Poor quality, unusable data is a burden for those at the end of the data’s journey. These are the data users who use it to build models and contribute to other profit-generating activities.

The use of data for competitive advantage isn’t new with business analytics predating the big data era, says Columnist Irving Wladawsky-Berger. But now data is being applied to machine learning ...

2. Build a strong data foundation. Machine learning models thrive on high-quality data. For businesses operating in data-intensive environments, ensuring that data is structured, clean and ready ...

By 2030, it’s expected that the market for streaming data will eclipse $73 billion, growing nearly 20% each year until then. More impressively, the machine learning market—which brought in $15 ...

CHICAGO--(BUSINESS WIRE)--CloudQuant LLC has proven the value in the Precision Alpha Machine Learning Signals (PA Signals) alternative data set.Its detailed data science study shows a long-short ...

Another way of thinking about the value of transfer learning is in terms of generalization. ... While many machine learning experts and data scientists are likely familiar with it at this point, ...

With machine learning ... There’s no disputing the value of accurate, timely and consistent data for modern enterprises — it’s as vital as cloud computing and digital apps.

Best practices for data preparation in machine learning. ... For example, if you’re an airline with passenger data, you might elect to drop a null value into the field that tracks meal preferences.

The Data Science Lab. Data Prep for Machine Learning: Normalization. Dr. James McCaffrey of Microsoft Research uses a full code sample and screenshots to show how to programmatically normalize numeric ...