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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

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The Third Modern Data Management Summit: Making Data Work!

Reltio

The third Modern Data Management annual summit ( #DataDriven19 ) held on February 26-27 2019 attracted more than 400 business and IT professionals getting together in San Francisco to witness the future of data management, share success stories and learn best practices. This year’s theme was “ Organize Master Data.

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Anomaly detection in machine learning: Finding outliers for optimization of business functions

IBM Big Data Hub

Anomalies are not inherently bad, but being aware of them, and having data to put them in context, is integral to understanding and protecting your business. The challenge for IT departments working in data science is making sense of expanding and ever-changing data points.

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How to choose the best AI platform

IBM Big Data Hub

” When observing its potential impact within industry, McKinsey Global Institute estimates that in just the manufacturing sector, emerging technologies that use AI will by 2025 add as much as USD 3.7 Visual modeling: Combine visual data science with open source libraries and notebook-based interfaces on a unified data and AI studio.

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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.

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#ModernDataMasters: Michele Chambers, AWS

Reltio

It all came out of applying business understanding to data and analytics, and they decided to go ahead with the top ten suggestions. That’s the piece that technical people forget – to join the dots between the analytics and its practical application in the business.

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Foundational models at the edge

IBM Big Data Hub

Another key vector is the increasing importance of computing at the enterprise edge, such as industrial locations, manufacturing floors, retail stores, telco edge sites, etc. More specifically, AI at the enterprise edge enables the processing of data where work is being performed for near real-time analysis.

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