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This report explores the applicability of semantic approaches to managing data lakes and providing governed self-service value to users.


  • In principle, data lakes should be transparent, manageable, and governable, even if incrementally, without which organizations may be exposed to risks and lower ROI.
  • There are many approaches from data platform and integration providers for making data lakes governable, each with positives and trade-offs.
  • The semantic approach, which is driven by World Wide Web Consortium (W3C) standards, can be helpful for multiple industry segments seeking high-quality data with provenance/lineage and data management. Examples included regulated industries such as financial services, life sciences, pharmaceuticals, healthcare, and the government sectors.

Features and Benefits

  • Illustrates the benefits of building a data lake versus a data landfill.
  • Assesses the applicability of semantics to making the data lake smarter.

Key questions answered

  • What are the key components of a smart data lake?
  • What is the semantic approach?
  • What can semantics do in the data lake?
  • How do organizations typically adopt semantic approaches?

Table of contents


  • Catalyst
  • Ovum view
  • Key messages

Key components to a smart data lake

  • Inventory
  • Security
  • Operation/integration

The semantic approach to data lakes

  • What is the semantic approach?

What can semantics do in the data lake?

  • Other technologies combined with semantics can deliver more than sum of parts
  • Semantic approaches to governing smart data lakes
  • Key positives to using semantic graph-based approaches to data lakes
  • Trade-offs in using semantic approaches, and how to overcome them


  • Methodology
  • Author

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