Een onderzoek van TDWI heeft onlangs uitgewezen dat de toepassing van voorspellende analyses (predictive analytics) binnen bedrijven nog geen grote vlucht neemt. Slechts 20% van de bedrijven gebruikt deze vorm van business intelligence en 20% is bezig met een implementatie. Dit is een laag percentage gezien het aantal jaren dat er al bedrijven zijn die dit succesvol inzetten om o.a. klantgedrag te kunnen voorspellen. Want weten wanneer een klant naar de concurrent gaat is toch een stuk waardevoller dan weten hoeveel klanten er vorige week zijn overgestapt?

Uit het artikel 'Predictive Analytics: Business Intelligence's Next Step' (29 mei 2006):

Real-time information, once a competitive differentiator that produced more timely and relevant business decisions, is now a commodity. Even midsize companies process transactions as fast as the New York Stock Exchange, while decision makers communicate and collaborate over broadband networks as if they were in the same office. Sheer speed isn't the advantage it once was.

So what's next? What's next is what's next--the ability to forecast where events are heading, then make informed decisions based on that assessment. Predictive analytics, the scientific name for using a data warehouse as a crystal ball, is where business intelligence is going. It involves running historical data through mathematical algorithms--neural networks, decision trees, Bayesian networks--to identify trends and patterns and predict future outcomes. Will product demand surge? Will a patient relapse? Will a customer take his business elsewhere? Our ability to make such educated guesses is key to improving service, cutting costs, and exploiting new market opportunities.

Bron: www.crn.com

In dit artikel staan meerdere interessante cases, waaronder de toepassing van 'predictive analytics' bij de politie van Richmond. Andere cases gaan over de toepassing bij FedEx, de gezondheidszorg en fondsenwerving voor een universiteit.

[...]

In Richmond, Va., police use predictive analysis to determine the probability that a particular type of crime--armed robbery, auto theft, murder--will occur in a specific area at a given time. Police lieutenants who command the city's 12 sectors use desktop computers linked to the system to decide where to deploy a mobile task force of 30 officers. "Based on the predictive models, we deploy them almost every three or four hours," Police Chief Rodney Monroe says.

Officers have arrested 16 fugitives and confiscated 18 guns based on the system's guidance. In the first week of May, Richmond had no homicides, compared with three in the same week last year. Monroe attributes that outcome, in part, to moving officers around based on the calculated probability of shooting incidents. "It's more proactive," Monroe says. "We're not waiting for a homicide to occur."

A 911 call is a real-time event; pre-empting that call involves predictive analysis. Vivek Ranadive, CEO of Tibco Software, the data-integration and middleware company, is convinced a growing number of companies are about to begin applying data forecasting. "I've spent all my life evangelizing 'real time,' but by its nature it's still reactive," Ranadive says. "You really have to get ahead of the curve."

But vendors need to be careful not to overpromise. The Richmond police will never be able to foretell the actions of lawbreakers in the way Tom Cruise's character, Chief John Anderton, did as a member of a "precrime" team in the Steven Spielberg film Minority Report.

Police commanders can query the system about specific crimes, such as determining which neighborhoods are most likely to experience armed robberies or auto thefts. For example, the police have zeroed in on armed robberies in nightclub parking lots near closing time--robbers consider inebriated club-goers to be easy marks, says Colleen McCue, a senior research scientist at RTI.

As more data is added to the system, accuracy should improve, Chief Monroe says. But it has its limitations. The analysis is primarily restricted to time, place, and type of crime, while details such as the type of weapon used in past crimes aren't considered. And the predictive models must be updated with new information, such as increases or decreases in the types of drugs being sold on the streets.

[...]

Waarom wordt 'predictive analytics' toch nog niet overal ingezet? Het eeder genoemde TDWI artikel 'Predictive Analytics: Slow Adoption Despite Big Benefits ' gaat hier dieper op in. Een belangrijk argument is de angst voor het onbekende:

"Predictive analytics is also an arcane set of techniques and technologies that bewilder many business and IT managers," Eckerson points out. "It stirs together statistics, advanced mathematics, and artificial intelligence and adds a heavy dose of data management to create a potent brew that many would rather not drink!"

"[M]ost experts agree that predictive analytics requires great skill—and some go so far as to suggest that there is an artistic and highly creative side to creating models—most would never venture forth without a clear methodology to guide their work," Eckerson explains."

De technieken, middelen en leveranciers zijn er. Het lijkt nu meer een kwestie van investeren in medewerkers met analytische competenties en het zoeken van operationele toepassingen. (zie ook Davenport in zijn boek 'Competing on analytics'). Voor bedrijven die de concurrentie voor zijn met een goede en snelle inzet van 'predictive analytics', kan deze techniek de komende jaren zeker concurrentievoordelen geven.