[Note from the editor: This is the third in our series of BI-focused blog posts. Thanks again to Bob Johansen for sharing his expertise as the head of the Deltek BI Affinity group. If you want to learn more about Vision Performance Management, please register for a live demonstration/Q&A or register for this whitepaper.]
Predictive analytics is about looking into the future with some certainty. It is a crystal ball of sorts. At its core, it is the point where math, magic sprinkles, and a data source come together. When someone first hears about a program that can reliably predict the outcome of some situation, based on a pool of random, seemingly unrelated data, they are amazed, confused, and skeptical. This is all perfectly normal. People are naturally skeptical of things that are beyond their comprehension. Arthur C. Clarke is often quoted for saying, “Any sufficiently advanced technology is indistinguishable from magic.” If Arthur is correct, predictive analytics are magic.
The magic of predictive analytics is that we can see into the future and know what should happen. When applied to technology, predictive analytics tells us is that technology is converging. As explained by Daniel Burrus in his book Flash Foresight, convergence is about taking separate things and bringing them together in a whole new integrated and unified way. One example of convergence is the smart phone that everyone now carries in their pocket. The device is more than a phone. It is also a GPS, calculator, email client, text client, flashlight, music player, camera, and much more. Things are converging. It is the natural way, the logical progression. As technology converges unrelated things get integrated with one another. Silos disappear and data from multiple sources flows together creating new and interesting views that we were never able to see before.
One interesting area that is developing out of several disparate data sources is called “Personal Quantification.” Data scientists are now able to quantify not only what we do and what our personal preferences are, but who we are. “How can they do that?” you ask. Let’s take a look. What if data sources from your cell carrier, the supermarket you frequent (and use your little card to get the discounts on the products you buy), and your credit card issuer could coalesce into one giant database? The data would show where you go, how much you talk and to whom, what products you buy, and how often you buy them (which means it will likely show how many people in your household, how old your kids are, how healthy your diet is, if you smoke, drink, etc.), and what you spend your money on. With that quantity and quality of data, predictive analytics would predict how likely you are to visit the hospital in the future, your annual salary range, your age, your access to a computer, the kind of car you drive, and, of course, how likely you would be to buy a certain product or service. This data set, in combination of other data sets such as Equifax, Facebook, Twitter, Instagram/Flikr, and your cable provider, and your entire life would be laid out like a book to be read. Make no mistake. It is only a matter of time before the data that exists in all of these data siloes and federated into one. The day that happens, the data scientists will rule the world.
Do you think that the trend toward personal quantification is a good thing or a bad thing? Please let me know in the comments.