A Rudimentary Guide to Metadata Management

 According to IDC, the size of the global datasphere is projected to reach 163 ZB by 2025, leading to disparate data sources in legacy systems, new system deployments, and the creation of data lakes and data warehouses. Most organizations do not utilize the entirety of the data at their disposal for strategic and executive decision making. 

Identifying, classifying, and analyzing data historically has relied on manual processes and therefore, in the current age consumes a lot of resources, with respect to time and monitory value. Defining metadata for the data owned by the organization is the first step in unleashing the organizational data’s maximum potential. 

The numerous data types and data sources that are embedded in different systems and technologies over time are seldomly designed to work together. Thus, the applications or models used on multiple data types and data sources can potentially be compromised, rendering inaccurate analysis and conclusions. 

Having consistency across the data is the only way to ensure that the conclusions reached upon by analysis are actionable and accurate regardless of the structure or location of data. In addition, the policies and processes designed to manage information and its metadata in defining and controlling the access to the data are critical for the protection of sensitive data. 

Basically, metadata can be defined as data about data. It gets generated every time data is captured at a source, moved across an organizational structure, integrated with other data from other sources, profiled, cleansed, analyzed, or accessed by users. Metadata is valuable as it captures information about the attributes of data elements that can be used to guide strategic and operational decision-making. Typically, Metadata Management helps users:  

• Discover and Define Data. 
• Accumulate and consolidate metadata from various data management repository into a single source. 
• Structure and deploy physical metadata to specific data models, business terms, and reusable design standards. 
• Analyze the data relating to the business and its metadata. 
• Identify where to integrate the data and track its trajectory and transformation. 
• Govern data by developing standards, policies, and best practices and associate them with data assets. 
• Empower stakeholders to manage and analyze data in one place and in the context of their roles.

Metadata management is the administration of data that describes the data within an organization, emphasizing associations and lineage. It involves establishing policies and ensures proper information management and maintenance. Metadata management answers a lot of important questions about the data including: 

• What data can we utilize? 
• Where did it come from? 
• Where is it now? 
• Has it transformed since it was originally created or captured? 
• Who owns the data and who is authorized to use it? 
• Is it sensitive and what are the key risk indicators associated with the data? 
• Is the data of any critical use to the organization and what quality constraints need to be applied to it?

Continue reading >>>