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It was an eclectic gathering at the IBM Analytics Vision 2015 conference in Orlando, Florida, where several hundred IBMers, partners, and customers came together to talk about finance, risk, and performance management. The star of the show, however, wasn't the latest in compliance reporting or fraud detection; it was the application of IBM's self-service cognitive analytics tool, Watson Analytics, to everyday finance, sales, and incentive compensation problems.

Changing the rules of engagement in finance and operations

At the conference, I met many enterprises that have started on a journey to transform the culture of finance and operations. Every day, finance professionals make incredibly complex decisions that require a deep understanding of volatile markets, shifting regulatory environments, and expanding risks. But as anyone in the trade will testify, it's getting difficult to evolve beyond bean counting given the plethora of mundane tasks that need to be undertaken. Application of creative ideas has traditionally proved challenging in finance, with technology that remains rooted in the IT-led, schema-bound analytics era.

Early adopters at the conference included enterprises that were managing complex supply chains (manufacturing, retail) extending beyond their walls and into their customers and suppliers. Healthcare was another sector where Watson Analytics appeared to be making significant headway. The key takeaway for me from these customer discussions was not how Watson Analytics brought in new technology to solve age-old problems, but how the analytic goalposts themselves changed as end users directly engaged with an analytics solution. Customers were using Watson Analytics to get started quickly on the cloud (ingesting their own data, mashing it up with organizational data), asking the second- or third-level-of-detail questions that are usually never asked (or disappear in an IT loop), and finally, acting on their newfound knowledge. Organizations that had a significant footprint of IBM Analytics software were also finding Watson Analytics a much better way to engage with data, right from the boardroom to the trenches.

This is a different kind of engagement than IBM has ever had in the past with its end users. Application in the world of finance proves that Watson Analytics will pass the acid test of even the most draconian of IT shops, while having the potential for "viral" adoption in its end-user base. In a crowded market (exploratory analytics) where bigger actually does not mean better, IBM Watson Analytics is surprisingly giving younger vendors a run for their money.


Further reading

How to Justify the Business Case for EPM, IT014-002906 (March 2014)

Fundamentals: Enterprise Performance Management, IT014-002874 (January 2014)

A Practitioner's Guide to Self-Service BI and Analytics, IT0014-002967 (December 2014)


Surya Mukherjee, Senior Analyst, IT – Information Management

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