The Beginners Guide to Operationalizing Metadata for DataOps
Table of Contents
- Introduction to Metadata and Its Role in Data Management and Data Lineage
- Understanding the Impact of Metadata Management on Data Trust and Operationalizing Pipelines
- How to Operationalize Metadata in DataOps
- Best Practices for Implementing Metadata in Data Pipelines
- Leveraging Metadata for Data Lineage and Discovery with Manta
The world of data-driven decision-making has no room for errors. Trustworthy, accurate data is therefore of the utmost importance. That leaves data professionals, including DataOps teams, data scientists, data engineers, and business analysts, to constantly seek ways to optimize data processes that ensure data trust. One powerful solution to enable these objectives is by leveraging metadata. In other words, accessing the contextual information that provides valuable insights into data assets.
In this post, we’ll dive into the significance of metadata in the context of improving data trust and operationalizing data pipelines for DataOps teams. Furthermore, we will dive into the role of data lineage in metadata management to create more trustworthy data pipelines.
Introduction to Metadata and Its Role in Data Management and Data Lineage
Before we dive into the depths of metadata, let's define it.
Metadata is essentially data about data – it provides descriptive information that helps understand the characteristics, structure, and context of the underlying data. Think of it as a set of labels that give meaning and relevance to data assets. Without metadata, data becomes a sea of numbers and letters, lacking the crucial context necessary for interpreting and utilizing it effectively. For example, metadata shows who modified data, when, and within which system the modification occurred.
Gartner defines metadata management as “a set of capabilities that enables continuous access and processing of metadata that support ongoing analysis over a different spectrum of maturity, use cases, and vendor solutions.”
Active metadata takes metadata a step further. But what makes metadata “active”? Gartner’s Market Guide for Active Metadata Management explains that active metadata is:
- Continuously collected and processed to distill information
- Used to derive intelligence and insights in the form of recommendations, warnings, and notifications
- Delivered to people when and where they need it (rather than those people needing to seek out metadata insights themselves)
3 Types of Metadata
You can break metadata down into three categories:
- Technical Metadata provides information on the characteristics of data, including an inventory of objects as tables or files, data structure and location, etc.
- Operational Metadata helps you understand how the data is being used and the overall data lifecycle, as well as who can access it, when and where it was create, and when it should be deleted for compliance.
- Business Metadata shows the business use of the data object, including reason for collection and storage, agreements, policies, regulations, governance, and consent as defined in a business glossary.
Definition and Purpose of Metadata Management
Metadata management ensures that all necessary metadata is captured, stored, and made accessible to relevant stakeholders. This process is vital for establishing consistency in data usage, enabling data consumers to understand the context and limitations of the data they interact with.
In data management, metadata plays a pivotal role. Metadata provides critical insights to make sense of vast amounts of data. On the flip side, data lineage shows the movement of that data and serves as a bridge between data consumers and data creators, enabling seamless data discovery and fostering trust in data quality. At the same time, metadata is an important element of data lineage.
Think of it like this: data lineage shows where the data flows, while metadata shows who made recent changes, when, and how. By facilitating data understanding, metadata becomes a catalyst for operationalizing data pipelines and improving overall data reliability.
It’s important to note that data lineage is not the only tool for active metadata management, but it is the most important one. Data lineage allows your metadata characteristics to add context to your data flows in a way that other tools simply cannot. If you imagine a data catalog as a static map, data lineage is like Waze, offering not just a map but an active update on traffic jams, construction sites, and all possible routes. Seeing data quality issues and dependencies on a lineage path is the most valuable possible context.
How Metadata Improves Data Quality and Integrity
Metadata provides the context around the data. Metadata can provide information about the quality level of stored data in a given database through data tagging. Because metadata is created objectively, this creates a better understanding of the data integrity overall. Metadata tells you where something came from and when and under what conditions, which is important when using data to make decisions for your business. When you unlock insights into the metadata you will be able to understand whether it is trustworthy and find where the data quality can be improved.
Understanding the Impact of Metadata Management on Data Trust and Operationalizing Pipelines
Metadata management involves the process of organizing, storing, and maintaining metadata to achieve specific data governance and compliance objectives. This is created with the synergies of metadata and data lineage, and is critical for successful data-driven strategies.
In other words, metadata management is the way that you can take the abstract data and operationalize it, creating a cycle of trustworthy data that can be used for better decision-making and actionable processes.
How to Operationalize Metadata in DataOps
DataOps is a collaborative data management practice. DataOps teams strive to streamline and automate data workflows to deliver accurate, trustworthy data across the organization. In this context, metadata plays a critical role in enabling successful DataOps implementations and optimizing data processes throughout the data lifecycle. DataOps teams can begin operationalizing metadata through the following methods:
1. Establish a Source of Truth through Data Discovery and Lineage:
Metadata provides valuable information about the origin, transformation, and usage of data assets. In dataops, data discovery and lineage are essential for understanding data dependencies, tracking data flows, and ensuring data reliability. By documenting and utilizing metadata, DataOps teams can trace data from its source to its consumption points, facilitating data tracking and auditing. This knowledge is crucial for maintaining data integrity, troubleshooting issues, and enhancing data trust.
2. Automate Data Pipelines:
DataOps relies on automated data pipelines to deliver data efficiently and consistently. Metadata serves as a blueprint for these pipelines, guiding the automated flow of data through various stages. By incorporating metadata into pipeline design, data engineers can ensure that data is properly transformed, validated, and routed to its intended destinations. Metadata-driven automation streamlines data workflows and reduces the risk of manual errors, improving data pipeline efficiency and reliability.
3. Understand the Relationship Between Data Quality and Governance:
Maintaining data quality is a fundamental objective in DataOps. Metadata helps assess data quality by providing insights into data characteristics, such as completeness, accuracy, and timeliness. By monitoring metadata attributes, DataOps teams can identify data issues and anomalies, enabling proactive data quality improvement. Additionally, metadata supports data governance initiatives, ensuring compliance with data policies, regulations, and data usage guidelines.
4. Use Metadata to Promote Collaboration and Communication:
DataOps promotes collaboration between different teams involved in data management, including data engineers, data scientists, and business analysts. Metadata acts as a common language that facilitates communication between these teams. Data professionals can use metadata to understand data assets, share insights, and align their efforts in optimizing data processes. This shared understanding improves cross-functional collaboration and streamlines decision-making.
5. Use Metadata in Data Security and Privacy Strategies:
Metadata plays a critical role in DataOps' data security and privacy efforts. By documenting data classifications, access controls, and sensitivity levels, metadata supports the implementation of security measures to protect sensitive data. Furthermore, metadata aids in complying with data privacy regulations by identifying and tagging personally identifiable information (PII) and sensitive data elements.
TL;DR: Metadata is a foundational element in DataOps, driving data discovery, lineage tracking, and data quality improvement. As organizations increasingly embrace agile and automated data practices, metadata becomes a crucial asset for successful DataOps implementations.
Best Practices for Implementing Metadata in Data Pipelines
To fully realize the benefits of metadata in data pipelines, businesses should adopt best practices for implementation.
Integrate Metadata into Data Pipelines
Integrating metadata into data pipelines ensures that critical information about data assets is captured and utilized throughout the data lifecycle. Data consumers can make informed decisions based on the metadata that accompanies the data.
Automate Metadata Extraction and Management
Automating metadata extraction and management reduces manual efforts and enhances data processing speed. This empowers data professionals to focus on data analysis and strategic decision-making instead of spending time on repetitive tasks.
Align Metadata Practices with Business Objectives
Understanding the specific needs and goals of the organization is essential for aligning metadata practices with business objectives. This alignment ensures that metadata efforts support and contribute to the overall business strategy.
Leveraging Metadata for Data Lineage and Discovery with Manta
Metadata management and utilization are vital components of modern data-driven DataOps strategies. Metadata provides the context and relevance that data needs to be understood and trusted. Through effective metadata management, businesses can enhance data trust, operationalize data pipelines, and empower data-driven decision-making.
Manta’s automated data lineage can help you unlock and harness the insights provided by metadata management, creating trust in your data so that you can operationalize it well. Believe us, this matters. According to Gartner: “Through 2024, organizations that adopt aggressive metadata analysis across their complete data management environment will decrease time to delivery of new data assets to users by as much as 70%.”
But, traditional metadata management isn’t going to cut it anymore. Manta can help.
In fact, we’ve been recognized by Gartner in the Market Guide for Metadata Management. Get your copy here!
P.S. This post was written by a human.