Organizations are increasingly relying on data management solutions to automate and optimize their operations. One crucial component of these solutions is a robust data catalog that provides comprehensive metadata management capabilities.
The active metadata market encompasses a broad spectrum of data management tools that enable significant active metadata use within their platforms. Active metadata management involves continuous analysis of users, data management, systems/infrastructure, and data governance experiences to align data as designed with actual experiences. It incorporates operationalizing analytic outputs through operational alerts and generated recommendations, which drive AI-assisted reconfiguration of data and active metadata utilization.
Essential capabilities in the active metadata management market include ML over profiling, content analysis, user/use-case clustering, resource allocation metrics, alerts and recommendations, ML by case example with trend and usage, orchestrate recommendation and response, and use case to new asset inference. The market is shifting with a focus on market adoption and the business value of metadata sharing. Passive metadata continues to drive growth, but active metadata management is gaining traction. Established metadata management solutions are leveraging active metadata practices and techniques, while new players are entering the market with branded active metadata management solutions.
Interest in active metadata has seen significant growth, highlighting the increasing demand for comprehensive metadata management capabilities. Purpose-built metadata management tools are facing challenges from adjacent data management platforms, such as databases, data integration, data quality, and data governance tools.
In Summary:
- Organizations are seeking active metadata to achieve augmented data management capabilities, organizations are demanding “active metadata” that enables continuous access and processing of metadata. This ensures ongoing analysis, design recommendations, and operational alerts for improved decision-making.
- Existing metadata management tools fall short in that they are increasingly incapable of fulfilling comprehensive metadata needs in the enterprise. As a result, organizations are exploring advanced metadata functionality from mature metadata solutions or embedding metadata capabilities within other data management platforms.
- Lack of common metadata standards poses a significant challenge for metadata sharing and interoperability across multiple metadata management solutions in the market. This further emphasizes the need for flexible and adaptable metadata management solutions.
Companies Should:
- Adopt metadata management solutions with prescriptive capabilities with parameterized recommendations to alter design inputs. These solutions should exploit adjacent data management tools, such as data observability tools, for effective operations.
- Assess capability to share internal metadata via data management tools and platforms that are able to support broader platform-to-platform orchestration and enhance interoperability across the data management ecosystem.
- Capture runtime metadata including data usage, data affinity, and user behaviors. By automating metadata capture, organizations can unlock the value of metadata and drive automation in their data management processes.
- Import and export metadata to facilitate metadata integration, processing instructions, and optimization strategies to enhance metadata-driven decision-making.
- Enable automated system changes by leveraging metadata analytics workflow management capabilities in adjacent data management systems. Collaborative design capabilities can be the key to enabling seamless integration and efficient metadata-driven operations.