Research

Digital Systems Maturity Model

By Geoffrey Moore, Aug 17, 2017

Every so often a phrase emerges from the Word Cloud to achieve capital importance, the sort of thing that authors and pundits can dine out on years to come (well, we do have to eat too, you know). At present that phrase is digital transformation.

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When it comes to creating a more data-and-analytics-driven workforce, many companies make the mistake of conflating analytics training with data adoption. While training is indeed critical, having an adoption plan in place is even more essential.

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The analysis of Internet of Things (IoT) data is quickly becoming a mainstream activity. For this blog, I’m going to focus on a few unique challenges that you’ll most likely encounter as you move to take IoT data into the AoT realm.

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Thanks to data analytics and machine learning, we are now discovering that the exact words teachers use to give students feedback is among several factors that directly influence whether a student succeeds or fails academically. And furthermore, whether she stays in school or drops out.

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The ever-increasing role of technology in the modern marketplace has made transactions quicker and more convenient than ever for both businesses and consumers. Unfortunately, it’s also invited a greater risk of payment fraud and other cybercrime. Fraudsters have access to a variety of sophisticated attacks that can cause tremendous harm in a very short period of time – a reality that requires advanced tools capable of rapidly predicting, detecting and responding to suspicious activity and adapting to a constantly evolving digital landscape. Perhaps no such tool is more powerful than machine learning, and businesses are increasingly turning to this technology to guard themselves against cyberattacks.

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Talent Analytics uses data gathered from our own proprietary talent assessments as an input variable to predict hiring success – pre-hire. We treat this dataset just like any other dataset in our predictive work. We are careful to analyze it for a strong (or weak) correlation to actual job performance. Our theory? If there is no correlation between data gathered via this method our clients should stop using it. Continuing without proof of success would be a little like a doctor “knowing” a certain medication doesn’t work for you, but continues to encourage their patients to keep using the medication. Malpractice at the very least.

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Are Analytics Truly Self-Service?

By Thomas H. Davenport, Jul 25, 2017

I have been thinking about some of the changes over the last decade in analytics, coinciding with the revised and updated release of my book with Jeanne Harris, Competing on Analytics. The book is ten years old, and much has changed in the world of analytics in the meantime. In updating the book (and in a previous blog post about the updates), we focused on such changes as big data, machine learning, streaming analytics, embedded analytics, and so forth. But some commenters have pointed out that one change that’s just as important is the move to self-service analytics.

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Is AI over-hyped in 2017?

By Joanne Chen, Jul 20, 2017

Over the next ten years, I don’t believe AI is overhyped. However, in 2017, will all our jobs be automated away by bots? Unlikely. I believe the technology has incredible potential and will permeate across all aspects of our lives. But today, my sense is that many people don’t understand what the state of AI is, and thus contribute to hype. So what can AI do today?

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Saving Retail

By Geoffrey Moore, Jul 18, 2017

Okay, so you know a sector is in trouble when there is a Web page in Wikipedia entitled “The Retail Apocalypse.” This post is not about how much trouble retail is in. This one is about how it can get out.

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Artificial intelligence has quickly become one of the hottest topics in analytics. For all the power and promise, however, the opacity of AI models threatens to limit AI’s impact in the short term. The difficulty of explaining how an AI process gets to an answer has been a topic of much discussion. In fact, it came up in several talks in June at the O’Reilly Artificial Intelligence Conference in New York. There are a couple of angles from which the lack of explainability matters, some where it doesn’t matter, and also some work being done to address the issue.

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