In my last blog post, I introduced data democratization. Today, I’ll share an approach to architecting a metadata search solution that enables data democratization.
There are a number of ways to craft a comprehensive guided search of a firm’s data catalog and governance metadata repositories, each with its own cost/benefit profiles. For example, creating a single data model that integrates metadata from a set of enterprise data governance tools (glossary, catalog, quality, lineage, etc.) and uses a purpose-built interface can provide a guided user experience, it is also the most time-consuming and costly approach.
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A more efficient approach is to leverage the capabilities of AI-enabled search tools, in conjunction with a data abstraction/indexing/connection layer, to provide the intelligent search capabilities required for an optimal user experience. In this construct, the subject metadata does not have to be extracted from the respective governance tools, reorganized, and redundantly stored. Instead, the abstraction layer performs the logical normalization and indexing necessary to access the various metadata.
In this scenario, the AI-enabled search tool accesses the data governance metadata stores via the abstraction/connection layer, enabling a unified experience across multiple silos while seamlessly directing users back to the content-native environments to view the detailed results. Over time, the AI-enabled search gains intelligence. It collects context and signals from users to drive holistic relevance and gathers analytical insights to drive reporting, refinement, and machine learning (ML)-driven improvement.
In my next post, I will highlight different approaches to developing a metadata search solution.
In the meantime, if you are interested in learning more about this topic, consider downloading our new guide The Search for Data Democratization in Financial Services.