Utilizing a Self-Defined Data Fabric for the Implementation of Active Metadata
Published On: October 12, 2023
When individuals assert that data is the modern equivalent of oil, they are implying that data must be extracted and processed to yield valuable insights. Otherwise, data remains as inert as untapped oil reserves buried underground or beneath the ocean floor. Metadata represents a pivotal tool in enhancing data accessibility and usability. Metadata, often referred to as data about data, empowers users to efficiently locate desired information, adhere to established rules and policies, and ultimately derive valuable insights from it.
In recent times, enterprises have begun to recognize that the advantages offered by passive metadata alone are limited. To fully capitalize on their data’s potential, organizations must activate their metadata, enabling data to dynamically flow in a contextualized manner across the entire data pipeline.
However, implementing active metadata is no simple feat. The deployment of active metadata entails a multifaceted process fraught with numerous challenges, including data integration, data governance, data privacy and security, data quality, metadata versioning, and interoperability.
To effectively deploy active metadata, organizations can leverage a self-defined data fabric provided by Orion Governance’s Enterprise Information Intelligence Graph (EIIG). EIIG stands as the premier platform for active metadata management, distinguished by its extensive coverage, holistic approach, and AI/ML-powered automation.
EIIG’s comprehensiveness is evident in two major aspects. First and foremost, EIIG possesses the capability of ingesting metadata from an expansive array of technology sources, encompassing mainframes, DBMS, NoSQL databases, ETL and reporting tools, as well as programming languages such as Python, Java, and Scala. This coverage extends beyond breadth; it also delves into depth. EIIG scrutinizes code and captures the DNA of datasets, thus providing the most granular metadata available. It is important to note that active metadata relies on metadata itself for activation. In the absence of this comprehensive coverage, an active metadata infrastructure remains inadequate.
Secondly, with EIIG, enterprises can comprehensively address the aforementioned challenges. EIIG intelligently weaves metadata, irrespective of differences in schemas, formats, or integration technologies, into a knowledge graph that establishes both intra- and inter-system connectivity. This data integration prowess is complemented by data governance features, including the mapping of governance rules and policies to technical assets, role-based access control, and the enforcement of data standards.
EIIG also facilitates the identification of sensitive data, such as Personally Identifiable Information (PII), to ensure compliance with data privacy regulations. Data quality is enhanced through the visualization of quality scores at each stage of the data pipeline. Moreover, EIIG meticulously documents every minute change, ensuring thorough metadata versioning. Finally, with an open architecture, EIIG enables enterprises to seamlessly integrate with other platforms and tools via bi-directional APIs, thereby eliminating vendor lock-in.
Active metadata does not operate in isolation but rather intertwines with various other capabilities. EIIG adopts a holistic approach, integrating all these capabilities seamlessly within a single product. In the case of EIIG, the product and platform are synonymous. There is no patchwork of disparate products, acquisitions, or reliance on third-party tools. With EIIG, intelligent metadata ingestion and stitching, dynamic data cataloging, end-to-end data lineage tracking, impact analysis, metadata analytics, quality augmentation, and trust propagation are all inherently integrated from the outset.
A hallmark of a self-defined data fabric is the minimization of human intervention. Powered by AI and machine learning, EIIG automates all aspects of the active metadata ecosystem. EIIG not only automatically ingests metadata from a wide spectrum of technology sources but also continually updates metadata as data is utilized and modified. The creation of a data catalog is automatic, complete with the automatic mapping of terms to assets. EIIG’s potent knowledge graph automatically visualizes data lineage as metadata is stored in its repository. By simply clicking on a data point in the data lineage graph, users can swiftly conduct impact analysis and obtain results within seconds.
EIIG also offers hierarchical views of the same data flow. As a result, different user personas can access and visualize the same data from their unique perspectives, at varying levels of granularity. Furthermore, EIIG’s automation obviates the need for laborious platform management tasks. It effortlessly fosters collaboration between diverse teams and bridges the gap between technical and business users. Data stakeholders are freed from expending manual efforts in the quest for data discovery and access, allowing them to focus on leveraging trusted, readily available data for informed decision-making.
In conclusion, EIIG offers a robust platform for the implementation of active metadata, enabling enterprises to fully unlock the value of their data and information assets. With EIIG, organizations gain the capacity to efficiently discover, access, govern, and harness data for innovation and informed decision-making. Schedule an EIIG demo to learn more.
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