Making Analytics Elementary with Watson

IBM recently announced that Watson, the groundbreaking technology that most of us first saw outsmarting humans on the television game show Jeopardy! in 2011 (the journey to which was chronicled in the excellent Stephen Baker book Final Jeopardy: Man vs. Machine and the Quest to Know Everything) is now available as a service to help humans get smarter about analytics.

As Alex Konrad reported, Watson Analytics “allows companies to upload data to the IBM cloud for free and then receive what the company claims will be predictive insights for businesspeople to use. If successful, Watson Analytics should work for companies that don’t have a data scientist. IBM will put up sample data on its Watson Analytics site to help users get started, along with tutorials and guides, and plans to update the tools continuously. Companies with security concerns will have IBM’s full cloud security support, but can also take their data down from the site after running it.”

“Watson’s strength over other applications,” Larry Dignan reported, “is that it cuts down on data preparation and loading, which can take 60 percent of the time involved with an analysis project, and deliver relevant information without questioning. There’s even a ’fix it’ button to address data quality issues and repair them as well as various sharing tools. Once data is dumped into Watson Analytics, the system looks at key items such as sales, location, product, and audience and then serves up a visualization best suited to the information.”

“In the era of big data,” Steve Lohr reported, companies of all sizes are looking “to bring modern data analysis and prediction to the rank and file of business. 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.”

Watson Analytics is designed to empower non-technical professionals to make more informed business decisions by analyzing big data using familiar business terms instead of technical jargon. Natural language processing enables business users to interact with big data in a natural way, making advanced and predictive analytics easier. Watson Analytics seeks to accelerate users’ ability to get the answers they’re seeking, quickly and on their own, generating results in terms familiar to their business.

I am cautiously optimistic about Watson Analytics since one of the biggest challenges in deriving business insights from big data is data science. Not only is mathematics, the native language of data science, a difficult second language for business users to become conversationally fluent in, but other languages foreign to business users are often also required (e.g., programming languages such as R and Python), creating the skills gap that makes data scientists high-priced hires—and often too pricey for small to midsize businesses. With its natural language interface and automated analytics, Watson Analytics allows business users to play a data scientist on the cloud—and its freemium model makes it an affordable option for small to midsize businesses.

Although Watson was named after IBM’s first CEO Thomas J. Watson and not Dr. John H. Watson of the Sherlock Holmes stories by Sir Arthur Conan Doyle, with this new cognitive computing service, IBM is trying to make analytics elementary with Watson.

This post was brought to you by IBM for Midsize Business and opinions are my own. To read more on this topic, visit IBM’s Midsize Insider. Dedicated to providing businesses with expertise, solutions and tools that are specific to small and midsized companies, the Midsize Business program provides businesses with the materials and knowledge they need to become engines of a smarter planet.


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