Profit and Loss (P&L) statements permeate businesses due to the need to track how a business is performing at overall, business unit, and even project-by-project levels. This blog raises a question: why shouldn’t an analytics plan be expected as a part of the business plan for any major initiative just like a P&L is?

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Evaluating the Risk of Analytics Partners for Your Enterprise

By Bill Franks, Jun 28, 2018

Available to Research & Advisory Network Clients Only

How do you choose the right vendor partners to move your analytics program forward? Large organizations today need to partner with vendors to successfully build, deploy, and maintain enterprise-level analytics programs. In today’s world, however, there are so many potential vendor partners that it is hard to know where to begin. An often under-addressed aspect of evaluating which partners to invest in is the need to look at the inherent risk a potential partner brings to the table along with its compelling offerings. This research brief provides a framework for evaluating the risks of potential analytics partners. By developing a consistent and complete approach to assessing risk, an organization will help itself make better investments and, therefore, improve its analytics performance.

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Predictive Analytics World 2018

By Bill Franks, Jun 26, 2018

Available to Research & Advisory Network Clients Only

IIA attended the Predictive Analytics World (PAW) show in Las Vegas, June 6–7, 2018. This year the show had a new format called “Mega PAW” where a number of (previously individual) PAW shows were all housed together under one roof. Some of the keynotes were shared by all attendees, while the breakouts were segmented by topic. All of the sessions IIA attended were in the business track. In this event summary, the key points from each session attended will be provided, as will some commentary on IIA’s thoughts about the topic.

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Why Google Duplex Did Not Pass the Turing Test

By Bill Franks, Jun 14, 2018

Recently, a lot of press was given to Google’s Duplex Artificial Intelligence (AI) bot. The Duplex bot calls businesses, such as a restaurant, on your behalf and makes a reservation. We’re going to save for another day the potential ethical issues raised by AI bots fooling people into believing they are talking to a person. Instead, let’s focus on whether or not Duplex has successfully passed the Turing test as some have suggested.

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Cutting Through the Hype of Artificial Intelligence, Part 1

By Bill Franks, May 30, 2018

Available to Research & Advisory Network Clients Only

Artificial Intelligence (AI) is one of the hottest topics in the analytics space today. Within a two to three year span, AI went from relative obscurity to an extreme level of industry attention and media coverage. As a result, organizations that barely knew how to spell Artificial Intelligence a few years ago are now charging full steam ahead in pursuit of AI initiatives. In many ways, this is a good thing. After all, AI is quite powerful and has the ability to drive tremendous value if applied appropriately. However, this attention also has some negative consequences. Most notably, the topic of AI is so full of hype today that many organizations are struggling to separate what is real and achievable from what is pure hype and wishful thinking. This is the first of a two-part series on Artificial Intelligence. This brief will examine some critical aspects of AI to understand in terms of where the market is a bit over-exuberant and also how AI works in reality. It will also cover some strengths and pitfalls to be aware of.

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As a person who has been involved with analytics for a long time, I have historically considered analytics to be a huge differentiator while data was more of a table-stakes enabler. Several trends have come together to make me realize that the equation has been reversed in many cases today.

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Why Analytics of Things Standards are Needed

By Bill Franks, Apr 12, 2018

As the Internet of Things (IoT) continues to explode, so does the need for the analysis of IoT data. At IIA, we call the analysis of IoT data the Analytics of Things (AoT). There are many success stories from the world of AoT. However, without some attention to standards of both IoT data itself and the analysis of it, organizations will struggle to achieve AoT’s potential. In this post, we’ll dig into several different areas where standardization must be pursued.

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Within a two- to three-year span, Artificial Intelligence (AI) has gone from relative obscurity to an extreme level of industry attention and media coverage. As a result, organizations that barely knew how to spell “Artificial Intelligence” a few years ago are now charging full steam ahead to pursue AI initiatives. A common question that is raised is, “Why is now the time for AI?” After all, there have been bursts of hype around AI multiple times over the last few decades. Is today different? And, if so, why?

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Is Real-Time Analytics A Money Pit?

By Bill Franks, Feb 08, 2018

Certainly, it is important to have analytics available in the timeframe needed for making decisions. For many years, it was too difficult and expensive to execute analytics anywhere near real-time and so everything was done using infrequent batch processes. As processing power has increased exponentially and costs have dropped to unprecedented levels, it is feasible to perform a wide array of enterprise analytics on a near real-time basis. However, many organizations today are vastly over-utilizing real-time analytics and are paying a price for it that, unfortunately, isn’t always recognized.

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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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