Research

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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Inquiry Response: Planning to Standardize Metrics Across the Enterprise

By Mark Molau, Dec 04, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

We’re moving to establish standardized metrics across our business units and divisions. What’s important to consider as we begin this transition? How can we socialize what we create and encourage adoption?

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Inquiry Response: Thoughts on Platforms and Recruitment, Looking to the Future

By IIA Faculty, Nov 20, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

What hard packages are being taught in data science programs (SAS, Stata, SPSS, R, Python, etc.)? We’ve seen a shift away from proprietary platforms. How are organizations reacting to attract and retain new talent? What does the future look like in this space?

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Data-driven decision-making: who doesn’t think it is a good idea? But it typically has a rough go in the real world of enterprise management, in part because the data itself often proves unreliable. For much of my business life IT has been tasked with building systems that could represent a single source of the truth. Unfortunately, that quest proved to be right up there with the holy grail and the fountain of youth—at best, aspirational, at worst, delusional. Today we have an opportunity to make a great leap forward, however, because for the first time in history we have broad access to high-volume data from a variety of sources that, when matched against each other, dramatically increase the probability of something like truth, and do so in a time window that is actionable. Part 3 of the blog series.

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Data-driven decision-making: who doesn’t think it is a good idea? But it typically has a rough go in the real world of enterprise management, in part because the data itself often proves unreliable. For much of my business life IT has been tasked with building systems that could represent a single source of the truth. Unfortunately, that quest proved to be right up there with the holy grail and the fountain of youth—at best, aspirational, at worst, delusional. Today we have an opportunity to make a great leap forward, however, because for the first time in history we have broad access to high-volume data from a variety of sources that, when matched against each other, dramatically increase the probability of something like truth, and do so in a time window that is actionable. Part 2 of the blog series.

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Putting Unexpected Results to Work at Sanford Health

By Robert Morison, Nov 08, 2017

Available to Research & Advisory Network Clients and Professional Members

An initiative in predictive analytics at Sanford Health yielded unexpected results and valuable lessons learned. A perennial challenge for health care providers is recognizing and anticipating which patients are incurring the greatest effort and expense. Some studies have suggested that as much as 80 percent of total health care spent is on only 20 percent of patients. With the migration to population health, where providers aim to improve quality while decreasing costs, high-utilization patients have become a focus for health systems.

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Data-driven decision-making: who doesn’t think it is a good idea? But it typically has a rough go in the real world of enterprise management, in part because the data itself often proves unreliable. For much of my business life IT has been tasked with building systems that could represent a single source of the truth. Unfortunately, that quest proved to be right up there with the holy grail and the fountain of youth—at best, aspirational, at worst, delusional. Today we have an opportunity to make a great leap forward, however, because for the first time in history we have broad access to high-volume data from a variety of sources that, when matched against each other, dramatically increase the probability of something like truth, and do so in a time window that is actionable. Not everyone, of course, has access to all the sources, so to kick things off let me present a framework of the possible, within which each organization can determine what its actual will be. Part 1 of the blog series.

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In order to compete in the age of digital disruption, companies must find ways to create exponential changes in speed to market and time to value. To achieve these changes, many companies have replaced a “permission based” product development process with a “show the customer what’s possible” approach.

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A New Approach to Technology Tool Selection at Steelcase

By Robert Morison, Oct 16, 2017

Available to Research & Advisory Network Clients and Professional Members

As part of an effort to increase analytics aptitude and usage across the enterprise, Steelcase is implementing an enterprise data visualization tool. Inspired by over 100 years of insight, Steelcase leverages the power of place to offer a comprehensive portfolio of workplace, healthcare, and education products, furnishings and services to serve the world’s leading organizations. Founded in 1912, Steelcase is the global leader in enhancing how people work, heal, and learn.

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Inquiry Response: Considering User Experience in a Terminal Server Environment

By IIA Faculty, Oct 09, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

We’ve invested a lot of time and money into our on-premises Microsoft products, with a core of Excel and the PowerPivot add-on for data analysis and modeling in a Windows Terminal Server environment. We’re trying to create a better user experience, especially when it comes to latency for our remote users. We’ve started to use SQL Server Analysis Services (SSAS) to that end. When should we look beyond the Microsoft universe to meet our data needs? Also, is there anything we can do to improve latency?

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