The relevance of the topic of data governance is growing from year to year. Any organization understands the need to manage processes to collect, process, store, and use data. As a result, companies are beginning to build business processes based on data governance. Here is more about it.
Data governance for effective digital transformation
Data governance is an important concept that organizations in almost any industry can use to manage their data. Over the past few years, there has been an understanding, especially in large companies, that studying data and their relationships and understanding their value takes a lot of time and resources, prompting us to engage in this area purposefully. The volume of only structured data is increasing by 40% annually. If we consider all data, including unstructured data, then their annual growth is estimated at 80%. World experience shows that companies that effectively manage their data open up new opportunities for business development.
Data governance is an organizational initiative to optimize, protect, and use information as a corporate asset. The main goals for the organization of data governance are:
- increasing the efficiency of the organization, accelerating business functions and projects by increasing the value of the data used;
- increasing the flexibility of the company’s current activities;
- obtaining new business opportunities, developing new areas of activity;
- increasing transparency when working with data;
- reduction of labor costs for the coordination and implementation of improvements when making changes;
- simplification and efficiency increase in the formation and analysis of collected reports on accountable organizations;
- compliance with the requirements of regulatory authorities on time.
This strategy works most effectively as a supportive function across all company business lines, being the core discipline for creating repeatable and scalable data policies, processes, and standards for efficient use of data.
Systematic data quality governance
Data quality governance relies on organizational structure, processes, and data quality governance tools. The organizational structure ensures the distribution of roles and those responsible for the operations of working with data. Organizations in virtually any industry can use data governance to manage the data of their employees, customers, customers, suppliers, and patients. Depending on the size of the company, different specialists can perform data management.
Data quality governance processes are divided into three groups:
- processes for performing operations on data,
- continuous data quality control processes
- data quality improvement processes.
The processes of performing operations on data include managing the data structure within an organization, taking into account the use of data in distributed systems, developing and constructing a data schema, and performing operations on data (creating, searching, deleting, updating). Continuous quality control processes are aimed at identifying data errors and include quality planning, the definition of quality metrics and verification rules, and regulation of data evaluation processes according to specified criteria. Data quality improvement processes should ensure that data errors are corrected, and their causes are eliminated. Finally, the third element is data quality management tools. There is a wide arsenal of instrumental systems for working with data on the market. These are Data Quality class products, master data management systems, specialized solutions for working with client analytics, numerous integration platforms with a rich arsenal of ETL tools, and industry solutions.
Nowadays, most projects dealing with data quality control issues are related to building corporate data warehouses or implementing analytical applications, including risk management, customer relationship, and reporting applications. The most illustrative example in this sense is the example of the financial industry, in which the requirements for the quality of client analytics and the requirements for the quality of reporting provided to the regulator are extremely high.