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1-Page Summary of Predictive Analytics
Overview
The amount of data we produce on a daily basis is truly mind blowing. Every time you like something on Facebook, buy something online or click an ad, you’re producing data that can be used to predict your future behavior. Companies and governments are using predictive analytics (PA), which predicts the future based on past events, to do this. PA allows them to make very accurate predictions about people’s futures. However, there are many ethical questions surrounding this issue because it makes us wonder whether we want our futures predicted by other people in such detail.
This passage describes how PA works, why IBM’s Watson is the greatest leap in artificial intelligence so far, and that PA may be somewhat prejudiced.
Big Idea #1: Predictive analytics can help you lower your risks and make safer decisions.
Every time a company spends money on marketing, they’re taking a risk. They could spend millions of dollars on an expensive campaign and it might not work at all or worse, fail completely. However, if the company uses predictive analytics, they can reduce that risk by predicting what will happen before the campaign even takes place.
Predictive analytics is a field of study that helps us understand people’s behaviors by looking at statistics and human characteristics. It gives us an idea about how specific people will respond to certain situations, such as advertisements.
Prediction is an important part of any business. However, this process has been made easier by the use of algorithms and predictive scoring. These scores don’t tell you what will happen in the future as much as they tell you how likely certain events are to occur.
For example, if you want to know which ad will attract the most people in the United States who are searching for grants and scholarships online, you can narrow it down by supplying more details about your audience. This includes age, gender and email domain.
These scores are useful for companies that want to target certain groups of people with their advertisements and discounts. It’s also helpful for investment firms that need to know which stocks or people to buy.
PA is more dynamic than other predictive models since it’s based on machine learning, which means it can change, grow and adapt based on the kind of data given to it. And its accuracy is better because backtesting takes old data to determine how accurate your results will be.
If you want to predict whether the S&P index will go up or down in a year’s time, then you can test it by using old data from 1990.
Big Idea #2: Making predictions leads to questions of responsibility, morality and prejudice.
As technology gets better at predicting the future, we need to ask ourselves this question: How much do you want to know about what’s going to happen in your life? And more importantly, how many lives would you be willing to ruin by knowing too much about them?
But data mining has raised concerns beyond privacy. It’s not just about knowing the future, but also about how companies will use that information to their advantage.
Target stores use personal analytics to determine who is pregnant. The press found out about this and felt that Target was going too far by advertising maternity goods to the right women. This kind of marketing can leak people’s personal information, which may not be something their friends or family are ready for them to share yet.
There is a potential to do good in crime prevention. A firm used backtesting of old data from Santa Cruz, California, to predict 25% of burglaries accurately. This system can help police identify different “hot spots” that could be patrolled daily.
