Forrester shifts perspectives on data catalogs, revealing essential insights
In the rapidly evolving world of data, a company's data assets in 2022 encompass a wide range of data products and associated assets, such as databases, pipelines, services, policies, code, and models. This diversity necessitates a shift in data management strategies, leading to the emergence of new concepts like DataOps, data mesh, and data fabric.
At the heart of these modern data management approaches lies the ability to collect, store, and analyze metadata. DataOps, for instance, leverages Enterprise Data Catalogs (EDCs) to manage metadata effectively. An example of EDCs in action is a data team that ingests 1.2 TB of event data every day and uses APIs to assess incoming data and automatically create its metadata.
Companies are increasingly using APIs to automatically track and trigger notifications for metadata change events. This continuous monitoring ensures that they always know the downstream effects of these changes, enabling them to maintain control over their data pipelines.
Implementing Continuous Integration/Continuous Deployment (CI/CD) practices by DataOps requires detailed intelligence of data movement and transformation. EDCs provide this intelligence, enabling seamless integration of CI/CD practices into data workflows.
In recent years, metadata management has undergone significant changes, moving away from old-school data catalogs and towards the future of metadata. The shift in focus from machine learning data catalogs to enterprise data catalogs for DataOps is due to the low adoption rates of the former.
The new concept of metadata is not the old-school idea of metadata that is monolithic, complex, and slow to implement. Instead, it is dynamic, real-time, and essential for DataOps. Active metadata sends enriched metadata and unified context back into every tool in the data stack, enabling powerful programmatic use cases through automation.
EDCs are designed around modern DataOps and engineering best practices, connecting the "data and developer environments" and facilitating simpler, faster data delivery across teams and functions. Each of these assets has its own metadata that keeps getting more detailed.
The data industry is experiencing a shift in how it thinks about metadata, with new ideas such as the metrics layer, modern data catalogs, and active metadata emerging. The use cases for metadata are growing at a rapid pace, making the standard way of storing metadata no longer sufficient.
This transformation positions Enterprise Data Catalogs not merely as static repositories but as active enablers of agile, automated, and intelligent data operations aligned with modern enterprise needs. EDCs handle the diversity and granularity of modern data and metadata, acting as a "system of record" to automatically capture and manage all of a company's data through the data product lifecycle.
In summary, the current state of EDCs for DataOps is evolving towards more automation, continuous data management, and integration with AI/ML capabilities to improve data workflows' speed and accuracy. Future trends focus on increasing automation of data pipelines, enhanced metadata-driven governance, and integration with observability tools for monitoring data workflows.
Active metadata plays a crucial role in this evolving landscape by enabling dynamic, real-time metadata collection and utilization throughout the data lifecycle. Unlike traditional static metadata cataloging, active metadata continuously captures metadata from data pipelines, applications, and infrastructure, allowing enterprise data catalogs to support automated data lineage tracking, real-time data quality insights, impact analysis, and governance enforcement.
Forrester has recently scrapped its Wave report on "Machine Learning Data Catalogs" to make way for one on "Enterprise Data Catalogs for DataOps". EDCs provide programmatic features, such as automated flags, alerts, and suggestions, to help users keep on top of complex, fast-moving data flows. They are tools that create data transparency and enable data engineers to implement DataOps activities.
In essence, the role of EDCs is to act as the backbone of this new category by enabling real-time, actionable metadata that supports automated data pipeline monitoring, dynamic governance, and proactive issue resolution within DataOps workflows. This active approach to metadata management is the key to unlocking the full potential of DataOps in today's data-driven world.
- Businesses are shifting their focus from traditional data catalogs to enterprise data catalogs for DataOps, as the latter offers better metadata management and integration with modern data workflows.
- In the realm of personal finance, technology-driven advancements in data management, like DataOps and enterprise data catalogs, can help streamline data delivery across teams and functions, leading to more efficient and accurate financial decision-making.
- Investing wisely in DataOps technologies, such as enterprise data catalogs, can provide a company with the data transparency and real-time, actionable metadata necessary for proactive issue resolution and automated data pipeline monitoring, contributing to overall business success.
- In an era of rapid technological growth, particularly in data and cloud computing, understanding and mastering the concepts of data mesh, data fabric, and DataOps through education and self-development can be crucial for career development in the business sector, especially for data engineers and business analysts.
