Metadata management is a critical capability providing business context to improve data integrity and is a common goal of data governance programs.
Delivering multifaceted Metadata for the enterprise
Table of Contents
- What is metadata?
- Three types of metadata
- Supported with Data Governance
- Unified Data Lineage
- Understand your Enterprise Systems
- Without context your data is useless
- Six Considerations for Successful Metadata Management
- What is metadata management?
- Why is metadata management important?
- How do I build a successful metadata management strategy within a data governance framework
- What are some common challenges in metadata management?
- What are some best practices for metadata management?
- What are some tools for metadata management?
- How do you choose Critical Data Elements?
- How does data lineage enhance enterprise metadata?
- How can I build my metadata and data modelling skills?
What is metadata?
Metadata is the foundational data management capability used to transform data into information by providing business and technical context. It provides essential details about your organisation's information assets, such as:
- What it means
- Where it resides
- How it is stored
- What it is used for
- Whether or not it is Personal data
- and much more.
Simply put, metadata provides a comprehensive, shared understanding of where data resides in your organisation and how it is used. Learn how metadata can help you to assess the severity of a data breach by reading our blog post.
Three types of metadata
To build this comprehensive capability organisations must collect and manage the following metadata types:
A view of systems, tables and columns where data is physically stored. This metadata can frequently be harvested automatically into a data dictionary or data catalogue
2. Logical / lineage
A view of how data is linked together, and how data flows through systems and processes. Again automated harvesting is essential to get and maintain an enterprise view.
The business context for data includes its definition, how it is used, its responsibilities and so forth. Automated processes, supported by machine learning, can support your stewards and subject matter experts as they collaborate to capture this context.
Supported with Data Governance
Many organisations focus their data stewardship activities around the business glossary, for example capturing the definitions of business terms or identifying and classifying personal data fields. Metadata provides crucial information that powers accurate analytics for rich business insights. And an effective metadata management strategy relies on a comprehensive data governance framework.
There is no debate that data governance is important, both to support collaboration between various stakeholders and to ensure accountability and maintain standards.
When everyone works together to interpret and document metadata, organisations institute a mutual understanding of data assets, minimising any confusion business users face when working with the metadata catalogue.
Leveraging a platform like Data360 ensures that knowledge workers can quickly and easily find the information that they need to do their jobs. It engages stewards and knowledge workers combining automated workflows and machine learning to curate metadata and build your enterprise view.
Unified Data Lineage
Unified data lineage is a process that enables organizations to track the origin and movement of their data across various systems and applications. MANTA is a powerful data lineage tool that provides end-to-end visibility into data flows and data dependencies. It helps organizations to understand the data flow from the source to the target, enabling them to make informed decisions about their data management strategies.
MANTA automatically discovers, maps, and tracks data lineage across complex data environments, including big data platforms, data warehouses, and cloud-based infrastructures. With MANTA, organizations can ensure data accuracy, compliance, and governance, which is critical for their success. Read our blog post on How to Enhance Enterprise Metadata Management with Data Lineage!
Understand your Enterprise Systems
We also provide a unique business understanding of your enterprise ERP and CRM systems with Safyr.
Safyr automatically harvests technical definitions from your SAP, Oracle, Salesforce or Microsoft enterprise systems and provides business context, and can share this context with your enterprise data catalogue.
Without context your data is useless
The companies that invest the time and energy to build a shared understanding of data are also those that will be the most successful in leveraging big data, data science and advanced analytics techniques including artificial intelligence and machine learning.
They are also able to deal effectively with data-related compliance and regulatory requirements, such as PoPIA, as metadata provides input into gap analysis, or to assess and manage the impact of a data breach.
Whether you are just beginning, or whether you are just looking to plug a gap in your existing capabilities, we can help.
The importance of metadata to IT is (I hope) obvious. Metadata provides business context that makes data useful, and, without it, advanced analytics applications bog down. Yet metadata management remains poorly invested and often poorly implemented. We wrote a blog post discussing how to get six considerations to get traction for metadata management including:
- Start from the business
- Consider your audience
- How will you govern your metadata?
- How will you structure metadata management?
- How will you make metadata accessible?
- Rome wasn't built in a day
What is metadata management?
Metadata management refers to the process of organising, storing, and maintaining data about data (i.e., metadata) to ensure its accuracy, consistency, and relevance. This includes identifying, defining, and tracking metadata attributes, such as data lineage, quality, security, and access permissions.
Why is metadata management important?
Metadata management is essential for data governance, as it enables organisations to understand and manage their data assets effectively. It helps to ensure that data is accurate, consistent, and accessible, which improves decision-making, reduces risk, and supports regulatory compliance.
How do I build a successful metadata management strategy within a data governance framework
When metadata is leveraged to classify, manage, and organize massive amounts of enterprise data, organizations can better understand and effectively deploy resources to support their analytics efforts.
But maximizing that potential is dependent on the data governance framework – that’s because the metadata management strategy needs a proper foundation, transparent processes, and the right people to execute the work. Doing so combines people and processes, fosters open communication, and creates an enterprise-wide data-centric culture.
What are some common challenges in metadata management?
Some of the most common challenges include data silos, inconsistent or incomplete metadata, outdated metadata, lack of metadata standards or governance, and limited resources for metadata management.
What are some best practices for metadata management?
Some best practices for metadata management include establishing clear metadata standards and governance, integrating metadata management into the data lifecycle, automating metadata collection and maintenance where possible, and providing tools and training to support metadata management.
What are some tools for metadata management?
There are many tools available to give data context, including metadata repositories, data dictionaries, data lineage tools, data quality tools and data modelling tools. The broad range of applications for metadata means that approaches to metadata management vary widely, with no one tool set or platform addressing every need – particularly when addressing the complex data landscapes of big, modern enterprises.
Some popular tools that we have worked with include Onetrust, Collibra, Data360, MANTA, Safyr and Trillium
In Approaches for selecting critical data elements, we proposed that rather than looking at a data domain and trying to define CDEs within that context, we should look at specific business problems and how data supports them.
From this perspective, we suggested three approaches:
- Report driven – understanding which data elements underpin Key Performance Indicators
- Policy Driven – understanding which data supports specific business policies or regulatory requirements
- Process driven – which data are inputs or outputs of critical business processes.
By mapping where data comes from, where it goes, and how it changes along the way, your company can enhance its data management capabilities and unlock insights hidden within enterprise metadata.
How can I build my metadata and data modelling skills?
Our CIMP curriculum in metadata and data modelling offers 11 courses covering data modelling and metadata, leading to CIMP accreditation