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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2018 Analytics Predictions and Priorities

By Bill Franks, Thomas H. Davenport, Robert Morison, Dec 07, 2017

Available to Research & Advisory Network Clients Only

Each year, the International Institute for Analytics takes time to focus on the latest analytics trends and the most pressing analytics challenges currently facing organizations. We gather the basis for our predictions from our day-to-day work supporting and advising analytics leaders and programs. Our insights arise from the breadth of expertise and cross-industry perspectives we receive every day from our clients, partners, and members of the IIA expert network. This is our 8th annual look forward into the upcoming year.

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Why Blockchain And Analytics Don’t Mix Well

By Bill Franks, Nov 09, 2017

The concept of a blockchain is quite a phenomenon in recent times. It has quickly risen from a relatively obscure idea known mostly within some small circles to one that is being discussed as having potential to literally change some of the fundamentals of the world’s economic systems.

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The breadth of analytics has certainly increased in recent years. So, too, has the pool of people who dip their toe into creating analytics of one sort or the other. The trends toward democratization of data and self-service analytical capabilities are powerful and both have driven a lot of value for organizations in recent years. At the same time, it is possible to go too far.

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Yes, Artificial Intelligence Is Analytics

By Bill Franks, Sep 14, 2017

There seems to be some confusion as to exactly what artificial intelligence (AI) is, and how the discipline of AI should be categorized. Is AI a form of analytics or is it a totally new discipline that is distinct from analytics? I firmly believe that AI is more closely related to predictive analytics and data science than to any other discipline. One might even argue that AI is the next generation of predictive analytics. Additionally, AI is often utilized in situations where it is necessary to operationalize the analytics process. So, in that sense, AI is also often pushing the envelope of prescriptive, operationalized analytics. It would be a mistake to say that AI is not a form of analytics.

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O’Reilly AI Conference NYC 2017: Developments in Artificial Intelligence

By Bill Franks, David Alles, Aug 16, 2017

Available to Research & Advisory Network Clients Only

As we reported in Strata Hadoop World 2017 – Big Data and Analytics Developments from the Heart of Silicon Valley, O’Reilly’s Strata Conference already has a heavy focus on machine learning and AI. What makes O’Reilly AI unique, versus Strata, is its exclusive focus on AI and the inclusion of more cutting-edge AI research topics that have huge potential, but are further from commercialization. The objective for this report is to summarize the common themes and key trends emphasized at O’Reilly AI into an easy-to-read guide that can serve as both a general reference and a resource for planning AI initiatives. With this in mind, the report is organized into seven sections.

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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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Learnings from O’Reilly AI Conference

By David Alles, Bill Franks, Jul 25, 2017

Available to Research & Advisory Network Clients Only

From theoretical use to real-world implementations, Artificial Intelligence is taking a more prominent place in analytics. Last month, IIA’s VP, David Alles, and CAO, Bill Franks attended the O’Reilly AI Conference, and returned with an array of emerging best practices and insights.

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5 Things New Analytics Leaders Should Do to Succeed

By Bill Franks, Thomas H. Davenport, Jul 19, 2017

Available to Research & Advisory Network Clients Only

There is a fair amount of management research suggesting that the first 90 days or so are the most important time of a leader’s tenure. It’s when you establish your reputation and it determines what people start to think about you in your role. It’s often hard to change those first impressions. Therefore, IIA held a webinar to discuss this very important period for senior analytics leaders like a Chief Analytics Officer, Chief Data Officer, VP of analytics, or similar senior role. This paper captures the key elements of the discussion between Bill Franks and Tom Davenport, which focused on five essential things new analytics leaders should do to set themselves up for success.

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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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