Big data

Big data (232)

How One Nonprofit is Bridging the Gender Gap in the Data Industry

According to Kaggle, an online community for data scientists and machine learners; only 16% of women make up all data scientists. While there are no statistics to confirm, trends from other industries would indicate the percentage for women of color in the data field is even lower. I had the chance to speak to Sadie St. Lawrence, the CEO and founder of Women in Data to discuss women’s role in data and why it’s so crucial to minimize the gender gap in the industry.

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4 Ways Marketers Can Ensure They Learn The Right Lessons From Data

In this digital age, marketers have more data to work with than ever before. But actually deriving meaningful insights from that data and converting those insights into action is easier said than done. Often, the problem is that, with so much data at their disposal, marketers are confused about where they should direct their attention.

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Why Machine Learning Failed at Predicting the Academy Awards

Can predictive analytics be used to forecast which movie will walk away with the top honor at the Academy Awards? Apparently not. While predictive analytics is useful to forecast future trends, behaviors and patterns- Oscar winners are not one of them. Often times it is used to lower risks, improve software or anticipate customer behavior. Combined with machine learning and algorithms, predictive analytics incorporate past and current data to provide the most logical conclusion.

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How to Monetize Your Data

Is your company a data company? Whether you actually sell software that monitors data or run a brick and mortar store, the answer is yes. Monetizing data a business already collects can significantly increase a company’s revenue.  Gartner defines Data Monetization as “using data for quantifiable economic benefit.”  Whether that entails selling your findings to third party sources or using this intelligence to create a more efficient product is up to the organization itself.

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6 Steps to Data-Driven Transformation

We’re now well into the Fourth Industrial Revolution. The First Industrial Revolution was about steam and railroads, the Second about electricity, and the Third brought about by the Internet. AI, the basis of the Fourth Industrial Revolution, will completely change the way business is done and companies are run in the next five to ten years, just as the Internet has done in the last ten. The transformation will be bigger than that any previous revolution has brought about.

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How Much Is Your Company’s Data Worth?

In the age of digital technology, the biggest asset any company has is its data. But despite hearing that fact repeated over and over, organizations often haven’t a clue what their data is worth. That’s no surprise, considering how difficult it is to measure the value of enterprise data without generally accepted accounting principles.

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Alibaba, the Supply Chain, and Changes in China

It is well known that China has been the biggest producer of goods for many years. From children’s school supplies to the contents of every handyman’s toolbox, “Made in China” is found in small print. However, China’s labor costs, much due to its economic growth from manufacturing, have risen to the point that the competition from nearby Asian countries is taking some of that manufacturing business. These shifts, along many others over the last two decades, have been heavily influenced by the conglomerate Alibaba, affecting the supply chain of companies worldwide.

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Predicting Machine Failure with Big Data

The fourth industrial revolution is on the rise. This time, instead of replacing human hands with machinery, companies are replacing scheduled surveys with timely repairs. Industry 4.0 includes machine learning on a variety of levels, but one of the most intriguing (and financially applicable) is predictive maintenance; the practice of predicting machine failure before it happens. Face detection, talking refrigerators, and mood-setting light bulbs are fun, but when big money talks, everybody listens.

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