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2 months ago
Three Common Mistakes That Can Derail Your Team’s Predictive Analytics Efforts

 

Originally published by Harvard Business Review

With today’s high demand for data scientists and the high salaries that they command, it’s often not practical for companies to keep them on staff.  Instead, many organizations work to ramp up their existing staff’s analytics skills, including predictive analytics. But organizations need to proceed with caution. Predictive analytics is especially easy to get wrong. Here are the first three “don’ts” your team needs to learn, and their corresponding remedies.

1) Don’t Fall for Buzzwords – Clarify Your Objective

You know the Joe Jackson song, “You Can’t Get What You Want (Till You Know What You Want)”? Turn it on and let it be your mantra. As fashionable as it is, “data science” is not a business objective or a learning objective in and of itself. This buzzword means nothing more specific than “some clever use of data.” It doesn’t necessarily refer to any particular technology, method, or value proposition. Rather, it alludes to a culture – one of smart people doing creative things to find value in their data. It’s important for everyone to keep this top of mind when learning to work with data.

Under the wide umbrella of data science sits predictive analytics, which delivers the most actionable win you can get from data. In a nutshell, predictive analytics is technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions. Prediction is the Holy Grail for more effectively executing mass scale operations in marketing, financial risk, fraud detection, and beyond. Predictive analytics empowers your organization to optimize these functions by flagging who’s most likely to click, buy, lie, die, commit fraud, quit their job, or cancel their subscription – and, beyond predicting people, by also foretelling the most likely outcomes for individual corporate clients and financial instruments. These predictions directly inform the action to take with each individual, e.g., by marketing to those most likely to buy and auditing those most likely to commit fraud.

In their application to these business functions, predictive analytics and machine learning (ML) are synonyms…

This article, which has been excerpted, goes on to cover:

2) Don’t Lead with Software Selection

3) Don’t Leap to the Number Crunching

For access to the entire article, see its original publication in Harvard Business Review.

About the Author

Eric Siegel, Ph.D., founder of the Predictive Analytics World and Deep Learning World conference series and executive editor of The Predictive Analytics Times, makes the how and why of predictive analytics (aka machine learning) understandable and captivating. He is the author of the award-winning book Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, the host of The Dr. Data Show web series, a former Columbia University professor, and a renowned speaker, educator, and leader in the field. Follow him at @predictanalytic.

 

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