
Metadata is simply data about data. It means it is a description and context of the data. It helps to organize, find and understand data.
Here are some of the Metadata examples which we see on a daily basis
Metadata is created and collected because it enables and improves use of that data. Metadata ensures that we will be able find data, use data, and preserve and re-use data in the future.
This lecture talks about the different common questions Metadata Management can answer.
This lecture talks about the common questions for a specific attribute in a real time project.
How does Metadata help with the Business Processes and Procedures?
How does Metadata help with the Regulations and compliance like GDPR and CCPA?
Let's understand the different types of Metadata
Business rules
Stewardship
Business Definitions
Auditing
Terminology
Glossaries
Algorithms
Lineage using business language
Technical metadata consists of metadata that is associated with data transformation rules, data storage structures, semantic layers, and interface layers.
Difference between Technical Metadata and Business Metadata
Operational metadata is made up of the operational reporting and statistics such as access logs, timestamps, transaction counts and volumes, or system performance and response times.
Governance Metadata Contains information about various data domains, data owners, data stakeholders, data stewards, data standards, policies, processes and workflows.
Let's do a quick review of the different types of Metadata
Descriptive, Structural and Administrative Metadata Types
Where does Metadata exists in the Organization?
This lecture explains the overall data flow of the Generic Data Warehouse Architecture and how the different types of Metadata are available at different stages.
This lecture explains what is happening now at the Enterprise Level when it comes to Metadata with real time examples
What are the common questions we get on a daily basis?
Metadata Repository is the answer
How do we access the Metadata Repository Tool?
Self Service Metadata Management
Data Lineage/ Data Traceability
Relationships within Metadata
Enterprise View of Metadata
In this lecture, we will talk about the Hybrid BigData Warehouse Architecture
We will talk about the different ways the Metadata can be ingested from various sources of the system.
Metadata from the Source Layer
Metadata from JSON format
Metadata from Relational tools and technologies
Metadata from HDFS Layer
Metadata from EDW/Data Marts/Hive/Analytics Layer
Metadata from Analytics/Reporting Layer
Metadata from Data Science and Transformation Logic
How does all the Metadata tie up?
Let’s get started with the basics of Metadata Management.
Setting up the Metadata Management initiative at the enterprise level serves a variety of purposes. Data Asset discovery, effective cataloging, identifying resources, defining them by criteria, bringing similar resources together and the most important of them all is to have a Self Service approach to the enterprise assets.
It also is an effective means of organizing enterprise data assets, which is the need of the hour considering the huge variety and volumes of data and new ways of doing business online. Typically, all the metadata is stored in documents or excel sheets which are not usually up to date just for the namesake and these documents do not really have what one needs. A more efficient way is to use metadata to define, build lineage, relationships and create trace-ability so that the enterprise data citizens can easily find the data, the systems in which the data resides and the owners or stewards of the data.
Investing in metadata management/development have a lot of benefits:
Simplify data discovery and history of the data with a record of content-rich data across the enterprise.
Most of the enterprises have to manage increasingly complex systems in various locations and on varied platforms. By managing metadata effectively, organizations can create an inventory of its data and learn about its transformation across the life cycle, along with the variety of meaning and formats, and locations of each data object.
Drive consistency through data reuse, to increase productivity and reduce time needed for project implementation.
A managed metadata environment serves as the most effective way to identify the appropriate data elements / objects needed for any use.
Metadata Management allows companies to retire unused storage, reducing costs, and reducing time that was spent in deciding among “possibly correct” variations of an attribute.
Retain staff knowledge that is lost when business rules, definitions and other forms of metadata are not documented. Often, business metadata remains only in the minds of certain employees. When these individuals leave the company, this knowledge disappears with them. Implementing an enterprise approach to managing metadata preserves this knowledge and reduces the risk of losing valuable contextual knowledge.
Increase confidence in the data delivered to business users. Tracking data lineage provides important context to business users. Profiling data in source systems by business data stewards and IT staff can resolve data errors, which will result in accurate, reliable, high-quality data presented in reports, queries and analytics.
Improve IT performance in development, impact analysis, data integration, change management, etc… All of these enhancements will enable greater cooperation between business and IT, and ultimately lower total costs of any systems initiative. Metadata helps IT understand what data exists, where it is located, and what it means, minimizing information complexity. The ability to assess the impact of potential changes based on improved knowledge of the data can help managers estimate project duration and resource costs more accurately.