Developing technology is one thing; democratizing it is another. The latter involves finding the innovation that opens the door to widespread adoption with usefulness and usability.
In the personal computer era, it was the spreadsheet that transformed a hobbyists’ plaything into a must-have machine in corporate America. In mobile computing, it was Apple’s iPhone that brought the smartphone to the masses, establishing a new model for the gadgets as both smart and easy to use. In the era of big data, start-ups and big companies are in hot pursuit of a similar breakthrough, a vehicle to bring modern data analysis and prediction to the rank and file of business.
IBM thinks it has an answer, called Watson Analytics, which it announced on Tuesday. The offering is a software service, delivered over the cloud. As its name suggests, Watson Analytics is a result of a collaboration between teams from IBM’s data analysis group and its Watson unit, which has been built into a business in the three years since Watson beat human champions in the question-and-answer game “Jeopardy.”
The combined team of data scientists, developers and designers has been working on the project for a year. Its ambitious goal, according to Alistair Rennie, general manager of IBM Business Analytics, is to take a big step toward “getting analytics in the hands of every business user, which is a real challenge.”
IBM has shown an early working version of Watson Analytics to a handful of customers and industry analysts, letting them try it out, and they are generally impressed. It combines basics of data handling with the Watson technologies of natural-language processing and machine learning. A result, they say, is that a business person, who is not a statistician or data scientist, can type in questions to probe corporate data. Examples: “What high-value customers am I most likely to close sales with in the next 30 days?” and “Which benefits drive employee retention the most?”
Watson Analytics, they say, may respond with answers ranked by probabilities or with suggestions. The suggestions can begin a sort of dialogue, recommendations to improve the data used or add other sources. It can, for example, rate the quality of the data probed by an initial inquiry. More reliable predictions, the Watson software might say, would result from cleaning the data or including other sources, like local weather or traffic flows, for retail sales predictions, for example.
Click a button, and Watson Analytics, will do that. “It does a lot automatically, with behind-the-scenes intelligence,” said Dan Vesset, an analyst at IDC.
The IBM tool, Mr. Vesset said, can reply in text answers in addition to traditional visual representations of data like charts and graphs. The natural-language capability, he noted, goes beyond the visualization offerings by companies like Tableau, Qlik and SAS Institute.
A brisk demonstration running through a selection of cases does not mean the product will be a winner in the broad corporate market. Still, a couple of the veteran technology managers who have tried out Watson Analytics say it could be a significant advance. Richard Wiedenbeck, chief information officer of Ameritas, a life insurance and financial services company in Lincoln, Neb., said that he was “cautiously optimistic” that Watson Analytics could deliver on its promise, based on what he had seen so far.
“It looks like IBM has leapfrogged what others have done,” Mr. Wiedenbeck said. “It feels like the iPhone of analytics to me.”
Aaron Walz is director of the Business Intelligence Competency Center at Purdue, which applies data analysis tools to everything from classroom scheduling to identifying factors correlated with on-time graduation rates. The field of data tools, Mr. Walz said, has “really struggled with adoption,” mainly because the technology has been too difficult for nonexperts to use.
To be won over, Mr. Walz said he would have to see how Watson Analytics works in day-to-day use. But, he added, “it looks quite compelling.”