EDM Council® Data Innovation Laboratory

About the Data Innovation Lab

The EDM Council has a number of areas of advocacy that we support, including but not limited to driving standards for data management, governance, lineage, and related topics.

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Purpose:

Developing open-source, publicly available industry ontologies using our Collaborative Ontology Development model – thus driving data interoperability, industry adoption and addressing ambiguity across multiple, competing regulations and standards

Focus:

Industry standard ontology development using the DIL Platform and methodology, resulting in pre-competitive, vendor-agnostic open-source models

Projects:

Projects leveraging the collaborative ontology development methodology and framework provided by the Data Innovation Lab are cross-domain, ranging from finance (FIBO) to the pharmaceutical industry (IDMP-O), automotive (AUTO), government (NIST / OAGI IOF), ESG (Smart Dictionary for GHG reporting standards), and others.

Benefits & Value:

Reducing costs AND implementation effort, dramatically improving industry adoption and reducing development time through effective project management, reuse, customized methods, automation, continuous deployment / integration

To that end, the Data Innovation Lab is organized to

  • Provide Knowledge Graph Awareness, Training, and Certification Programs

  • Support Shared Industry Ontologies as Accelerators to Business Solutions

  • Support a Community of Knowledge Graph Advocates

Data models by and for the global data management community

  • The EDM Council promotes the adoption of data content standards to promote innovation across industries.

  • Identifying the precise meaning of data to ensure business understanding is critical to enable progress, as well as to support the next developments in advanced analytics, artificial intelligence (AI) and machine learning (ML).

Towards interoperability, decision support, new insights through semantics / knowledge graphs

  • Data linkage and integration despite silos

  • Open global reusable data standards

  • Consistent, unambiguous definitions including the logic that distinguishes them for people and machines

  • Highly expressive, flexible data schemas ‒ alignment and interoperability based on meaning, with support for provenance, lineage, interdependencies by design

  • Rich multi-level taxonomies, controlled vocabularies

  • Pattern-based architecture to enable AI / machine-learning with explanations

  • Much higher accuracy and provenance for question answering with large and custom language models (LLMs)*

* https://arxiv.org/pdf/2311.07509.pdf

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