Data management is the practice of collecting, organizing, and accessing data to support productivity, efficiency, and decision-making. It consists of several processes, such as storing, maintaining, protecting, organizing and processing data recovered by a company from various sources in a safe and secure manner with maximum efficiency. To achieve this, there are different types of data management functions available for different aspects of data management. The most common type of data management system is the relational database management system (RDBMS).
It relies on the Structured Query Language (SQL) programming language to structure and connect data. NoSQL databases are better suited for unstructured data. Data governance is responsible for setting precedents and laws for the state of information in an organization. It helps to implement policies, rules, and regulations for data-related processes.
Data quality and master data management (MDM) are also important components of data management. Data warehouse management is a process in which raw data is analyzed in depth to gain diverse business insights, as well as to monitor the cloud-based infrastructure to accumulate raw data. This has led to a substantial effort, during the 1990s, to integrate the flexibility of object-oriented concepts and methods with the reliability and scalability of relational data management systems. Advanced analytics (often leveraging machine learning) also relies on large amounts of high-quality data to produce relevant, actionable information that can be acted upon with confidence. Data management solutions need scalability and performance to deliver meaningful information in a timely manner. Addressing data management challenges requires a well-thought-out, comprehensive set of best practices.
The result is a standard for SQL3 with system-dependent implementations for multimedia data management. Formerly referred to as a repository, this function is increasingly occupied by software and infrastructure as service models that are precisely tuned for big data management. To learn more about how Talend can help you with your big data management challenges and start delivering critical business intelligence, see the Talend Data Management Toolkit. In addition, none of these techniques or tools are compatible with the standard for relational database management systems. This requires different indexing, search and retrieval techniques than those used for structured administrative data.
The most common include relational database management systems (RDBMS), object-oriented database management systems (OODMBS), in-memory databases, and columnar databases.
