Fast Company had a great article on Predictive analytics. Several years ago, I coined the term “PredictiveIT” For the purposes of discussion, they are one in the same. Fast company says:
Want to make really smart decisions for your company? It’s simple as looking into the future and assessing the data–a service that a few young companies dealing in “predictive analytics” are selling
Business swims in a sea of data. No matter what line of work you’re in–publishing, healthcare, retail, food service, drug-dealing–your business operates in a vast, swirling matrix of information. How many people are interested in your product? When do they show most interest? How much of your product should you produce? Where, when, and how should you sell it?
Data crunching giants like IBM and SAP have long mined and analyzed data for businesses, offering predictions about what actions will help their bottom line–“predictive analytics,” to use the industry buzzword. But the cost of such services has typically been so high–a price tag in the millions, if consulting services are added, is not altogether unheard of–as to limit their use to major corporations. Now, for the first time, a few smaller players are promising small and medium-sized businesses that they, too, can use predictive analytics to better forecast what actions will yield a greater profit.
Take, for instance, web publishing. Web publishers have traditionally been content with “real-time analytics”–that is, information about how readers are interacting with their sitenow. But as vast stores of this data accumulate, there’s no reason why publishers can’t demand to know more. They should be able not only to observe the present, but to make guesses about the future.
Example: an editor who thinks a story is strong, and showcases it prominently; the article subsequently gets higher traffic. “It looks like the decision you took was correct,” says Mortensen. “But that’s just a self-fulfilling prophecy,” he says–of coursepromoted stories fare better. “What you don’t know is, was there another piece of content that could have done better?” Would a more substantial article have spurred deeper engagement with your site, prompting a reader to dig deeper into your archive? Visual Revenue makes predictions about how it thinks your content will fare and what sort of return on investment you’ll get out of it, down to the dollar and cent. VR’s own services start at about $1,000 per month, though price varies based on volume.
Web publishing is one thing, but isn’t the messy world outside the Internet too complex to forecast the future? No, cross-industry, in finance, retail, manufacturing, a lot of web 2.0, healthcare, government…” In every sector, companies find that they have “tremendous amounts of data, tremendous value locked in the data, and are currently unable to get it out, either because of complexity or cost.” For years, companies were mostly happy just to be accumulating data for reports, charts, and graphs, which were used to create accountability. But increasingly, companies want more out of their data. But now that so much data is in hand, “people want to optimize for the future, to forecast more accurately.
Services aren’t exactly cheap, running some 40 grand a year. But competitors–folks like IBM, SAP, and Oracle–often charge up to five times as much. (Some companies offer a range of prices, depending on what services are contracted; while some clients might spend thousands of dollars per year, others could spend “millions.”)
The cost-benefit equation certainly looks increasingly favorable. “With the affordability we bring to the table, it’s not just big financial institutions with 20 billion dollars in annual sales who are able to take advantage, the SMBs of the world are now able to have that same kind of competitive advantage that the big titans have.”
By the same token, the big titans of predictive analytics itself–the IBMs and the SAPs of the world–may have to watch their back for the up-and-coming Pentahos and Visual Revenues. Were they able to predict that?