Exponential technologies are about to change the way tax professionals work


Posted by Beth Mueller on April 7, 2017

Digital technology is disrupting all manner of knowledge work, and tax is no exception. In tacit admission of this trend, taxpayers and regulatory authorities have been stepping up their efforts to collect and analyze tax data. On the horizon? Machine intelligence—part of a group we call exponential technologies because of its rapidly compounding power.

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What’s in store for government organizations in 2017?

A new route for better outcomes


Posted by Rachel Frey on February 9, 2017

Anyone can create insight. What really counts is the actions government organizations take with it. The best analytics initiatives start with outcomes in mind—outcomes like supporting better engagement, working smarter, increasing efficiency, reducing costs, and bettering lives—and then the mission-critical plan for realizing them. As 2017 kicks off, four exciting trends are emerging in the health and human services arena that can make use of data to produce cost-containing outcomes more than ever:

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A smartphone-enabled journey toward a healthier future


Posted by Preeta Banerjee on January 06, 2017

Most of us love traveling. What about a journey inside the human body? Movies such as “Fantastic Voyage,” where shrinking to microscopic size allows humans to enter a patient’s body to save his life, may still be in the realm of science fiction, but to some extent, the vision has been realized via modern endoscopic techniques. Technology to monitor the body from inside and to deliver medicine to the exact spot of the disease or injury might change the way illnesses are prevented and cured in future.

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Moving to a precision medicine model in pharma


Posted by Nitin Mittal on October 10, 2016

Pharmaceutical companies have operated the same way for decades. Conduct R&D, run clinical trials, endure the regulatory gauntlet, and swing for a home run. This blockbuster-driven business model has produced miracle drugs and flops, winners and losers. And now, it’s going away.

Instead of investing in drugs whose success hinges on risky bets with their inevitable boom or bust cycles, pharma companies are pivoting to a precision medicine-based model–developing drugs to impact a specific patient’s malady or condition.

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Can we all agree to avoid these “I wish I would haves” about cognitive computing?


Posted by Paul Roma, on October 10, 2016

Cognitive computing is already huge, and it’s likely only going to get bigger. And so far I’ve already observed a few seriously risky views on cognitive computing. Why are they risky? Because if they take hold, they’re likely to lead many to say “I wish I would have” in the not-so-distant future. In this case, the implications of getting it wrong, or simply not getting on board fast enough, are serious. Don’t let yourself get caught saying these things a year from now.

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Finding critical customer complaints when lives depend on it

Posted by Greg Szwartz, on October 7, 2016

Sometimes, being able to quickly separate critical customer complaints from others is a matter of life and death. Welcome to the daily challenge of the medical device industry.

The volume of complaints can be crushing, especially for a high-profile product. Which are reportable to the FDA? Setting aside the issue of reporting, which represent opportunities for safety and quality improvements? For instance, a patient may complain about something that’s not a safety issue–a broken shipping box. Another client may raise concerns about something far more serious. From an analytics perspective, it can be difficult to distinguish between the two, especially in the face of a large volume of complaints, coming from virtually anywhere in the world, in any language, from any customer or third party.

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The era of the billion-dollar data lake is over as quickly as it started


Posted by Paul Roma, on October 7, 2016

Well, that was fast.

Remember all those massive, mega-billion dollar data lakes we all kept hearing about over the past few years? With the exception of the US government, we’ll probably never see their likes again. Many of the large organizations that were pursuing those data lakes (not to mention countless smaller ones) have largely changed course. Why? The answer is actually not so surprising, even if this particular outcome is.

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