Data Management is defined as “the development of architectures, policies, practices and procedures to manage the data lifecycle”.
But when people say “data management”, what do they really mean. It can be one of the below possibilities:
- Cloud Data Management: Integrating data from an organization’s ecosystem of cloud applications. In this case all data storage, intake, and processing takes place in a cloud-based storage medium.
- ETL and data integration – loading data from data sources into a data warehouse, transforming, summarizing and aggregating them into a format suitable for high in-depth analysis
- Master data management – a method for managing critical organizational data: customers, accounts and parties named in business transactions, in a standardized way that prevents redundancy across the organization
- Reference data management – defines permissible values that can be used by other data fields, such as postal codes, lists of countries, regions and cities, or product serial numbers. Reference data can be home-grown or externally provided
- Data analytics and visualization – processing selected data from ETL and data integration – loading data from data sources into a data warehouse, transforming, summarizing and aggregating them into a format suitable for high in-depth analysis.
With today’s massive quantities of data, high-quality tools are essential to achieving data management best practices. Therefore, Organizations use data management tools from all five categories above, to manage and automate the data management process.
In next blog, we will talk about data management tools.