Volume 1: Metadata Management – Part 1: Understanding & Select Tools

Metadata management is an important part of data governance, but data governance encompasses broader measures that help manage all data assets within an organization. Measures such as setting up data policies, establishing data stewardship / ownership, steps toward data quality, or data privacy and security, to name a few.

Metadata management focuses on handling information about data assets — this in turn assists in implementing data governance policies and practices, providing information about the data assets themselves – data about data.

Metadata management serves as both a foundational and operational element that spurs effective data governance practices. 

The Role of Metadata Management

Effective metadata management ensures that data is well-documented, identifiable, understandable, and useful. To enable effective metadata management, focus on:

Metadata Creation, Capture and Storage

Ensure that you identify and capture metadata when creating or retrieving data. For example, aside from gathering technical metadata like data types and lengths, business metadata like terms or categories, and operational metadata like data lineage and usage statistics, be sure to properly store this information in an easily accessible and manageable environment. This central repository should support the types of metadata used throughout the organization.

  • What can go wrong:
    • When data isn’t handled correctly from the start—whether through flawed creation or capture—it can become nonsensical or meaningless. This often leads to errors during processing, which can result in poor decision-making.
    • Additionally, if metadata isn’t securely stored and made easily accessible, individuals may struggle to locate, comprehend, or effectively utilize these data assets.
  • Value:
    • On the other hand, accurately describing data right from the outset ensures that everyone can understand, use, and trust it!
    • This fosters easier access, management, and integration of an organization’s data, streamlining operations and supporting informed decision-making.

Metadata Standardization

Metadata standardization sets clear standards and conventions, such as naming conventions, formats, and precise definitions, to guarantee uniformity throughout an organization. This approach not only supports interoperability but also bolsters mutual comprehension among teams.

  • What can go wrong:
    • The risks of overlooking standardization are significant. Inconsistencies in data can obstruct integration and degrade data quality.
    • For instance, if date formats aren’t standardized, data scientists may interpret timelines incorrectly, potentially leading to faulty analyses and misguided strategic choices.
  • Value:
    • Implementing standardization enhances consistency in data management, thereby improving communication, integration, and the overall quality of data.
    • When departments share a common language and format for data, it becomes easier for everyone to understand and use the information effectively. This increased clarity and ease of use boost the adoption of data tools and contribute to a data-literate organizational culture.

Data Cataloging

Data cataloging involves the systematic organization and indexing of metadata, making it simpler for users to find and access the data they need. Metadata management tools enhance this process through robust search and browsing features.

  • What can go wrong:
    • Challenges arise without effective cataloging. Data assets might remain underused or lead to unnecessary duplication of datasets.
    • Moreover, if the metadata interface isn’t user-friendly, it can deter users from engaging with the system, diminishing its overall utility.
  • Value:
    • The value of well-executed data cataloging is clear. It significantly improves the discoverability and usability of data, enabling users to efficiently find and utilize data assets.
    • By investing in user-friendly interfaces, organizations can boost engagement with their metadata systems, making it easier for users to access, understand, and apply data effectively.

Compliance and Security

Enforcing compliance and security is pivotal in data management. This means implementing rigorous access controls, thorough audit trails, and adherence to data protection regulations are essential. It’s also critical to shield metadata from unauthorized adjustments and breaches, thereby protecting sensitive data details.

  • What can go wrong:
    • Lapses in robust governance and compliance can lead to severe consequences, including legal issues and a diminished trust from customers and stakeholders.
    • Weak security protocols might allow unauthorized access to sensitive information, leading to data breaches and undermining data integrity. 
  • Value:
    • The value of strong compliance and security practices is immense. They ensure that data management practices align with legal and regulatory standards, which protects sensitive information and builds trust.
    • Securing metadata not only maintains data integrity but also adheres to privacy regulations, protecting the organization from various risks and legal challenges.

Metadata Quality Management

Metadata is key in the ongoing process of monitoring, measuring, and improving data quality. This involves tracking key metadata quality metrics, identifying any issues, and taking the necessary steps to correct them.

  • What can go wrong:
    • When metadata quality management is overlooked, data can quickly degrade, becoming inaccurate or obsolete. This often leads to poor decision-making and operational inefficiencies. For instance, inadequate management of customer data can disrupt communication and skew marketing efforts.
    • If customer contact details are not regularly verified for accuracy, critical communications might end up at the wrong addresses, frustrating customers and potentially harming business relationships.
  • Value:
    • The benefits of diligent metadata quality management are clear. It preserves high standards of data quality, which in turn supports reliable analytics, reporting, and business intelligence functions.
    • Accurate and up-to-date data helps businesses avoid costly blunders, such as non-compliance with regulations, which can result in fines and damage to their reputation. 

Data Lineage and Integration

We’ve all been in the situation where we wonder or need to know the source of specific information – in other words, we’re looking for the data lineage to show the flow of data through its lifecycle, from source to destination, including transformations it undergoes. This is crucial for understanding data dependencies, conducting impact analyses, and troubleshooting data issues. However, to have this, we first need to put in the work to ensure the necessary systems are integrated to be shared and used across different systems, platforms, and tools. This often involves integrating metadata management tools with other data management and IT systems. 

  • What can go wrong:
    • Lack of integration can lead to siloed data, making it difficult to share or consolidate information across different systems and platforms. 
    • Without effective collaboration across tools, miscommunication and misunderstandings can arise, slowing down projects and leading to errors. 
    • Without clear data lineage, it’s challenging to understand data origins, transformations, or to assess the impact of changes, leading to potential errors and inefficiencies. 
    • If the systems remain siloed, we fail to continuously improve and adapt metadata management practices in a collaborative way, which can result in outdated or inefficient processes, diminishing data’s value over time. 
  • Value:
    • Facilitates the sharing and use of data across various tools and platforms, enhancing collaboration and operational efficiency. 
    • The sharing of understanding of data, fosters teamwork and more effective data use. 
    • Provides transparency into data origins and transformations, enabling better impact analysis and trust in data. 
    • Ensures metadata management practices remain effective and aligned with evolving business needs and technological advancements. 

Now with the most impactful elements of metadata management understood, in our first deep dive, we’ll jump into the world of metadata management tooling and how specifically they can be leveraged to bring visibility to your data and help accelerate your parallel efforts on people and process. 

What does metadata look like?

Table metadata is the blueprint of a database table, describing what information it contains, and their data types. As most widely termed; metadata is data about data.

For an understanding, let’s take a dataset for shipment tracking for example. Below with general and column-specific metadata.

General metadata:

  • Description: This dataset tracks the status of shipments across all stages of the supply chain, from manufacturer to distribution centers worldwide.
  • Created Date: January 1, 2024
  • Last Updated: May 1, 2024
  • Number of Records: 50,000
  • Source: Supply Chain Management System
  • Access Restrictions: Accessible to logistics and management departments only

Column-specific metadata:

  1. Shipment ID
    • Data Type: String
    • Description: Unique identifier for each shipment.
    • Example: “SHP10002345”
  2. Product Code
    • Data Type: String
    • Description: Code that identifies the product type.
    • Example: “P1234”
  3. Origin
    • Data Type: String
    • Description: Starting location of the shipment.
    • Example: “Factory A, Shanghai, China”
  4. Destination
    • Data Type: String
    • Description: Final delivery location of the shipment.
    • Example: “Warehouse D, Chicago, USA”
  5. Departure Date
    • Data Type: Date
    • Description: Date when the shipment left the origin.
    • Example: “2024-04-15”
  6. Estimated Arrival Date
    • Data Type: Date
    • Description: Projected date for the shipment to reach its destination.
    • Example: “2024-05-01”
  7. Status
    • Data Type: String
    • Description: Current status of the shipment (e.g., In Transit, Delayed, Delivered).
    • Example: “In Transit”
  8. Carrier
    • Data Type: String
    • Description: Name of the company transporting the shipment.
    • Example: “Global Freight Solutions”

What’s a metadata management tool?

A metadata management tool is a centralized repository that enables organizations to locate, understand, and utilize their data effectively. It provides a searchable index of all available datasets and databases, along with metadata that describes their source, content, format, and connection details.

Metadata management tool benefits

  1. Centralized data access – Metadata management tools serve as a central repository of all data assets across the organization, making it easier for employees to find the data they need. 
  2. Facilitated collaboration – A metadata management tool allows users to share insights, annotations, and use cases related to the data, a stepping-stone to collaboration and knowledge sharing. 
  3. Compliance and governance – A metadata management tool helps ensure compliance with data governance policies and regulations by providing visibility into data usage, lineage, and permissions. 
  4. Time efficiency – By reducing the time spent on finding and understanding data, a metadata management tool allows employees to focus more on analysis and decision-making. 
  5. Data Lineage – Provides a visual representation of the full data lifecycle of the data, where it came from, and the transformations which have taken place.  

Challenges 

  1. Data silos and limited system integration – Occurs when data is stored in separate systems or departments, and data / metadata is not shared. 
  2. Poor metadata quality – Affects the usefulness of the metadata management tool. 
  3. Lack of standardization – Without proper standardization rules, the data assets contained in the catalog may hinder data discovery, as the same data can be described differently between datasets, making integration between data sources more complicated. 

Consideration of select metadata management tools 

You want to explore and select a metadata management platform capable of ingesting the complexity of the systems in your organization while enabling data discovery and governance. Additionally, it is important to be able to be cloud independent and keep the complexity and deployment low. 

Decision Drivers 

  1. Cloud Independence: Since a consideration for most companies is cloud-independence, the metadata platform should have the capability to support multi-cloud or hybrid cloud environments. 
  2. Complexity and Deployment Time: The chosen tool should not have a steep learning curve, and the deployment time should be minimal. 
  3. Cost-Effectiveness: Companies need a tool with a price structure that aligns with their financial capability at that time. 
  4. Integration: The metadata platform should have the capacity to integrate seamlessly with other tools and technologies in your technology stack. 
  5. Scalability: The chosen platform should be able to handle the increasing volume of data as our business grows.

There are a number of solutions on the market to choose from, however we’ll provide a quick highlight of the following:  

  1. DataHub 
  2. Atlan 
  3. OpenMetadata 
  4. Databricks Unity Catalog 

Pros and Cons of the Options

DataHub 

DataHub is an open-source metadata platform developed by LinkedIn. It is designed for scalable metadata ingestion, providing better understandability of data processes. 

  1. Cloud Independence 
    1. Good: DataHub is highly portable and supports multi-cloud and hybrid cloud workflows due to its container-based microservices architecture. 
    2. Neutral: DataHub doesn’t restrict which cloud platform you can use, but some clouds may offer deeper integration features. 
  2. Complexity and Deployment Time 
    1. Good: DataHub’s architecture is intuitive and straightforward, leading to less complexity and shorter deployment time. 
    2. Neutral: While DataHub reduces complexity, there can still be some learning curve associated with understanding its architecture and capabilities. 
  3. Cost-Effectiveness 
    1. Good: DataHub is an open-source product, which means that there are no upfront costs associated with its usage. 
    2. Neutral: Although DataHub itself is free, there may be cost implications associated with infrastructure, storage, and labor costs for setup, maintenance, and management. 
  4. Integration
    1. Good: DataHub offers seamless integration capabilities with other tools in our stack. 
    2. Neutral: The integration process, while generally smooth, can vary depending upon the specific technologies in the tech stack. 
  5. Scalability
    1. Good: DataHub’s microservices architecture enables easy scalability as our data volume increases. 
    2. Neutral: The extent to which you can scale depends on the resources allocated to DataHub. 

Atlan 

Atlan is a modern data workspace that helps data teams to collaborate and automate routine tasks. 

  1. Cloud Independence 
    1. Good: Atlan supports different cloud storage systems such as AWS S3, Google Cloud Storage, and Azure Data Lake Storage. 
    2. Neutral: Atlan doesn’t restrict which cloud platform you can use, but some clouds may offer deeper integration features. 
  2. Complexity and Deployment Time 
    1. Good: Atlan’s interface is user-friendly, leading to lower complexity. 
    2. Neutral: Despite having a user-friendly interface, understanding all functionalities of Atlan can take time. 
    3. Bad: The deployment time might be longer compared to other alternatives due to the learning curve associated with mastering all its functionalities. 
  3. Cost-Effectiveness 
    1. Good: Atlan offers a range of pricing models that can be flexible to suit a startup’s budget.
    2. Neutral: The cost can vary depending on the specific features that are utilized within Atlan.
    3. Bad: As a commercial product, Atlan might be more expensive compared to open-source alternatives such as DataHub. 
  4. Integration
    1. Good: Atlan provides easy integration with popular data sources and platforms. 
    2. Neutral: The ease of integration largely depends on your existing tech stack and how compatible it is with Atlan. 
  5. Scalability
    1. Good: Atlan can handle increasing data volumes as our startup grows. 
    2. Neutral: Although Atlan is scalable, the specifics of scaling might depend on your cloud service provider’s capabilities. 

OpenMetadata 

OpenMetadata is an open standard for metadata and an open-source metadata platform that includes APIs, schemas, and a runtime. 

  1. Cloud Independence 
    1. Good: OpenMetadata, being a set of APIs and schemas, is cloud-agnostic and can support any cloud workflow. 
    2. Neutral: OpenMetadata doesn’t restrict which cloud platform you can use, but some clouds may offer deeper integration features. 
  2. Complexity and Deployment Time 
    1. Good: OpenMetadata is designed to be easy to deploy and use, minimizing complexity. 
    2. Neutral: Even though it’s designed to be easy, there might still be a learning curve associated with the understanding and implementation of its APIs and schemas. 
  3. Cost-Effectiveness 
    1. Good: As an open-source product, OpenMetadata has no licensing costs. 
    2. Neutral: As with any open-source solution, potential costs related to infrastructure, implementation, and ongoing management should be considered. 
  4. Integration 
    1. Good: OpenMetadata’s APIs and schemas are designed to integrate smoothly with other tools and platforms. 
    2. Neutral: The ease of integration can depend on the complexity and type of the existing tech stack. 
  5. Scalability
    1. Good: Due to its lightweight nature and schema-based design, OpenMetadata is generally scalable with minimal effort. 
    2. Neutral: While OpenMetadata is easily scalable, the scalability in terms of large data volumes can depend upon resources allocated to it. 

Databricks Unity Catalog 

Unity Catalog is a feature within Databricks that unifies data access and governance. 

  1. Cloud Independence 
    1. Good: Unity Catalog is part of the Databricks ecosystem, which supports multiple cloud environments including AWS and Azure. 
    2. Neutral: Despite supporting multiple cloud platforms, the depth of support can vary between different providers. 
    3. Bad: Databricks Unity Catalog is not as cloud-independent as the other options. Running it outside AWS and Azure can be a challenge. 
  2. Complexity and Deployment Time 
    1. Good: As a part of Databricks, Unity Catalog’s deployment and usage complexity can be relatively low if we are already using Databricks. 
    2. Neutral: If Databricks is not already a part of the tech stack, the learning curve and deployment time could possibly increase. 
    3. Bad: If Databricks is not part of the tech stack, then adopting Unity Catalog will require adopting Databricks, leading to potentially higher complexity and longer deployment time. 
  3. Cost-Effectiveness 
    1. Good: Unity Catalog comes as part of the Databricks suite, and if we’re already using Databricks, this could be a cost-effective solution. 
  4. Integration 
    1. Good: As a part of the Databricks suite, Unity Catalog has excellent integration capabilities within the Databricks ecosystem. 
    2. Bad: If you’re not using Databricks, integrating Unity Catalog into your tech stack might be a challenge. 
  5. Scalability 
    1. Good: Unity Catalog, being part of the Databricks suite, inherits Databricks’ robust scalability. 

Links and additional resources: 

Now let’s take what we’ve learnt and get our hands dirty. For this, checkout Part 2: Deep dive on Metadata Management with DataHub.

The Adventures of Data Builder Dan – Metadata Mayhem

As the world becomes more digital and data literacy becomes more important, we feel there are not enough lessons for kids to learn the basics, or at the very least help explain what Mom or Dad does for a living! So as big kids ourselves, we bring to you the Adventures of Data Builder Dan, to help make the complex, well, simpler.

In Episode 1, Metadata Mayhem disrupts data organization and understanding in the digital realm. Dan explores metadata management understanding in an effort to restore clarity and order. A fun way to explain the content above to a younger audience!

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