enterprises have batches of ERP, CRM, MES, APS and other systems. It is necessary to avoid the separation between systems and ensure that the systems can be connected and data can be shared from multiple dimensions such as technology, suppliers, data, and business. The second is to make effective use of data, analyze trends, rules, and anomalies through historical data, analyze user behavior through logs, build user portraits, provide support for enterprise decision-making, and provide basic data for personalized user operations.

For example, the Dunhuang Academy has digitized large sites and caves, forming a large number of digital archives and digital results, and further processed and processed more than 6,500 high-definition data materials to the platform. Innovate the sharing and co-creation model of cultural relics data resources, divide the materials into public welfare and commercial use, encourage secondary creation and automatically share the accounts through the platform's accounting system. Since its launch in December 2022, the number of visits has exceeded 4.2 million, the number of orders has exceeded 16,000, and the number of material downloads has exceeded 22,000 times.

Data governance [what to govern]: Solve data problems by targeting the problems and risks of the data itself.

3. What is Data Governance?

The core content of data governance includes: implementing responsibilities and roles; clarifying processes and tools; establishing standards and specifications; and continuous improvement and perfection.

Implement responsibilities and roles: Clarify the allocation of responsibilities and role definitions for data governance to ensure that each participant is clear about their roles and responsibilities. You can first set up a dedicated data management department to coordinate data management work and ensure the professionalism and systematicness of data management. Then clarify the responsibilities of each department in data management, such as the business department is responsible for providing and using data, the IT department is responsible for technical support and storage management of data, and the data management department is responsible for formulating data standards and supervising data quality.

Clarify processes and tools: Establish standardized data processing processes and use appropriate tools to ensure the effective implementation of data governance activities. Standardizing data management processes requires including collection, storage, use, sharing, and destruction processes. Ensure that the collected data is complete, accurate, and timely by clarifying the channels, methods, and frequency of data collection; ensure the security and recoverability of data by determining the data storage method, location, and backup strategy; ensure that the use of data complies with regulations and security requirements by establishing processes for data application, approval, and use; standardize data sharing within and outside the organization by formulating the conditions, scope, and methods of data sharing; ensure that data that is no longer needed is safely destroyed by specifying the timing, method, and person responsible for data destruction.

Establish standards and specifications: formulate unified data standards and specifications to ensure data consistency and comparability. Ensure data consistency and compatibility between different systems and departments by unifying data formats, coding rules, data dictionaries, etc.; formulate clear and unified data naming rules so that the names of data can accurately reflect their meaning and purpose, and avoid difficulties in understanding and use caused by confusing data naming; establish data quality assessment indicators and methods based on accuracy, completeness, consistency, timeliness and other dimensions, and regularly assess data quality.

Continuous improvement and perfection: Continuous follow-up, continuous iteration, and continuous improvement to ensure the real-time and effectiveness of data. Through data quality monitoring tools and technologies, real-time monitoring of data quality, timely discovery and resolution of data quality issues; regular evaluation of the operating effects of the data management system and processes, collection of relevant data and feedback, discovery of existing problems and deficiencies; based on the evaluation results, development of improvement measures and plans, continuous optimization of the data management system and processes, and improvement of the level and efficiency of data management.

Data Governance [What to Manage]: By establishing standardized management systems and processes, streamline the use and management of data and standardize data management processes.

To sum up

Data governance is a set of mechanisms, processes and policies that aims to ensure that data is properly managed throughout its life cycle to provide strong support for the decision-making of an enterprise or organization.

• Cure: Solve the problems of data itself (quality, security, compliance, silos, waste), which is essentially to cure the disease;
• Reason: Establishing the order of data management (rights and responsibilities, standards, processes, tools, and assessments) is essentially [legislation].

The ultimate goal: to transform data from a cost burden into a trusted strategic asset to support business decision-making and innovation.