“Data Governance” in the 3 main areas of 1) data policy, 2) data governance team and 3) process will ensure the highest efficiency in data storage and use. The organizations which use high quality data are likely to enjoy increased sales and unlock their potential for future business expansion, it points out.
Business competition was driven mainly by data which were used in the design of new products to follow trends and generate more revenue and were analyzed to cut unnecessary operating costs. However, many organizations cannot achieve their goals because of their poor data. Their data may be incomplete, duplicate or irrelevant to the products they wanted to study. Consequently, business units are unable to really use the data and have to bear the unjustified costs of storing and maintaining the unnecessary data which may affect confidence in their business in the future.
The root cause of the problem is substandard data management. So, data governance must take priority at organizations to establish a standard of data management and supervision because it increases potential for the efficient use of data and supports the formulation and implementation of policies concerning intra-organizational data, personnel management and processes to reduce risks. If organizations effectively manage data and fully use them, they will have competitive edge
Data governance is aimed at clearly setting the rights, duties and responsibilities of stakeholders in the data of organizations. It varies to suit the contexts of individual businesses because different organizations have different sources of data and the types of their data and units responsible for their data are also different. Bluebik has offered consulting services to many large-scaled businesses. They include the organizations that already have standard systems to handle data but still need advice to improve their systems and the organizations that never have a standard data system. Data governance covers three areas – data policy, data governance team and process.
1. Data policy
It is the first step which concerns basic rules and metrics that are essential for data storage. Data policies must comply with relevant laws and regulations. They fall into the following categories:
- Data standardization – This is to set a standard for data storage which includes the naming, formats and lengths of data to guarantee the highest efficiency in data use.
- Data quality – Organizations may need data of different qualities for different situations. The qualities of data can be categorized by their precision, completeness and times needed for their acquisition.
- Security policy – This concerns authority to access different levels of data, the disclosure of data to external organizations, privacy protection and data management processes.
- Compliance with laws and regulations including the Personal Data Protection Act (PDPA). There must be clear guidelines for data creation, correction and deletion.
2. Data governance team
After designing data management policies, organizations should form specific teams to handle data. The following teams will have their own duties.
- Data governance council – It is tasked mainly with determining policies on data management and solving relevant problems. In general, the council includes the chief executive officer, the chief information officer and the information department head of an organization.
- Data steward team – It may consist of the information service heads of business units, the information technology department and the information department. The team is duty-bound to advise on the definitions and standards of data and set the quality standards of data.
- Data stakeholder – This concerns stakeholders in data, namely data owners, data management teams, data users and data creators. Apart from creating and using data, a stakeholders’ team is also responsible directly for maintaining data and managing data systems.\
This integrates all elements to make sure that system development and data management comply with policies and standards. The process can begin with the “data architecture” which covers the whole data structure and then proceed with data modeling, meta data, data security and the installation of data warehouse and data lake facilities to store data at one place where they can be acquired for analyses.
Data governance also helps unlock business potential in various areas. For example, it raises income opportunities for businesses. With advanced analytics, standard data increase precision in business decisions and work efficiency. Thanks to data analyses, project implementation can be accelerated. Organizations can be quickly aware of problems, solve them right away and also prevent risks.
In the era when competitiveness depends on data, good standards of data management certainly create competitive edge for businesses. For example, a world-renowned fast food restaurant chain used properly stored data for its customer services and efforts to woo customers with the promotional campaigns and privileges that better impress target groups of customers. As a result, its sales soared by 35%. The Economic Intelligence Center of Siam Commercial Bank reported that in 2017, 56% of leading Thai companies in many industries used big data to develop sale and marketing processes and improve products and services. Their use of data is likely to grow by 20-25% annually and within 2022, the marketing value of big data can reach 13 billion baht. Moreover, setting data management standards is crucial to organizations’ transformation to increase potential and create new growth opportunities in the future
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