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Data Integrity, the Basis for Reliable Insights

Sisense

Uncomfortable truth incoming: Most people in your organization don’t think about the quality of their data from intake to production of insights. However, as a data team member, you know how important data integrity (and a whole host of other aspects of data management) is. What is data integrity?

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End-to-end development lifecycle for data engineers to build a data integration pipeline using AWS Glue

AWS Big Data

Many AWS customers have integrated their data across multiple data sources using AWS Glue , a serverless data integration service, in order to make data-driven business decisions. Are there recommended approaches to provisioning components for data integration?

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3 Ways Atlas for Microsoft Dynamics 365 F&O Addresses Data Integrity Issues

Jet Global

Data integrity issues are a bigger problem than many people realize, mostly because they can’t see the scale of the problem. Errors and omissions are going to end up in large, complex data sets whenever humans handle the data. Prevention is the only real cure for data integrity issues.

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CIO insights: What’s next for AI in the enterprise?

CIO Business Intelligence

IT leaders expect AI and ML to drive a host of benefits, led by increased productivity, improved collaboration, increased revenue and profits, and talent development and upskilling. Ensuring data integrity is part of a broader governance approach organizations will require to deploy and manage AI responsibly.

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Artificial intelligence and machine learning adoption in European enterprise

O'Reilly on Data

Given the end-to-end nature of many data products and applications, sustaining ML and AI requires a host of tools and processes, ranging from collecting, cleaning, and harmonizing data, understanding what data is available and who has access to it, being able to trace changes made to data as it travels across a pipeline, and many other components.

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Confidential Containers with Red Hat OpenShift Container Platform and IBM® Secure Execution for Linux

IBM Big Data Hub

The protection of data-at-rest and data-in-motion has been a standard practice in the industry for decades; however, with advent of hybrid and decentralized management of infrastructure it has now become imperative to equally protect data-in-use.

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NLP Isn’t Enough. Leading Financial Services Companies Are Now Moving to Conversational AI.

CIO Business Intelligence

As with all financial services technologies, protecting customer data is extremely important. In some parts of the world, companies are required to host conversational AI applications and store the related data on self-managed servers rather than subscribing to a cloud-based service.