Predictive Analytics Buchzusammenfassung - das Wichtigste aus Predictive Analytics

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Zusammenfassung von Predictive Analytics

Name: Predictive Analytics
Rating: 4 (75 reviews)
Author: Eric Siegel

Eric Siegel

The Power to Predict Who Will Click, Buy, Lie, Or Die

4 (75 Bewertungen)

17 Min.

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"Predictive Analytics" by Eric Siegel offers insights into the world of data science and how predictive models can be used to make informed decisions for businesses and individuals. Learn how to turn data into gold.

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What’s in it for me? Learn the basics of predictive analytics.

The amount of data swirling around online every day is truly mind blowing. Every post you like on Facebook, every purchase you make online and every ad you click generates data – oceans of data. For companies and governments wishing to better understand, and influence, your behavior, this data is a goldmine.

By using predictive analytics, or PA, a branch of analytics concerned with predicting future events, companies can use the data you leave behind to predict your future behavior with uncanny precision.

As you’ll see in these blinks, however, this increasing ability to predict individual behavior also raises important moral and ethical questions. Do we want our future to be predicted?

In these blinks, you’ll learn

how PA works;
why IBM’s Watson is the greatest leap in artificial intelligence so far; and
that PA may be somewhat prejudiced.

Predictive analytics can help you lower your risks and make safer decisions.

Every time a company invests in an expensive marketing campaign, they’re taking a risk; there’s always a chance the campaign might fail and millions of dollars will disappear down the drain. However, when predictive analytics are used, a company can reduce that risk.

The purpose of predictive analytics, or PA, is to study human behavior and get a sense of how people will respond to certain situations, such as seeing an advertisement.

It does this by taking into consideration a wide variety of statistics and human characteristics, all of which are focused on understanding individual, as opposed to general, behaviors. So you wouldn’t use PA to determine which advertisement has the broadest appeal; you’d use it to determine the likeliest responses of specific people to specific advertisements.

More precisely: once you enter all your variables, you’re given a predictive score. Now, this score doesn’t tell you the future as much as it tells you how probable certain individual reactions will be.

For example, let’s say you want to know which online ad people in the United States will be most tempted to click on while searching for grants and scholarships. The more variables you supply, such as age, gender and email domain, the more precise the predictive score will be.

These predictive scores are useful to organizations that want to know the best demographics to target for certain discount offers and advertisements, as well as for organizations that want to know which stocks to buy or people to audit.

The predictive model used in PA is more dynamic than other models since it’s based on machine learning, which means it can change, grow and adapt based on the kind of data it is given. And it’s more accurate than other predictive tools since it uses backtesting, which takes old data to determine how accurate your results will be.

So, if you’re trying to predict whether the S&P Index is going to go up or down in a year’s time, with backtesting, you can feed it old data from 1990 to see how accurate it is about the S&P in 1991.

Making predictions leads to questions of responsibility, morality and prejudice.

As our ability to use technology for predictive purposes gets better and more sophisticated, an important question arises: How many spoilers do you want to be given about your life? And, more importantly, how many lives are you willing to spoil?

But it’s more than just the question of knowing about your future; a more pressing concern with predictive analytics, and the data mining that goes hand-in-hand with it, is privacy.

Data is always predictive but accuracy requires a balanced amount of data.

These days data is a valuable and essential business commodity, and we’re producing more of it every single day. In predictive analytics, the philosophy is: the more data, the better – as long as it’s well balanced.

This means you have to be selective in the data you use, and add equal amounts of each type.

Machine learning can find risks that get overlooked, but there are risks to machine learning as well.

As we've seen, predictive analytics benefits from machine learning, so that, over time, a model can get even more precise in its predictions.

But there’s another very important benefit of machine learning, which is its ability to recognize disguised risks, or “microrisks.”

Bringing together multiple sources and models increases accuracy and performance.

Like aspiring artists and entrepreneurs, predictive analytics has benefited from crowdsourcing. By opening itself up to the collective mind of the public, PA has been able to reap the rewards of ensemble modeling.

The ensemble model is a combination predictive model, and it was developed through the kind of competition and cooperation that crowdsourcing contests promote.

Human language poses difficult challenges, but big advancements have already been made.

The increased power of ensemble models has been put to use for some fascinatingly complex projects, including the ability of machines to process natural language.

One of the biggest challenges in any project involving computational linguistics is picking up on all the subtle nuances in human speech.

Predictive analytics can help identify the imperceptible by quantifying persuasion.

Do you hate getting spam from mobile-phone companies and creditors? The good news is that advancements in predictive modeling now let companies know which people are receptive to unprompted ads and which ones are best left alone.

No company wants to annoy its target audience into avoiding its product or service, so there’s a need to be subtly persuasive, which is where the art of predictive analytics is headed.

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Worum geht es in Predictive Analytics?

Predictive Analytics (2016) provides a helpful introduction to a complex and fascinating field. Learn how data gets crunched so that people can make more informed decisions, a practice that has drastically altered the way the world conducts its research and runs its businesses. Siegel offers an enlightening glimpse at the wide-ranging areas that have been forever changed, from marketing to health care, banking to artificial intelligence.

Bestes Zitat aus Predictive Analytics

Ensemble modeling is often cited as the most important advance in predictive analytics to occur in this century.

—Eric Siegel

Wer Predictive Analytics lesen sollte

Business students interested in applied analytics
Readers interested in economics
Tech geeks curious about artificial intelligence

Über den Autor

Eric Siegel is a world-renowned leader in the field of predictive analytics and the founder of the Predictive Analytics World Conference Series. A former Columbia University professor, he’s also the executive editor of the Predictive Analytics Times.

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