A Comprehensive Definition of Data Governance
The agreed-upon data governance definition is constantly evolving as organizations recognize the need for a coordinated and consistent approach to managing data as an important business asset. However, there are some key elements that are typically included in a data governance framework.
At its most basic level, data governance is the process of ensuring that the right people have access to the right data, that the data is accurate and reliable, and that the data is used in a way that supports the organization’s business goals.
More specifically, data governance includes the following key activities:
Data Discovery and Identification
Data discovery and identification is a critical first step in good data governance. It is necessary to know what data you have, where it is stored, who owns it and who is responsible for managing it. Without this information, you cannot make informed decisions about how to manage your data.
Data discovery and identification begins with a review of all the data within the organization. This includes data stored in databases, data warehouses, data marts, operational systems, files and email. It also includes data stored in the cloud and data shared with third-party partners.
The goal is to identify all of the data within the organization and classify it into categories such as master data, reference data, transactional data, analytical data and metadata. You also need to identify the source of the data and the owner of the data.
Once the data is classified, you can begin to develop a data governance framework. The framework will identify the roles and responsibilities for managing the data. It will also create procedures for managing the data, including how to access, use, share and protect the data.
Data Quality Management
Data quality management is essential for any organization that relies on data to make informed decisions. Data quality refers to the accuracy, completeness, and timeliness of data. Poor data quality can lead to inaccurate decisions, which can impact an organization’s bottom line.
There are many factors that can impact data quality, including data entry errors, missing data, and incorrect data. In order to ensure that data is of the highest quality, organizations should establish standards for data quality and ensure that data is cleansed and corrected as needed.
One of the most important steps in data quality management is data profiling. Data profiling is the process of identifying the characteristics of data, including the type of data, the range of values, and the distribution of values. This information can be used to establish standards for data quality and to identify any areas that may need improvement.
Data Cleansing and Correction
Once standards have been established, organizations can use data cleansing and data correction techniques to improve the quality of their data. Data cleansing is the process of identifying and correcting inaccurate or incomplete data. Data correction is the process of correcting errors in data values.
Once data has been cleansed and corrected, it is important to track the changes that have been made. This way, organizations can ensure that data is consistently of the highest quality.
One of the most important aspects of data security is keeping your data safe from unauthorized access. You need to have procedures in place to protect your data from hackers, cybercriminals, and other malicious actors. These procedures may include installing firewalls, using encryption, and regularly updating your security software.
Data governance also includes the development of processes and tools for monitoring and reporting on data quality, security, and usage. By tracking data usage and performance, data governance can help to identify potential issues and problems and ensure that the data is being used in a way that supports the organization’s goals.
Ultimately, data governance is about ensuring that data is managed effectively and efficiently, so that it can be used to improve business performance and create value for the organization.