Difference between revisions of "Data Governance"
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| − | <b> | + | <b>Spark+AI Summit 2018 - Data Governance and Compliance |
| − | </b><br> | + | </b><br>FRANCISCO JAVIER SOTO SUAREZ Twillio Sri Esha Subbiah, Sunil Patil, and Jeechee Chen |
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<b>Data Governance and AI: New Dimensions in Privacy and Compliance | Dataiku & GigaOM | <b>Data Governance and AI: New Dimensions in Privacy and Compliance | Dataiku & GigaOM | ||
</b><br>This webinar walks through practicalities of governance in the age of AI, including governance “checkpoints” for data scientists; the relationship between data regulations, ethics, and AI; and making ML models compliant, both with government regulations and corporate policy. | </b><br>This webinar walks through practicalities of governance in the age of AI, including governance “checkpoints” for data scientists; the relationship between data regulations, ethics, and AI; and making ML models compliant, both with government regulations and corporate policy. | ||
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| + | <youtube>cYml4t-2WU8</youtube> | ||
| + | <b>Data governance in the age of AI | ||
| + | </b><br>Alpharithm Technologies | ||
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| + | <youtube>J7cdI2Nqs7w</youtube> | ||
| + | <b>Non-Personal Data Governance Framework: Impact on AI Based Businesses | ||
| + | </b><br>Voices at Esya Centre In this video we speak with Saket Gupta, Technical Architect at GreyOrange on the Non-Personal Data Governance Framework. | ||
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| + | == Implementing Data Governance with Knowledge Graphs == | ||
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| + | <b>How Implementing Data Governance with Knowledge Graphs Enables Enterprise AI | ||
| + | </b><br>Artificial Intelligence (AI) and Machine Learning (ML) are umbrella terms for a wide set of algorithms, technologies, and approaches that make software seem “smart.” It is now commonly understood that knowledge graphs can help with many enterprise needs such as addressing key challenges of data governance. It is also becoming widely accepted that Knowledge Graphs are excellent at guiding and focusing ML and at serving as a unifying fabric for different AI algorithms. In this webinar we: | ||
| + | • Provide a brief history of Knowledge Graphs | ||
| + | • Demonstrate how they address key challenges of data governance | ||
| + | • Give a concise overview of AI and ML technologies | ||
| + | • Discuss how knowledge graphs provide a powerful platform for both integrated data governance and strategic enterprise AI/Machine Learning | ||
| + | • Showcase specific real-world examples of how knowledge graphs support rules and learning that add new knowledge that can support further learning in a virtuous cycle | ||
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| + | <youtube>1C7nwj3u3w8</youtube> | ||
| + | <b>Enterprise Data Governance: Leveraging Knowledge Graph & AI in support of a data-driven organization | ||
| + | </b><br>As one of the largest financial institutions worldwide, JP Morgan is reliant on data to drive its day-to-day operations, against an ever evolving regulatory regime. Our global data landscape possesses particular challenges of effectively maintaining data governance and metadata management. The Data strategy at JP Morgan intends to: a) generate business value b) adhere to regulatory & compliance requirements c) reduce barriers to access d) democratize access to data In this talk, we show how JP Morgan leverages semantic technologies to drive the implementation of our data strategy. We demonstrate how we exploit knowledge graph capabilities to answer: 1) What Data do I need? 2) What Data do we have? 3) Where does my Data come from? 4) Where should my Data come from? 5) What Data should be shared most? Presentation by Aftab Iqbal, JP Morgan Information Architect, at Connected Data London 2019 | ||
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Revision as of 09:50, 7 September 2020
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- Case Studies
- AI Governance
- Enterprise Architecture (EA)
- Enterprise Portfolio Management (EPM)
- Architectures supporting machine learning
- Evaluation
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Implementing Data Governance with Knowledge Graphs
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Access Sciences: Data Governance series
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