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AI model governance: What it is and why it’s important

Collibra

If you want to implement AI governance, you may face several challenges, including: AI models are complex and require a cross-functional team for development and deployment that includes expertise in various areas such as data science, software engineering and compliance.

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The top 10 blogs and columns of 2017

Information Management Resources

Data science, business intelligence, data security, the GDPR and artificial intelligence were the topics that were top of mind for readers.

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Harnessing Analytical Insights and Illuminating the Physical Realm of Dark Data – An Interview with Markus Lindelow of Iron Mountain

Information Governance Perspectives

I interviewed him this November to discuss his thoughts on the evolution of metadata, content classification, AI, and how companies are using the new pillars of data science to break down their silos, help customers get lean and discover the hidden values in their big data sets.

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The top 10 blogs that were reader favorites for 2018

Information Management Resources

Data science skills, blockchain, master data management and the General Data Protection Regulation were topics that most captured reader attention in 2018.

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Harnessing Analytical Insights and Illuminating the Physical Realm of Dark Data – An Interview with Markus Lindelow of Iron Mountain

Information Governance Perspectives

I interviewed him this November to discuss his thoughts on the evolution of metadata, content classification, AI, and how companies are using the new pillars of data science to break down their silos, help customers get lean and discover the hidden values in their big data sets.

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Talking data ethics: What it is and why it’s important

Collibra

Let’s take the latter scenario in today’s data ecosystem: Privacy laws such as GDPR and CCPA are meant to mandate organizations to implement and enforce consumer privacy protections for customer personal data. So how do you anchor your data strategy to an ethical framework that works for your organization?

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Four use cases defining the new wave of data management

IBM Big Data Hub

As for data delivery at the “right time” automated data engineering tasks, workload balancing and elastic scaling should provide the needed alacrity for all businesses. Data governance and privacy. Poor data quality costs organizations an average of $12.9 Read the eBook. Start a trial. million each year [1] and $1.2