Research & Insights

Inquiry Response: Tips for Building Marketing Mixed Models

By IIA Expert, May 13, 2019

Available to Research & Advisory Network Clients Only

Inquiry:

After using an outside vendor to build our marketing mixed models, we’re going in-house to leverage our particular business expertise to improve the models. What should we be thinking about given that we use a Bayesian hierarchical time series model and we want to understand the impact of marketing spend at our stores nationwide?

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The Questionable Analytics of Censorship

By Bill Franks, May 09, 2019

Historically, concerns about over-zealous censorship have focused on repressive governments. In recent times, however, a new path to censorship has arisen in the form of search engine and social media companies that are building analytically-based censorship algorithms. This post outlines why using analytics for centralized censorship is a steep and slippery slope and also lay out an alternative that will enable those same censorship analytics to provide people with a choice rather than a dictate.

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Analytics Fluency – How Optum Is Boosting Six Critical Competencies

By Alex Barclay, May 08, 2019

Available to Research & Advisory Network Clients Only

Optum has launched a number of initiatives to boost analytics fluency, especially among its business leaders and team members. The goal is to equip individuals in business units, operations and other key parts of Optum with the knowledge and skills needed to effectively engage, employ and capitalize on analytics. While our efforts are a work in progress, we view analytics fluency as a critical prerequisite to “competing on analytics” and key to our mission of transforming health care. The next sections provide an overview of Optum and the challenges we’re addressing in health care, while subsequent sections describe the motivation for and our experience with fluency-building initiatives to date.

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GE’s Path to Emerging Analytics Technologies

By Mano Mannoochahr, May 01, 2019

Available to Research & Advisory Network Clients Only

GE aspires to be an algorithmic business, but recognizes this transition will not occur overnight. It will occur in stages as the company develops new capabilities and implements multiple emerging technologies. This transition requires building solid foundational systems and encouraging broad experimentation and innovation using new analytics technologies.

Beyond getting experience with next-generation technologies, transitioning to an algorithmic business requires cultivating an enterprise-wide data culture and changing how people work throughout the company, particularly on the front line.

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Artificial Intelligence – A Primer On Several Common Approaches

By Bill Franks, Apr 24, 2019

Available to Research & Advisory Network Clients Only

There is a lot of well-deserved hype for artificial intelligence algorithms and for deep learning in specific. Self-driving vehicles are already being tested and rolled out into our communities. So, the future is here. The way the cars are enabled is partly through using convolutional neural networks to do object detection. There are certainly many other algorithms that are part of the self-driving process, but a lot of the key algorithms that enabled us to get to where we are today are the convolutional neural networks that are explained in this research brief.

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Inquiry Response: Agile For Analytics

By IIA Expert, Apr 22, 2019

Available to Research & Advisory Network Clients Only

Inquiry:

We use an Agile-like methodology for analytics projects and are always looking for ways to improve our execution and speed. Do you have any advice?

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Portland 2019 Analytics Symposium Video: Zachery Anderson

By Zachery Anderson, Apr 17, 2019

Available to Research & Advisory Network Clients Only

It’s No Game to Find and Keep Your Data Scientists - EA Battles The Market Forces for Talent

In 2013/14, EA’s voluntary turnover among data scientists was 21-22%. It is now 8%, with consistent improvements. These improvements occurred without major changes in compensation and without disproportionate change in investment in the analytics platform, which are common data scientist complaints.

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Portland 2019 Analytics Symposium Video: Michael Li

By Michael Li, Apr 17, 2019

Available to Research & Advisory Network Clients Only

Employment and Training in The Era of AI

As AI replaces some jobs and changes others, it raises questions of, “What is the role for humans in the AI world?” It is most useful to see humans and AI working together, taking advantage of the strengths of each.

The training and learning tracks will vary by role. Foundational learning will be required in all technical roles including basic software engineering, data wrangling, predictive analytics, and data visualization. Data scientists will require additional training in advanced machine learning; data engineers will require more immersion in distributed computing.

The demand for data scientists and analysts is estimated at 140,000 to 190,000. But the demand for data-savvy managers is even greater at 1.5 million. It is unlikely universities will be able to meet this demand. Universities tend to be more theoretical and less focused on practical application. Private training will be needed to fill the gaps.

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Portland 2019 Analytics Symposium Video: Michael Hoffman

By Michael Hoffman, Apr 17, 2019

Available to Research & Advisory Network Clients Only

Mixed Reality and Analytics

Mixed reality (XR) technology is providing quantifiable business value through multiple features and benefits, which include shared 3D context, spatial mapping, data visualization, and much more. Companies are deploying XR across multiple uses cases—and many of these use cases require the use of analytics to analyze and gain insights from massive amounts of information.

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Portland 2019 Analytics Symposium Video: Melanie Mitchell

By Melanie Mitchell, Apr 17, 2019

Available to Research & Advisory Network Clients Only

AI Hits The Barrier of Meaning

Hype about AI is not new. In 1965 experts predicted that by 1985, AI would do anything humans could. Today, many are optimistic about AI while others want to put the brakes on. So how close are we to human-level AI?

Today, the most common form of AI is deep neural networks, which can do impressive things like object detection and tracking. Classification errors have gone down and are now only 3%, while detection has improved. Combining vision with language enables systems to identify a picture and generate a caption, often with impressive results. Speech recognition and translation have improved as has the ability of machines to answer questions. Machines have shown improved reading comprehension and the ability to play video games. This progress is why some feel AI is closer to human-level intelligence.

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