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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

Data from various sources, collected in different forms, require data entry and compilation. That can be made easier today with virtual data warehouses that have a centralized platform where data from different sources can be stored. One challenge in applying data science is to identify pertinent business issues.

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Disrupt and Innovate in a Data-Driven World

Cloudera

The private sector already very successfully uses data analytics and machine learning not only to realise efficiency gains but also – even more importantly – to create completely new services and business models. Identify those most at risk or most affected by a problem more accurately by using predictive analytics.

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Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

Enrichment typically involves adding demographic, behavioral, and geolocation data. You can use third-party data products from AWS Marketplace delivered through AWS Data Exchange to gain insights on income, consumption patterns, credit risk scores, and many more dimensions to further refine the customer experience.

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The Gartner 2021 Leadership Vision for Data & Analytics Leaders Webinar Q&A

Andrew White

But we also know not all data is equal, and not all data is equally valuable. Some data is more a risk than valuable. Additionally, the value of data may change, and our own personal judgement of the the same data and its value may differ. Risk Management (most likely within context of governance).

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What Is Embedded Analytics?

Jet Global

In a recent study by Mordor Intelligence , financial services, IT/telecom, and healthcare were tagged as leading industries in the use of embedded analytics. Healthcare is forecasted for significant growth in the near future. Show how embedded analytics will enhance sales and marketing through better demos and shorter sales cycles.