As technology advances, digital analytics have become more of a necessity to be competitive in almost any market. This in turn has caused more demand on the type, quality and sophistication of the information that can be obtained and utilized. Historically, digital analytics have reliably provided descriptive analysis to users. In more recent years, the addition of predictive analytics have been a great asset to many companies. Now, in 2016, prescriptive analytics have emerged as the next popular trend in analytics. That being said, many people still ask, “Is there really a difference between descriptive, predictive and prescriptive analytics?” And, “Don’t descriptive analytics automatically lead to predictions which facilitate ‘prescriptions’ of behavior?” Well, yes and no. Prescriptive analytics do require the information obtained by both descriptive and predictive analytics, but it takes things a step further than either of its predecessors ever could. In order for this to all make sense it’s best to stick with the basics. Below are definitions of the three phases of analytics:
Descriptive: This phase of analytics provides historical data in order for a human Analyst to discover trends and/or patterns of past behavior. Platforms such as the basic Google Analytics dashboard provide an abundance of descriptive analytics ready for dissection.
Predictive: In addition to historical data, predictive analytics automates predictions of future behavior which are based on the historical/descriptive data. Predictive analytics essentially accomplishes a large portion of the trend projection process that used to only be done by Analysts.
Prescriptive: After obtaining descriptions and subsequent predictions for data, prescriptive analytics goes on to prescribe the appropriate actions an organization should take in order to achieve its goals. A completely automated process, prescriptive combines the predicted future with the expressed goals of the organization to create the best game plan. Furthermore, it can make these suggestions in real-time; continually adjusting its recommendations based on the result of the actions it already prescribed. The highly complex algorithms that make up prescriptive analytics take the guessing game out of how to attend to predictive data. For example, instead of an Executive getting bogged down with an abundance of data and having to rely on historical trends or instinct to take action, prescriptive analytics provides a far more advanced method of organizing the data and providing answers based on mathematical probabilities.
The concept of prescriptive analytics is nothing new, but due to recent advances in technology it has now become a feasible reality. In the near future, prescriptive analytics will become common place for a large variety of online business applications. Its benefits are already of great interest to the fields of information security and ecommerce. Imagine being able to predict and know what course of action to take against a cyber terrorist before they even initiate their attack.
In ecommerce, prescriptive analytics is already being used by some large companies. For instance, when you purchase something from Amazon it doesn’t merely provide you a list of items “also frequently purchased” with the item you are buying. Instead, it utilizes the data it has on you, the unique individual, to recommend products that you are likely to buy at that particular time. Amazon is so confident in its prescriptive abilities, it claims that in the future it will be able to automatically purchase and ship items to customers before they have done so for themselves. Scary thought, isn’t it? However, if you think of this process with regard to routinely bought household items such as paper towel, it isn’t hard to imagine that Amazon, without much difficulty, could assess from your purchasing behavior that every 2 months you need more paper towel. Amazon can then charge it to your account and ship it to you without you ever having log in.
Beyond automatic ordering, prescriptive analytics is thought to be essential in providing truly customized user experiences for all types of websites and applications. Due to this, as it becomes more widely used, it is thought to become the great equalizer between small and large companies. It won’t just be Amazon which knows what you want, it will also be your favorite boutiques online or down the street. Digital data will be collected both online and off. It will be collected by your “smart” washing machine, which will remind you it’s time to wash your whites, to your dog’s collar, which will detect deer ticks and suggest you hike at another specified alternative location.
Needless to say, it appears prescriptive analytics will likely be common place in the not too distant future. Our desire to “get it right” with as little effort as possible makes this type of artificial intelligence highly attractive. Of course, it isn’t fool-proof. Just like humans, prescriptive analytics is subject to insufficient data and unpredictable future events. And, one might ask if having decisions made for you isn’t in equal parts liberating as it is annoying. For instance, how many times will you get that paper towel order even when you’re not ready for it? That being said, the competitive advantage prescriptive analytics provides to businesses dictates a prescription for it to be quite prevalent in everyday life in the near future. But, don’t take my word for it, I’m only human.