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Real-time artificial intelligence and event processing  

IBM Big Data Hub

Non-symbolic AI can be useful for transforming unstructured data into organized, meaningful information. This helps to simplify data analysis and enable informed decision-making. Stream analytics can be used to help improve the speed and accuracy of models’ predictions.

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How IBM and AWS are partnering to deliver the promise of AI for business

IBM Big Data Hub

IBM, a pioneer in data analytics and AI, offers watsonx.data, among other technologies, that makes possible to seamlessly access and ingest massive sets of structured and unstructured data. AWS, on the other hand, provides robust, scalable cloud infrastructure.

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The 7 most common data quality issues

Collibra

Duplicate data increases the probability of skewed analytical results. As training data, it can also produce skewed ML models. Rule-based data quality management can help you keep a check on duplicate and overlapping records. Ambiguous data. This situation gets more overwhelming for data streaming at high speed.

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OpenText Showcases how Intelligence, Integration and Automation Drive Innovation in Content Services at AIIM Conference 2019

Info Source

OpenText Content Services help organizations readily access critical business information through the delivery of “content in context” for both structured and unstructured data. Conference attendees can access demos and further information at OpenText’s booth (31 and 32) at the AIIM Conference tradeshow.

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Do I Need a Data Catalog?

erwin

A data catalog uses metadata, data that describes or summarizes data, to create an informative and searchable inventory of all data assets in an organization. Automating the cataloging of data assets saves initial development time and streamlines its ongoing maintenance and governance.

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Tech Tuesday: Getting Started with Classification

Everteam

Machine learning makes it possible to analyze unstructured data semantically to suggest classifications based on text found. It will help you meet regulatory requirements much quicker (and more accurately) than manual classification. But there are also situations where manual classification is necessary.

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Talk Data to Me: Why Employee Data Literacy Matters  

erwin

Data Literacy & the Rise of the Citizen Analyst. According to Gartner , “data literacy is the ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied — and the ability to describe the use case, application and resulting value.”.