Data management is the practice of managing data as a valuable resource to unlock its potential for an organization. Managing data effectively requires having a data strategy and reliable methods to access, integrate, cleanse, govern, store and prepare data for analytics. In our digital world, data pours into organizations from many sources – operational and transactional systems, scanners, sensors, smart devices, social media, video and text. But the value of data is not based on its source, quality or format. Its value depends on what you do with it.
Some say the need for data management began in the 1890s with mechanical punch cards that recorded information (data) on a thick card. But the concept of data management wasn’t widely discussed until the 1960s, when the Association of Data Processing Service Organizations (ADPSO) began providing data management advice for professionals.
Data management systems as we know them today weren’t common until the 1970s. These data management systems were strictly operational. They provided records (reports) of business operations at a given point in time, pulled from a relational database that stored information in rows and columns (typically a data warehouse).
- Batch processing and extract, transform, load (ETL).
- Structured query language (SQL) and relational database management systems (RDBMSs).
- Not-only SQL (NoSQL) and nonrelational databases.
- Enterprise data warehouses, data lakes and data fabrics.
- Data federation and virtualization.
- Data catalogs, metadata management and data lineage.
- Cloud computing and event stream processing (data streaming).
