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From principles to actions: building a holistic approach to AI governance

IBM Big Data Hub

Whether it be financial services, employee hiring, customer service management or healthcare administration, AI is increasingly powering critical workflows across all industries. Add navigating complex compliance regulations and standards to the mix, and the need for a solid and trustworthy AI strategy becomes clear.

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AI Governance: Why our tested framework is essential in an AI world

Collibra

As we speed into a new AI era, there’s a critical element that’s often missing when organizations rush forward in hyper-competitive markets to build scalable, trusted AI programs — and that’s AI governance. An AI governance framework offers a blueprint for how to create successful AI products.

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#ModernDataMasters: Martin Squires, The Analysis Foundry

Reltio

Martin Squires is a leader with extensive experience in customer insight, marketing analytics & data science. Selected for the last 5 years as a member of the Data IQ Data 100 , Martin has considerable experience helping organisations drive value from building a deeper understanding of their customers. Keep learning.

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Reltio Named a Leader in the 2021 Forrester Wave™ Report for MDM

Reltio

“The Reltio MDM solution is part of the Connected Data Platform, which supports all data types in real time for operational, analytical, and data science use cases.”. Reltio scored well in matching, context, governance, business templates, deployment, security, and scalability.

MDM 98
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3 key reasons why your organization needs Responsible AI

IBM Big Data Hub

This necessitates the detection of bias during data acquisition, building, training, deploying and monitoring models. Protect and scale against government regulations AI regulations are growing and changing at a rapid pace and noncompliance can lead to costly audits, fines and negative press. So what is AI governance?

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How to choose the best AI platform

IBM Big Data Hub

AI platforms offer a wide range of capabilities that can help organizations streamline operations, make data-driven decisions, deploy AI applications effectively and achieve competitive advantages. Visual modeling: Combine visual data science with open source libraries and notebook-based interfaces on a unified data and AI studio.