Harnessing Active Metadata for Data Management

As data landscapes evolve and expand at an unprecedented rate, businesses are turning to innovative solutions to manage the impact. Traditional data integration and management methods are proving inadequate for handling the high volume, variety, and velocity of data today.

This article explores an emergent concept known as active metadata and its vital role in modern data fabric architectures. This advanced usage of metadata continuously adapts and learns from the environment to empower and automate numerous data management tasks, transforming the landscape of data integration and operationalization. Gartner predicts that by 2024, organizations that adopt active metadata capabilities will be able to decrease the time to deliver of new data assets to users by as much as 70%.

Passive metadata refers to metadata that is collected but not actively leveraged for intercommunication among platforms or tools. In contrast, active metadata refers to metadata that is continually accessed, examined, and utilized to recommend or even automate various data management tasks. For instance, active metadata can be used to automatically optimize data throughput for new sources, while passive metadata may only exist as design blueprints or in a catalog.

With these definitions in mind, we delve into how active metadata is the driving force and force multiplier for efficient data management, enabling paradigms like data fabrics, data meshes, and data observability. We also discuss the benefits of activating metadata and typical use cases for it. Finally, we talk about how to maximize value from your current metadata and transition to an active metadata way of data management.

Summary of key active metadata concepts

Metadata and its types

To delve into the various types of metadata, we must first explore the distinction between data and metadata. At its core, metadata is data about other data. It’s the byproduct of data movement across organizational channels, often exceeding the volume of original data. Metadata encompasses the data about the underlying data, like schema definitions or a count of the number of records; data about the data systems storing the data; and even the data processing pipelines transforming the data. This has been illustrated in the figure below. As data changes, metadata proliferates, constantly being generated and categorized. Businesses are creating extensive metadata databases by uncovering and gathering it from a plethora of sources and channels.

What is metadata? (Source)

Broadly, metadata can be divided into four categories, as outlined by Gartner: technical, operational, business, and social (refer to the figure below). All four are usually regarded as “passive” metadata, the term implying that while this metadata is accumulated, it isn’t actively leveraged for intercommunication among platforms or tools. Passive metadata typically encompasses design blueprints, execution logs, catalogs, glossaries, and definitions; it might extend to flow charts, predefined procedures like scripts, and even performance evaluations.

According to Gartner, there are four types of Metadata, as presented in the table below.

The need for active metadata and its benefits

With an understanding of passive metadata, let’s delve into the dynamic nature of active metadata and its advantages. Picture passive metadata as a traditional GPS navigation system displaying a pre-set route. When real-time traffic updates or location shifts influence the GPS to modify the route, the metadata transforms from passive to active. Similarly, a data pipeline producing passive logs about data volume and schema becomes active when it auto-adjusts to data volume spikes or alerts about schema drifts, or even modifies the schema in response. 

Now let’s discuss the necessity for this transformation and the benefits it can bring to data management scenarios.

Streamlining system interoperability, auto-scaling, and orchestration

Pipeline-run metadata offers insightful data on system health and status, allowing automatic scaling and orchestration adjustments for downstream processes based on job-run metadata. In the figure below, we portray how Nexla activates run metrics or log metadata to auto-scale source containers, transforms, and output containers in parallel batch and streaming pipelines.

Auto-scaling pipelines using active metadata (Source)

Facilitating progressive automation

Active metadata serves as a vital catalyst for automation. Analysis of user connection strings, queries, and views inform performance optimization and resource allocation, triggering system changes to form active metadata. This facilitates automated tasks such as view creation or query caching based on usage patterns. Nexla efficiently automates this end-to-end data engineering life cycle by leveraging metadata actively. Refer to the figure below to understand all the aspects of data engineering automation that can benefit from activating metadata.

Data engineering automation powered by active metadata (Source)

Revamping recommendation systems for data management

Active metadata has the power to build data management recommendation systems. By analyzing usage statistics or even semantic metadata, the system can generate automatic recommendations, which can then be tested and validated in sandbox environments before production deployment. User feedback metadata can also be integrated to generate recommendations like the next best source to connect or a suggested schema for a table.

Enhancing data quality and compliance

Active metadata helps improve data quality and ensure compliance. When incorporated into data profiling, it assesses connectivity, parallelization, and workflow requirements. This allows data integration tools to tailor data flows, data quality tools to detect data drifts, and master data tools to evaluate and enhance workflows. Additionally, active metadata can facilitate compliance monitoring through real-time alerts triggered by changes in sensitive data assets, supporting a more proactive approach to regulatory adherence. This capability is particularly vital in today’s complex data landscapes, where constant vigilance is required to meet various compliance standards. Learn more about real-time data quality management here.

Strengthening data governance and security

Active metadata is an essential element of robust data governance. It assists in establishing alerts and recommending mitigation strategies based on historical experiences with data types, content, usage patterns, use cases, and individual user behavior.

Empowering low/no-code data integration and Transformation

By generating data connectors automatically, active metadata enables low/no-code data integration and transformation, making these processes more streamlined and accessible. Nexla leverages this continuous metadata intelligence to enable all these capabilities in its unified data operations offering.

Use cases and techniques for active metadata intelligence

Now that we understand what active metadata is and its benefits, let’s discuss typical use cases for active metadata and techniques for implementing them. 

Automation

The biggest use case for active metadata is automating typical data operations, which can be informed by already collected metadata. In the table below, we discuss the typical metadata that is usually collected passively and how it can drive automation. For example, Nexla leverages API documentation, credential, and rate limit metadata to auto-generate connectors for new data sources with optimized throughput and advanced pagination. Nexla activates passive metadata like data samples, schema design, and underlying logic, which leads to the automated generation of Nexsets (Data Products), which are the comprehensive building blocks for modern data pipelines. In a similar sense, all aspects and kinds of metadata can drive automation, like observable data management, dynamic data pipelines, and intelligent querying.

Automation use cases for active metadata

Data governance

Active metadata plays a pivotal role in driving and streamlining data governance. By offering real-time tracking, alerting, and policy enforcement, it ensures regulatory compliance, enhances data trust, enables security classification, and regulates data access effectively in a variety of areas:

• Compliance and regulations: Active metadata facilitates the tracking and tagging of data throughout its lifecycle, playing a crucial role in compliance and regulatory processes. For instance, sensitive data is monitored to detect and alert about misuse, while data retention policies are upheld by identifying and purging stale or unused assets.
• Data trust enhancement: Active metadata can enhance data trust by issuing real-time alerts and announcements related to data assets. Examples include flagging assets when anomalies are detected and communicating upcoming changes or asset depreciation to downstream users. These proactive notifications foster better trust among users.
• Security classification: Active metadata can support data security by tracking changes such as column additions or updates, tag alterations, and asset purging. This empowers the data security team to become more proactive as real-time alerts on change events are automatically dispatched. For example, any modification to a sensitive asset could trigger an immediate Slack notification and automatically generate a Jira ticket for the security team.
• Regulating data access: Active metadata enables efficient data access regulation. Access control policies can be defined using contextual metadata, such as classifications and business glossaries, and linked to pertinent data assets and their fields. This facilitates the automatic propagation of tag-based or attribute-based access control across assets via column-level lineage, making it easier to monitor data access requests and their context at scale.

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Enhancing data lineage and cataloging

Beyond governance and automation, the biggest driver of active metadata is in data lineage and cataloging. We discuss below use cases for active metadata in enhancing proactive data lineage and embedded actionable cataloging:

• Automated data lineage: Active metadata can enrich data lineage by inferring metadata from upstream systems and deriving lineage for downstream assets based on data transformations. This facilitates tracking data flow across end-to-end pipelines and expedites root cause or impact analysis. For instance, if a BI system user notices an anomaly in a metric, that user can use the lineage graph to trace the metrics back to the source, aiding in root cause analysis. Moreover, the proactive inference of impact on downstream systems due to changes in upstream systems can be facilitated based on transformation logic metadata.
• Expedited service requests: Active metadata can significantly reduce the turnaround time for service requests by analysts and data/analytics engineers. By activating metadata around a data product, users get a comprehensive view of each data asset within their workflow. This allows them to debug or understand potential points of failure without awaiting service requests. A complete profile of a data product also speeds up the onboarding process for new team members, providing them with all the necessary contextual information, such as ownership, dependencies, freshness, and quality. The following figure illustrates how Nexla’s Nexsets harness the “data as a product” methodology. This approach integrates metadata directly into the user’s workflow, ensuring that all pertinent metadata is displayed coherently alongside the associated data asset.

Metadata in Nexla’s Data Product (Nexsets) (Source)

• Enriched data cataloging experience: Active metadata can enhance the data cataloging experience by providing users with metadata and the context of data assets within their workflow. This could be in the form of labels in a report that provide a 360-degree view of a metric’s lineage, ownership, usage stats, and more. This experience can be further enhanced by leveraging a Data Marketplace where all data products are cataloged with a comprehensive view of their metadata.

Emergence of active metadata in diverse contexts

Now let’s delve into how active metadata serves as a driving force in various paradigms, such as data fabric architecture, data mesh frameworks, and data observability. We explore its role in these contexts, demonstrating its capability to enhance and streamline data management operations.

Data fabric

In the case of a data fabric, metadata extracted from a multitude of tools can be harnessed to identify overlaps among users, data flows, data assets, and security protocols. Initially, these patterns are essentially records from virtually every data management tool or platform. The data fabric utilizes metadata to learn, listen, and react. 

Despite its omnipresence in various forms, from database systems to query logs and data schemas, metadata is often fragmented. By applying continuous analytics to this diverse and dispersed metadata, we cultivate a more robust metadata intelligence, or “active metadata.” This process involves observing record-level data, deriving metadata, and merging it with system metadata to gain a profound understanding of the data. This metadata intelligence layer (refer to the figure below), embedded in the data fabric, enhances the semantics of the underlying data, enabling the generation of actionable alerts and recommendations. Consequently, it bolsters data accuracy and usability for data consumers.

Active metadata or metadata intelligence in a data fabric (Source)

Data mesh

Within a data mesh framework, passive metadata generated by domains and during inter-domain data processing can be instrumental in assessing the health, discoverability, and usage of data products. It can also aid in identifying opportunities for product improvement, accessibility enhancement, and potential combinations of different data products. Active metadata becomes crucial in understanding the metadata of various data products, integrating key aspects such as freshness, health score, usage, lineage, etc., into the data product marketplace or catalog. This can significantly improve user experiences across data domains and drive the federated governance model inherent in the data mesh.

Data observability

Active metadata enhances data observability by aiding in the identification of unexpected scenarios, determining what requires monitoring and who should receive notifications, and pinpointing the origins of issues while assessing their impacts. This approach contrasts with traditional monitoring, which often requires predefined conditions. Active metadata provides visibility into any changes within the data and the broader data landscape. Gartner’s data observability model includes five areas: data content; data flow and pipeline; infrastructure and compute; user, usage, and utilization; and financial allocation (refer to the figure below). The key to a successful data observability solution is to activate the metadata from these observations, rendering it actionable.

Metadata-driven data observability (Source)

Maximizing metadata intelligence with active metadata

In this section, we delve into strategies for kickstarting the activation of metadata and harnessing its maximum potential. The ensuing points serve as a comprehensive guide, outlining best practices for initiating and implementing active metadata.

• Comprehensive passive metadata collection: The first step toward maximizing metadata intelligence involves the comprehensive collection and utilization of passive metadata. This process requires a thorough examination of all data systems to identify potential metadata generation points. Given that active metadata is always on, it is important for data systems to continually collect metadata from various sources and data flow steps, such as logs, query history, and usage statistics. One effective approach could be applying “who, what, when, where, why, and how” to every available data asset.
• Metadata standardization: Utilize standards such as the Dublin Core Metadata Element Set (DCMES) or ISO 158369 to standardize metadata definitions and ensure compatibility across various data sources.
• Metadata activation: After preparing the passive metadata, the next step is to continually process this metadata to render it actionable and intelligible, thereby connecting disparate systems. The utility and intelligence of an active metadata system grow with the volume of metadata it handles.
• Making active metadata actionable: Active metadata should drive actions, starting with learning alerts, advancing to recommendations, and progressing to identifying systems capable of receiving instructions via metadata sharing or exchange. This could range from machine-managed orchestration and optimization for active systems to simply observing, reporting, and alerting for more brittle systems, allowing them to coexist within a data fabric, mesh, or data management ecosystem.
• Embedding metadata into user workflows: To maximize the adoption of metadata-driven data management, it is best to integrate metadata into user workflows. APIs can facilitate this integration at every step of the data management pipeline, or metadata-management-specific tools can be used. Adhering to DataOps best practices ensures that both metadata and data are fresh, reliable, and accurate.
• Metadata sharing: Employing data sharing, data marketplaces, and data catalogs can augment the use cases for active metadata management, enhancing collaboration between data teams. Metadata serves as the key connector between siloed and heterogeneous data assets.
• Addressing technical challenges: Implementing active metadata can present challenges in areas such as scalability, privacy, and security concerns. Practical solutions may include rigorous access controls, encryption, regular security audits, and leveraging scalable cloud architectures. Collaborating with data security experts and adhering to regulatory compliance can help mitigate these risks and enable successful implementation.
• Iterative improvements: Understand your metadata maturity and iterate accordingly. Gartner’s metadata maturity curve is one way to assess your current position in terms of metadata intelligence and can guide your approach to potential enhancements and improvements in metadata intelligence.

Metadata maturity curve (Source)

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Conclusion

The significance of metadata, particularly active metadata, in today’s data-centric world cannot be overstated. It forms the backbone of our understanding of data, serving as the map that guides us through the intricate landscape of data assets. 

This article has sought to provide an in-depth view of the impact and potential of active metadata, including discussing the types of metadata, the need for active metadata, its benefits, and various use cases. We also explored the emergence of active metadata in diverse contexts, such as data fabrics, data meshes, and data observability, demonstrating how it functions as a transformative agent across these paradigms. Finally, we shared strategies on how to maximize metadata intelligence with active metadata, offering a roadmap to navigate this promising terrain.

As aptly expressed by Tim Berners-Lee, the inventor of the World Wide Web: “Data is a precious thing and will last longer than the systems themselves.” It’s metadata that provides the context, meaning, and actionable insights from this precious data. As we move further into the data-driven era, the proactive use of active metadata will undoubtedly continue to unlock new levels of understanding and efficiency, driving innovation and growth.