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Big Data Engineering

Metadata Management Tools and its Best Practices

Chandan Gaur | 30 June 2023

Metadata Management Tools and its Best Practices

Introduction to Metadata Management

The most crucial process for a successful digital initiative plan is metadata management. It is currently essential for managing the information assets in an organization due to the advent of distributed architectures like Big Data and Cloud that may result in siloed systems and data. There is much literature on this subject on the internet, and the language can complicate readers.

Therefore, this article brings out the easy-to-understand knowledge around topics.

Metadata is essential information that helps us understand and use information assets throughout their life cycles. Taken From Article, Enterprise metadata management (EMM)

What is Metadata?

Correct metadata definition is the first step in managing metadata. Although others prefer to refer to metadata as "data about data," that is simply one aspect of the situation. We also refer to Metadata as "what identifies data." Yet another incomplete definition. In actuality, metadata encompasses much more than just a data description. Metadata plays more and more responsibilities as data complexity rises. Metadata sometimes relates to quarterly revenues from a business perspective. Other times, it might talk about the data warehouse's source-to-target mappings.what-is-metadata

What is Metadata Management?

Data analysis, labeling, and classification are all included in it. With more control over their data, companies (and their business users) can discover it quickly and use it for various operations.

What difficulties do Businesses have without Metadata Management?

Businesses and IT departments need high-quality metadata that keeps their data landscape tidy and structured. A company can only realize the value of its data with properly maintained metadata. The organizations that refuse to acknowledge the significance of it cannot answer below questions:

  1. What reports already exist? Do we need to spend money and make everything from scratch again?
  2. Where did the data originate from, and what does it stand for?
  3. How do systems transport data around?
  4. Who exactly has access to the information?
  5. Which data-related activities are governed by which regulations?

A few effects of poor metadata management include the inability to coordinate sales, marketing, finance, and business intelligence and governance.

Why Metadata Management is Important?

Because of the following, organizations must include it in their data management practices:

  • Improved Consistency: Establishes a uniform definition of Metadata throughout the business to prevent problems with data retrieval due to conflicting terminologies.
  • Better Data Quality: Automation is usually always used by metadata management solutions to spot problems and inconsistencies with data in real-time.
  • Faster Access to Insights: Data teams can complete projects more quickly, and data scientists have more time to study data and derive real business value.
  • Cost Savings: The efficiency improvements and repeatable procedures used in metadata management cut down on redundant spending and extra expenses like storage fees. 

What is a Metadata Management Framework?

A metadata management framework consists of tools for capturing, integrating, managing, and publishing metadata. It includes a repository for storage, an access portal for retrieval, discovery and mapping tools for exploration, collaboration features for teamwork, and analytical capabilities for insights.

Components of a Metadata Management Framework

  • Metadata Strategy: A high-level plan that aligns with the organization's data management strategy and supports business goals.
  • Metadata Architecture: Defines the model, standards, and guidelines for consistent Metadata across the organization.
  • Metadata Governance: Establishes policies, procedures, and controls for accurate, secure, and compliant metadata management.
  • Metadata Tools and Technologies: Enables effective metadata management through tools like data modeling, repositories, profiling, and lineage.
  • Metadata Processes and Workflows: Defines processes for capturing, storing, and maintaining metadata, ensuring timely updates and proper documentation.  
  • Metadata Integration: Integrates metadata from diverse systems to comprehensively view data assets.  
  • Metadata Usage and Analytics: Leverages metadata for data analytics, discovery, and governance support.

Benefits of Using a Metadata Management Framework

The key benefits of a metadata management framework include:

  • Enhanced Search and Accessibility: The framework provides a unified portal with role-based views and advanced search capabilities. This enables efficient data location and access.
  • Comprehensive business semantics management: The framework supports managing business glossaries, synonyms, terms, and relationships. It also facilitates rule definition, custom data types, and taxonomies to understand the business context comprehensively.  
  • Interoperability: The metadata management framework promotes shared understanding through standard vocabularies, facilitating seamless data utilization across applications and workflows.  
  • Usage Tracking: The framework offers visibility into data consumption and access logs, allowing organizations to monitor and analyze data usage for informed decision-making. 

What are the Best Practices of Metadata Management?

In today's data-flooded world, it is a complex task. While many businesses now understand its significance, they still need help knowing where to begin. We've listed a few options for you to think about:best-practices-of-metadata-management

Create a Metadata Administration Team

The initial objective should be to create a metadata administration team if the organization doesn't have one. Hire qualified, experienced professionals with expertise in data management to guide and manage the metadata processes.

Develop a Metadata Strategy

The basis of successful metadata management is to design a strategy that supports business goals. We should also be able to share that strategy with the key stakeholders. It should answer the following queries:

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The data asset is about what?

  1. Descriptions (tables, columns)
  2. Keywords or tags
  3. Themes or categories

Why does the data asset exist?

  1. Data source
  2. Lineage
  3. Impact analysis

The data asset is from where?

  1. Spatial coverage
  2. Language
  3. Business domains

Who is responsible for the data asset?

  1. Creator or owner
  2. Contributors or experts
  3. Point of contact

When was the data asset created and updated?

  1. Creation date
  2. Last updated or modified date
  3. Update frequency

How can the data asset be used?

  1. License
  2. Classification
  3. Use cases
Empower the entire stack with a new way to store and analyze data solutions that are secure, agile, flexible and cost-effective than traditional data management systems. Taken From Article, Enterprise Data Lake Services and Solutions

Establish Metadata Standards for your Company

Standards for metadata are rules, directives, or formats to be followed to organize and store metadata. The metadata standardization will act as a basis for the metadata management process.

Popular metadata standards, like the ISO 158369 standard and the Dublin Core Metadata Element Set, define fundamental characteristics for representing metadata resources.

Ensure that all Metadata is Accurate and Relevant

Organizations should ensure that the available metadata list is correct and relevant.
Create templates for each type of content or document, or use pre-populated metadata from a database, spreadsheet, or another frequently used source.

Create a Strategy to Support all Metadata Types

All types of metadata, including technical, operational, and corporate, must be collected, analyzed, and processed by an organization. Therefore, the strategy should allow for establishing processes to utilize all types of metadata. Otherwise, the organizations could skip crucial data, leading to faulty or irrelevant business insights.

Find the Right Tool

It is challenging to manage large volumes of metadata manually. So assess your requirements and pick the tool that best suits your workflow. Ensure integrability and scaling. Utilize AI/ML-based solutions that support active metadata management and provide vast features, such as predictive analytics.

Maintain Consistency Across the Entire Business

As soon as you've begun, add metadata consistently per your policy and chosen standards. You'll be able to have comprehensive metadata for all of your assets as a result. Please ensure all data users and stakeholders understand the value of metadata and pledge their support.

Establish a Data Catalog (With the Appropriate Components)

Effective metadata management depends on data catalogs. Choose and implement a data catalog with the following features:

  1. Flexible search
  2. The ability to gather metadata from various sources, such as object storage, on-premises systems, etc.
  3. Automation of metadata collection and discovery
  4. Editing and integration capabilities for business glossaries

What are the Best Tools for Metadata Management?

The best tools are below highlighted:

  1. Alation Data Catalog
  2. ASG Enterprise Data Intelligence
  3. IBM InfoSphere Information Server
  4. Alex Data Marketplace
  5. Collibra Platform

Types and Features of Metadata Management Tools

There are several types of metadata management tools available, including:

  • Metadata Repositories: These databases store metadata about an organization's data assets, making it easier to search, discover, and analyze data. Examples include IBM InfoSphere, Collibra, and Informatica Metadata Manager.
  • Data Modelling Tools: These tools create and manage data models, helping organizations understand data structure and relationships for easier management. Examples include ER/Studio, Embarcadero, and PowerDesigner.  
  • Data Lineage Tools: These tools track data origin and flow, providing a complete view of the data's journey and supporting compliance. Examples include Informatica Metadata Manager, Manta, and Collibra.  
  • Data Catalogs: These tools provide searchable inventories of data assets, including quality, lineage, and business context. Examples include Alation, Collibra, and Informatica.
  • Data Quality Tools: These tools assess and improve data quality, identifying and addressing issues. Examples include Informatica Data Quality, Talend Data Quality, and Trillium Software.  
  • Data Integration Tools: These tools integrate data from different sources and systems for better management and analysis. Examples include Informatica PowerCenter, Talend Integration Cloud, and IBM InfoSphere DataStage.

Factors to Consider When Choosing a Metadata Management Tool

The following are the points to consider while choosing any Metadata management tool:  

Data Governance and Metadata Management

Data governance and metadata management enhance data asset management and facilitate data-driven decision-making. Here's the relationship between them:  

  • Metadata is vital for data governance: Metadata offers context and comprehension to ensure data accuracy, consistency, and security. It aids in understanding data meaning, usage, lineage, and quality.  
  • Metadata ensures compliance: Effective metadata management supports regulatory and compliance obligations. It helps demonstrate data accuracy, lineage, usage, security, and privacy.
  • Metadata enhances data management: Metadata simplifies management and maintenance by providing a comprehensive view of data assets. It enables data search, discovery, analysis, and identification of quality issues and supports integration and migration projects.

Metadata Standards and Policies

Metadata schemas are sets of metadata elements organized for specific purposes, such as for a particular domain or type of information resource. These schemas define the names and semantics of each element, specifying their meanings within the context of the Metadata. Optional specifications may include content rules, which dictate how the content should be formulated, representation rules (e.g., capitalization rules), and allowed values for elements, often drawn from controlled vocabularies. Some schemas may also prescribe the syntax in which the elements must be encoded, differentiating them from syntax-independent schemas. Many contemporary schemas use Standard Generalized Markup Language (SGML) or XML to define syntax. Metadata schemas established and maintained by standard organizations, such as ISO, or dedicated initiatives like the Dublin Core Metadata Initiative, are called metadata standards. 

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Although we usually overlook metadata, it is crucial for extracting value from data and using that value to resolve real-world issues. Businesses use metadata management to meet shifting client demands. Companies that take pleasure in being the epitome of customer orientation may orient their products, marketing messages, and customer service plans with what customers desire using it. Adopting a strategy fosters a culture of data-driven innovation, cooperation, and the production of progressively wise business decisions.