The 2002 Steven Spielberg’s film ‘Minority Report’ showed to us the world of predictive analytics. The plot of the film tackles an innovative technology that allows Washington, D.C. to exist without a murder for six consecutive years by 2054. The predictive analytics software helps Tom Cruise, chief of the Pre-crime Unit, to identify, arrest, and prosecute would-be killers before they commit the crimes.
The film tech setup was based on a 80-pages ‘2054 Bible’ created in the result of a 3-days long think tank organized by Mr. Spielberg, with participation of the world’s leading urbanists, futurists, architects, computer scientists, and biomedical researchers.
That is why there are tech innovations in ‘Minority Report’ that we have seen evolving (at a different pace of course) in reality during the past decade, such as multi-media interfaces, kinect motion sensing cameras, retina scanners and even facial recognition advertising billboards. Predictive analytics isn’t fiction. Not any more at least.
What is Predictive Analytics?
At present, predictive analytics is applied in multiple domains, such as hospitals, schools, retail, and policing (IBM), as well as credit card, banking and financial services, governments and the public sectors, manufacturers, media and entertainment, insurance companies and sports franchises (SAS).
So what is predictive analytics? Let us see the definitions by industrial leaders:
Gartner explains that predictive analytics describes any “approach to data mining with four attributes:
- An emphasis on prediction (rather than description, classification or clustering)
- Rapid analysis measured in hours or days (rather than the stereotypical months of traditionaldata mining)
- An emphasis on the business relevance of the resulting insights (no ivory tower analyses)
- (increasingly) An emphasis on ease of use, thus making the tools accessibleto business users.”
“IBM predictive analytics can take you from guesswork to prediction by showing you where you are now, and where you can go next. It empowers you to analyze trends, patterns and relationships in your structured and unstructured data, apply those insights to predict future events, and act to achieve your desired outcomes.
Whether you work in marketing, customer service, sales, finance, operations or another area of your business, IBM predictive analytics software puts a wealth of advanced capabilities at your fingertips where you need them—on premises, on cloud or as a hybrid solution”.
What does this mean for us? Predictive models use known results to develop a model that can be used to predict values for different or new data (I know, it sounds a bit overwhelming J). The modeling can result in predictions defining a probability of the target variable (for example, revenue or number of customers) based on estimated significance from a set of input variables.
Predictive Analytics and Forecasting
Predictive analytics, at a first glance, reminds forecasting. Econometric methods should be producing the same results shouldn’t they?
Well, forecasting provides overall aggregate estimates, such as the total number of purchases next quarter. Predictive analytics is a different analysis domain. It produces a predictive score for each customer or other organizational element. For example, forecasting might estimate the total number of waffles to be purchased in a certain region, while predictive analytics tells you which individual customers are likely to buy a waffle. How do you like that?
The knowledge domains behind predictive analytics are namely:
linear algebrabasic statisticslinear and logistic regressiondata mining
market basket analysisdecision treestime-series analysisforecasting machine learning
Bayesian and Monte Carlo Statistics
text analyticssummarizationclassificationprimary components analysis
I hope this gives a hint on complexity of predictive analytics and its uniqueness, as well as differences from classical forecasting.
Predictive Analytics in Our Lives Today
Tech giants like Google, Apple, Facebook or GE are already using predictive analytics, as well as many startups: Evernote (digital workspace), reQall (productivity application), Cue (health tracker) and others.
For example, predictive search acts as personal assistant and not only recognizes the user’s intent, but also anticipates what he or she needs before they actually ask Google for it. As Larry Page stated, when he was still the CEO of Google: “perfect search engine” was something that “understands exactly what you mean and gives you back exactly what you want”. Such transition to contextual, intent-capturing search is fueled by data and predictive analytics.
Google has been through several steps of introducing predictive analytics into search, namely:
- Google first time applied predictive search. At that time the feature was called Google Suggest.
- Google Instant came on the scene, generating look-ahead search results as users type. Google Suggest was rebranded into Google AutoComplete.
- Google Now was launched as an intelligent personal assistant that ‘provides just the right information at just the right time’. It is available within the Google Search mobile application for Android and iOS, as well as the Google Chrome web browser on personal computers. Google Now delivers information about the traffic jams on daily commute, or changes in flight itinerary, or even results of last night’s soccer match on user’s phone, without him or her even asking.
- Knowledge Graph empowered search by anticipating what type of info user was searching for, when he/she searched for example a celebrity name “Tom Cruise”, and generated specific related content alongside standard search results.
- Singularity… Google acquired companies in the sphere of Artificial Intelligence and Robotics, such as UK startup Deep Mind and Boston Dynamics, to focus on machine learning and language processing.
Predictive analytics can amazing services and improve quality of human life. Many early adopters agree on the point they would trade personal information for better-personalized service. But, as James Heskett, EMERITUS at Harvard Business School said in his article about personalised predictive analytics, remember what happened to Tom Cruise in the movie. He was eventually accused on a pre-crime basis of a murder, and had 36 hours to determine whether the charge is accurate and, if not, who framed him.
How important are predictive analytics for our near future? Is predictive analytics a big deal or just a short-living buzzword? Would you like predictive analytics to be applied to you in more and sphere of life? If not, are you going to object it?
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