The origin of data management can be traced back to the early days of computing and has evolved significantly over the decades to address the growing complexity and volume of data.

Early Developments

• 1950s and 1960s: The concept of data management began with the use of punch cards for data input and storage. During this period, the Association of Data Processing Service Organizations (ADAPSO) played a crucial role in promoting data management practices, focusing on professional training and quality assurance​ (DATAVERSITY)​.

Introduction of Database Management Systems (DBMS)

• 1960s and 1970s: The introduction of database management systems revolutionized data management. Edgar F. Codd, a computer scientist at IBM, proposed the relational database model in 1970, which organized data into tables (rows and columns) and allowed for efficient data retrieval and manipulation using Structured Query Language (SQL)​ (365 Data Science)​.

Evolution of Data Management Technologies

• 1980s: The relational database model gained widespread adoption, and the first SQL standard was established in 1985. This era also saw the development of online transaction processing systems, which allowed industries like banking and airlines to manage large volumes of transactions efficiently​ (DATAVERSITY)​​ (DATAVERSITY)​.
• 1990s: The advent of the internet led to a surge in data generation and the need for more robust data management solutions. MySQL, an open-source relational database management system, was launched in 1995 and quickly became popular due to its scalability and ease of use​ (DATAVERSITY)​.

Big Data and NoSQL

• 2000s: With the rise of web applications and social media, the volume of unstructured data grew exponentially. This led to the development of NoSQL databases, which are designed to handle large-scale, unstructured data and offer higher flexibility and scalability compared to traditional relational databases​ (DATAVERSITY)​​ (DATAVERSITY)​.

Modern Data Management

• 2010s to Present: The concept of data lakes and data fabrics emerged to address the limitations of traditional data warehouses. Data lakes store raw data in its native format, while data fabrics integrate various data sources and provide a unified view of data across an organization. These technologies support advanced analytics, machine learning, and artificial intelligence workloads​ (IBM – United States)​.

Data management continues to evolve with advancements in technology, ensuring that organizations can effectively manage, analyze, and derive insights from their data to make informed business decisions​ (Tableau)​.