Research

While many companies are investing in online and other digital tools, most have not developed a comprehensive omnichannel strategy to deliver great customer experiences over multiple customer touch points. Legacy marketing mindsets, just like legacy IT mindsets undermine the ability of companies to explore and experiment with all whole new range of digitally enabled customer engagement tools.

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Inquiry Response: Balancing Centralized and Decentralized Analytics Functions

By Dave Cherry, Jan 08, 2018

Available to Research & Advisory Network Clients Only

Inquiry:

We are interested in discussing what specific use cases and responsibilities should be assigned to central analytics function and what use cases are better allocated to decentralized business units.

Another consideration would include analytic employee engagement – variety of work, career pathing, shared expertise, training/development, retention, and connection of decentralized resources. 

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Inquiry Response: Data Retention Policies for the Enterprise

By IIA Faculty, Jan 02, 2018

Available to Research & Advisory Network Clients Only

Inquiry:

What should we consider as we think about data retention policies? We’re trying to decide how long to keep data in general and specifically for customer lifetime value.

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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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