Research

While many companies are investing in online and other digital tools, most have not developed a comprehensive omnichannel strategy to deliver great customer experiences over multiple customer touch points. Legacy marketing mindsets, just like legacy IT mindsets undermine the ability of companies to explore and experiment with all whole new range of digitally enabled customer engagement tools.

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Any new tool or technology has the potential to be put to use for good purposes or, unfortunately, for harmful purposes. Artificial intelligence is no different. As we see the rapid progress occurring in the AI space, lots of attention has been paid to all of the good uses of AI. However, it is inevitable that those with nefarious intent are also studying AI successes with an eye toward how to twist them into tools to pursue their less than honorable goals.

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Augmenting Self-Driving Cars with Human Capabilities

By Thomas H. Davenport, Jan 09, 2018

In a previous piece I wrote about an MIT conference suggesting that fully autonomous vehicles are not just around the corner. In the short run, then, we’ll be riding in increasingly smart vehicles, but we humans will still be expected to be in charge. And even after full autonomy is available, there may well be some human role in the process beyond catching some Zs behind the wheel or watching videos on a mobile screen.

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Virtual reality is mainly a form of entertainment that provides immersive experiences such as sitting courtside at an NBA game, shooting aliens with your hands, starring in a horror movie and more. However, there has also been a lot of speculation about the potential applications of VR in the big data world. In industries where immersion is required, such as remote surgeries, trainings across industries such as auto repair and military, virtual reality provides major value. Will there be any applications for virtual reality in data analytics and visualization though?

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AI Standing On the Shoulders of Giants

By Kathryn Hume, Dec 21, 2017

My dear friend and colleague Steve Irvine and I represented our company integrate.ai at the ElevateToronto Festival in September. The organizers of a panel I was on asked us to prepare comments about what makes an “AI-First Organization.”

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Increasing the market value and operating performance of your company in the new digital world requires that you harness data analytics as a major contributor to your competitive success.

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The Post-Algorithmic Era Has Arrived

By Bill Franks, Dec 14, 2017

Last week, IIA hosted our annual Predictions and Priorities webinar, as well as the associated research brief. When we sat down to determine what we should focus on this year, Tom Davenport and I both immediately raised a trend that we’ve recently been discussing with organizations. After reconciling our semantics, we realized that we were both excited about the same base trend. I want to reiterate it here as I think it is a critical trend to understand and adapt to. Namely, “the post-algorithmic era has arrived”.

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What We Talk About When We Talk About AI

By Thomas H. Davenport, Dec 12, 2017

What do we call the collection of technologies that make up what we used to call “artificial intelligence?” This conundrum reminds me of a Raymond Carver short story (and book) called What We Talk About When We Talk About Love. Artificial Intelligence (AI) isn’t quite as ambiguous a concept as love, but it’s moving in that direction.

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Numbers don’t lie, and they don’t judge. They don’t care if you’re a woman, or where you grew up, or if you speak with an accent. These are some of the thoughts I’ve had after speaking at the Grace Hopper Celebration of Women in Computing Conference and the Women in Statistics and Data Science Conference. After my presentations, I met with diverse groups of young women who are entering fields rooted in math and science. They wanted to know how I got here, what kind of training I’ve had, how I got on the management track, and how I came to lead a data analytics team at Microsoft.

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Data-driven decision-making: who doesn’t think it is a good idea? But it typically has a rough go in the real world of enterprise management, in part because the data itself often proves unreliable. For much of my business life IT has been tasked with building systems that could represent a single source of the truth. Unfortunately, that quest proved to be right up there with the holy grail and the fountain of youth—at best, aspirational, at worst, delusional. Today we have an opportunity to make a great leap forward, however, because for the first time in history we have broad access to high-volume data from a variety of sources that, when matched against each other, dramatically increase the probability of something like truth, and do so in a time window that is actionable. Part 3 of the blog series.

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