By now, most people are familiar with the concept of artificial intelligence and even with some of its sub-categories such as machine learning or predictive analytics. Thanks to a number of very clever science fiction writers, however, it is sometimes difficult to determine what the reality of AI actually is, versus the fantastical creations imagined by writers. Here is an overview of predictive analytics; what it is, how it works and why it is important.
What is Predictive Analytics?
Predictive analytics is simply the science of predicting future behaviors based on complex algorithms that factor in various data sets measuring past performance. Predictive analytics is nothing new and mankind has been analyzing past data to predict future trends for as long as it has been collecting data in the first place. AI-powered predictive analytics, however, is capable of elevating previous methodologies to a whole new level based on the sheer amount of raw data it is capable of factoring into its analytical process.
How Does Predictive Analytics Work?
Consumer credit scores are one example of predictive analytics that has been around for some time. Consumer credit scores pull historical data from a range of sources and then give each factor a certain weight. For instance, a consumer that carries very little debt but misses a payment or two here or there may still have a much higher credit score than a consumer that carries a great deal of debt but pays their bills on time every month. A consumer with $100,000 in student loans can still have a higher credit score than another consumer that regularly carries $5,000 or more in credit card debt. Even personal behaviors such as a civil court case or divorce can have an impact on your credit score as well.
As complicated as credit scores might seem to be, however, they still derive a score based on a fairly narrow subset of data. In other words, they really only factor information from seven or eight different categories and those categories are the same for everyone. More recently, credit monitoring companies have started adding a wider range of data sets such as utility bills and banking habits. The time may not be far off, however, when credit scores will be powered by even more complicated algorithms that factor in a wider range of data sets than ever before. They may even be weighted by external factors such as the economic climate in the zip code where you live or even your driving habits. As smart technology begins delivering more and more detailed data on almost every person on the planet, predictive analytics may also become more eerily accurate than ever before.
The Future Of Predictive Analytics
The potential applications for predictive analytics are almost limitless. Currently, retailers use historical data to determine how much of any given product they might need for the holiday season. As every business on the planet knows, however, the vagaries of supply and demand can be treacherous. Products often have to be ordered months in advance to arrive in time for the holiday season and any number of factors can delay them. If retailers don't order enough of a certain product, they may lose significant business to their competitors but if they order too much of a certain product they may end up having to sell it at a loss - if they are able to sell it at all.
Similarly, product manufacturers have to ensure they have enough raw materials on hand to produce the right amount of product and the mines and other operations that produce the raw materials have to produce enough raw materials to meet the demands of manufacturers to produce all of the goods that retailers order. On top of that, all of the shipping and delivery companies that deliver goods from one point to another have to run smoothly in order to keep the entire operation running smoothly. A breakdown at any point in the entire global operation can cause economic catastrophe. In many cases, massive breakdowns occur as the result of small anomalies that slide right under everyone's radar.
Artificial Intelligence, however, is capable of monitoring every truck and every ship carrying every package and shipment of cargo. It can monitor exactly when goods arrive and which factories are operating at full capacity and which are operating at a reduced capacity. It can even track when necessary parts or services might be available to get production running up to speed again. Predictive analytics can then recalculate new delivery dates based on current production, which then allows other AI-powered systems to reroute delivery trucks and cargo vessels to transport goods that are predicted to be ready ahead of schedule. Predictive analytics can even factor in geographical events such as hurricanes or strikes in one region that may have a direct impact on production or distribution in another.
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