Data is the most critical resource that all other resources will be leveraging. We have to manage all data effectively, accurately, and securely so that these additional resources can properly leverage that data with ensured integrity, availability, and confidentiality. Data management in essence lays the foundation for data analytics. Without good data management, there will be no data analytics. Data management can be broken down into 11 pillars:
Data governance: The planning of all aspects of data management. This includes availability, usability, consistency, integrity, and security of all data within the organization.
Data architecture: The overall structure of an enterprise’s data and how it fits into the enterprise architecture.
Data modeling and design: The data analytics and the corresponding analytics systems. This includes the designing, building, testing, and ongoing maintenance of these analytics systems.
Data storage and operations: The physical hardware used to store and manage the data within the enterprise.
Data security: Encompasses all security requirements, controls, and components to ensure the data is protected and accessed only by authorized users.
Data integration and interoperability: The transformation of data into a structured form to be leveraged by other systems and resources.
Documents and content: All forms of unstructured data and the work necessary to make it accessible to the structured databases.
Reference and master data: The process of managing data in a way that allows it to be redundant, and if there are any errors or mistakes that can be normalized by standard values.
Data warehousing and business intelligence: Involves the management and application of data for analytics and business decision making.
Metadata: Involves all elements of creating, collecting, organizing, and managing metadata (i.e., data that references other data).
Data quality: Involves the practices of data monitoring to ensure the integrity of the data being delivered is maintained.
For a true data management model, all of these pillars need to be included. Without one of these pillars, there is an area of data management that is not being addressed. For example, if there isn’t a solution for metadata management, the business loses the ability to easily categorize data. Without data quality being ensured, all data is at risk and the analytics of that data becomes useless.