Data Governance refers to the process related to the formal management of data within an organization. Data governance, data security, etc. are sub-topics of the data governance that companies make to ensure that their data is used safely and appropriately.
The data governance is the basis for classifying and defining data, assigning ownership, and integrating day-to-day business processes. Assigning decision-making authority and responsibilities through an effective data governance can encourage good data use. However, if the data governance is not effective, problems arise in areas related to data quality management and security.
Maintaining the reliability and quality of data is paramount in order to effectively use artificial intelligence and machine learning. An effective data governance is required to ensure good data use and to maintain data reliability and quality.
Accurate data governance enable monitoring of the data sources involved in decision making. This allows businesses to see data at a glance and gain insight in a consistent form. If the data governance is not properly implemented, there will be problems in data management and security, and it will not be easy to obtain meaningful insights from the data. The data governance focuses on three key elements.
Data accessibility: A function to obtain correct data when necessary.
Data reliability: How confident the organization is about the quality, accuracy and security of data.
Data activation: A function to take action on the collected data.
Poor data governance can lead to unreliable conclusions in machine learning models. The data management system is critical to successful AI transformation. It's important to understand where your data comes from, how it's processed, the goals that AI platforms and machine learning models set to achieve.