Businesses across all types of industries rely on data as a business tool that needs to be properly managed. There are several forms of data that need to be managed including customer data, employee records, payroll data, network maps, and other internal and external data. It takes a bit of effort to turn data into something usable, but the right data management tools make it easier. Proper data management reduces the risk of duplicate or incomplete data, inaccuracies in information, wasted storage space, and the time it takes to manage data.
Data management is best defined as a set of architectures, policies, practices, and procedures that when executed manage the lifecycle of data. The practice keeps accurate, consistent data securely organized in a centralized, accessible place.
What is data governance?
An essential part of data management is a system that defines user access and control over data assets and how they can be used called data governance. It defines the people, processes, and technologies that can access and act on designated information and under what circumstances and methods. Digital businesses need to have planning, oversight, and control over data management.
What’s involved in a data governance framework?
Data governance supports a digital business’s overall data management strategy. Its framework provides a holistic approach to collecting, managing, securing, and storing data. There are several key components within a data governance framework:
- Data architecture is the structure of a business’s data and how it fits within the broader business architecture.
- Data modeling and design are responsible for data analytics and the building, testing, and maintenance of analytics systems.
- Data storage operations are responsible for the physical hardware that stores and manages data.
- Data security includes all aspects of protecting data and ensuring authorized-use access.
- Data integration and interoperability include what’s needed to transform data into a structured form and the means for maintaining it.
- Documents and content refer to all unstructured data and the preparation needed to integrate with structured databases.
- Reference and master data eliminate redundancy and inaccuracies in data by standardizing data values.
- Metadata includes the elements of creating, collecting, organizing, and managing data about the data itself.
- Data quality is the practice of monitoring data and data sources to ensure all data integrity is maintained when delivered and that poor quality data is filtered out.
All of these components are essential in a data management model. If one component is missing it can complicate and damage some aspects of data management.
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What is reference data management?
Reference data management (RDM) is an essential component of master data management. RDM manages classifications and hierarchies across systems including performing analytics on reference data, tracking changes to and distributing of reference data. This requires set policies, frameworks, and standards that govern internal and external reference data.
The best reference data management tools can manage complex mappings, provide connectivity such as service-oriented architecture (SOA) service layer, the means for sharing reference data across business functions, data science, and governance applications.
There are several benefits of successful reference data management. When businesses have centralized control of Etl data, they can ensure it’s consistent and compliant. RDM allows teams to access, distribute, and update reference data across business functions to meet business needs, including scaling business operations and analytics processes. RDM allows businesses to react to new data requirements or market changes quickly and efficiently.
Managing reference data and connecting it through correspondence tables results in semantic consistency and reliable data quality. Building a data management system is a time-consuming process that lays the foundation for the future use of big data. Businesses build data management systems to put their accurate, organized, accessible data to work.
