Research

Optimization of Inventory Allocation

By Dr. Chris Holloman, Leigh Helsel, Tayler Blake, Aug 23, 2017

Available to Research & Advisory Network Clients Only

In 2016, U.S. e-commerce sales totaled an estimated $394.9 billion, accounting for 8.1 percent of total annual sales. This total was a 15 percent increase from 2015. Advances in technology and adoption of the internet have forced the retail industry to make dramatic shifts toward e-commerce. While this change presents a tremendous opportunity for business growth, the cost associated with inefficiencies in supply chains makes optimally allocating inventory to fulfillment centers integral to retailers’ success. In this research brief, we describe a method to determine the best allocation of inventory to fulfillment centers after a total buy has been determined.

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Inquiry Response: Tips for Linking Retail Outlet Sales Back to Digital Marketing Efforts

By Greg Bonsib, Aug 21, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

A large part of our business is in consumer packaged goods sold through mass-channel outlets such as Wal-Mart. We’d like some insights into how we can use analytics to help us understand the marketing-driven revenue on the retail end. Is there a way we can link POS revenue back to our digital marketing efforts?

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Five Big Data Analytics Pitfalls to Be Aware of (And Avoid!)

By Bill Franks, Jun 28, 2017

Available to Research & Advisory Network Clients Only

Many people think that in the age of big data, we always have more than enough information to build robust analytics for almost every situation. Unfortunately, this isn’t the case. In fact, there are situations where even massive amounts of data still don’t enable basic predictions to be made with confidence. In many cases, there isn’t much that can be done other than to recognize the facts and stick to basic analytics instead of getting fancy. However, it is critical to recognize the situation before expending a lot of effort in a wasteful attempt to get predictive analytics to work in a situation where success isn’t in the cards. This challenge of big data that can’t be used to predict seems like an impossible paradox at first, but, as you’ll see, it is not.

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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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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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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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Video: What is “Analytics” to a Digital Native?

By IIA Faculty, Apr 13, 2017

Available to Research & Advisory Network Clients Only

2017 Analytics Symposium - Silicon Valley

Leaders from Bay Area “digital natives” share perspectives on the inner workings of companies founded on data, and the symbiotic relationship of analytics and technology in data-driven companies. Panel featuring David Hardtke, Director of Advertising Science, Pandora; Todd Holloway, Director of Content Science, Netflix; David Andre, CEO, Cerebellum Capital; Matt Fernandez, CEO, Simple Emotion

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