Research

The Manufacturer’s Dilemma

By Geoffrey Moore, Jun 20, 2017

There is a lot of serious talk in America these days about improving the state of our manufacturing sector. Smart products, Internet of things, robotics, predictive maintenance—all great stuff. But none of it addresses the most fundamental challenge facing the sector: how to deal with a demand/supply inversion which has made the customer king.

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Getting Real About Autonomous Cars

By Thomas H. Davenport, Jun 01, 2017

I attended the MIT Disruption Timeline Conference on AI and Machine Learning. There was interesting content on a variety of topics, but a primary focus was on when specific AI capabilities might become generally available. One particular technology addressed was autonomous vehicles. The key question was when 50 percent of vehicles on US roads would be fully autonomous.

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Balancing Analytics Agility and Stability

By Bill Franks, May 11, 2017

There have been many science fiction stories (as well as video games!) that revolve around the tradeoffs between powerful, strong, hard to harm combatants and those that are small, nimble, but easy to harm. Both have their merits and both can be useful in different situations. However, the same profile doesn’t work best in every situation.

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Understanding Power in the Digital Economy

By Geoffrey Moore, May 09, 2017

We are all stakeholders in the economic systems within which we live and work, and the better we can understand their dynamics, the more likely we are to navigate them successfully. For the most developed economies of today, this means understanding the transition from an industrial to a digital economy, and specifically, how economic power is migrating from familiar to unfamiliar sites.

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Move Your Analytics Operation from Artisanal to Autonomous

By Thomas H. Davenport, May 02, 2017

Many organizations today are wondering how to get into machine learning, and what it means for their existing analytics operation. There are many different types of machine learning, and a variety of definitions of the term. I view machine learning as any data-driven approach to explanations, classifications, and predictions that uses automation to construct a model. The computer constructing the model “learns” during the construction process what model best fits the data. Some machine learning models continue to improve their results over time, but most don’t. Machine learning, in other words, is a form of automating your analytics. And it has the potential to make human analysts wildly more productive.

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Strata + Hadoop World 2017: Big Data and Analytics Developments from the Heart of Silicon Valley

By David Alles, Apr 24, 2017

Available to Research & Advisory Network Clients Only

Strata is a large conference covering a diverse set of data, analytics, and business topics. Tuesday (3/14/17) featured morning and afternoon tutorials (22 total with half day and full day sessions) covering a range of topics including: Developing a Modern Data Enterprise, Getting Started with TensorFlow, Architecting a Data Platform, and Determining the Economic Value of Your Data. Wednesday (3/15/17) and Thursday (3/16/17) featured keynote sessions in the morning followed by 45-minute breakout sessions until late in the afternoon. There were up to 17 breakout sessions in each session block and the conference also had an Expo Hall featuring over 150 vendors. Our objective for this report is to summarize the common themes and key trends emphasized at Strata into an easy-to-read guide that can serve as both a general reference and a resource for planning analytics initiatives. With this in mind, the report is organized into the following seven sections.

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As I have discussed in prior blogs, the focus of enterprise computing for most of the 20th century was on deploying Systems of Record, first on mainframes, then minicomputers, then client-server systems. These were and continue to be the transaction processing backbones that drive global commerce. In the first fifteen years of this century, however, we have seen a profound shift in spending emphasis away from Systems of Record, which are now in maintenance mode, and toward Systems of Engagement, the focus being on connecting with customers, partners, and employees in digitally effective ways leveraging the ubiquity of smart phones. That movement has been inside the tornado for some time now such that, while there will be a lot of money spent here over the next ten years, I think it is time to look ahead to the next wave.

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IIA 2017 Spring Symposium Event Summary

By Jack Phillips, Apr 13, 2017

Available to Research & Advisory Network Clients Only

IIA hosted its first client-only Symposium of 2017 on March 14, 2017 at the VMware campus in Palo Alto, CA. Over 100 of IIA’s research clients gathered for the Symposium featuring five keynotes and two panel discussions. Given the location in the heart of Silicon Valley, the theme of the Spring Symposium was innovation, disruption, and the growing role of technology in shaping how analytics and data management are executed inside enterprises today.

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Video: Innovation, Disruption, and Enterprise Analytics

By IIA Faculty, Apr 13, 2017

Available to Research & Advisory Network Clients Only

2017 Analytics Symposium - Silicon Valley

This presentation addresses how enterprises of all sizes can adopt a “start-up mentality” to transform their organizations and the industry. Featuring Geoffrey Moore, Author, Thought Leader.

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Video: Artificial Intelligence is Data Analytics

By IIA Faculty, Apr 13, 2017

Available to Research & Advisory Network Clients Only

2017 Analytics Symposium - Silicon Valley

Recent advances in Artificial Intelligence have little to do with intelligence, and a lot to do with data. Machine Learning, in fact, is little more than an automated form of analytics. Discover the surprising history of these ideas and why—after fifty years—they are suddenly the next big thing. Featuring Jerry Kaplan, Author, Thought Leader.

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