Research

Inquiry Response: Start At The Beginning and Keep It Simple

By IIA Faculty, Oct 16, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

How sophisticated should our techniques and capabilities be at the outset? Where do others like to start? From the business view (demand): How complex are the strategic problems we seek to support and enable with data science insights? From the analytic view (capability): How sophisticated should our core techniques be?

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Phone Briefing: Learnings from Strata New York 2017

By David Alles, Oct 06, 2017

Available to Research & Advisory Network Clients Only

The Strata Conferences have built notoriety for sharing the latest insights on advanced analytics methods, emerging technologies, and impressive case studies on advancing analytics practices. During the Strata New York Conference, IIA’s VP David Alles attended several sessions and brought back a number of key takeaways.

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Inquiry Response: Tips for Prioritizing Ad-hoc Analytics Requests

By Adam McElhinney, Sep 25, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

Our company is taking a hard look at administrative cost structures, and for my department we’re trying to determine what kind of value we bring. How can we prioritize ad-hoc data analytics requests that come in along with the other projects we have in process?

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Inquiry Response: Starting a Knowledge Management Program

By Mark Madsen, Aug 28, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

Our analytics department is starting a knowledge management program. What are some best practices of starting a program for our analytics department? We’re looking for lessons learned and anything that would help a knowledge management program be more successful for people doing analytics.

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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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The analysis of Internet of Things (IoT) data is quickly becoming a mainstream activity. For this blog, I’m going to focus on a few unique challenges that you’ll most likely encounter as you move to take IoT data into the AoT realm.

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Driving Clinical and Operational Performance Through Analytics

By Jack Phillips, David Alles, Aug 02, 2017

Available to Research & Advisory Network Clients Only

As much as any industry today, healthcare sits at the intersection of both technological and societal change. Web, mobile, cloud, and data technologies are being applied to myriad patient-level applications to disrupt traditional patient care methods, and the very way that hospitals operate and compete. Emerging technologies leveraging the Internet of Things (IoT), particularly in the wearables category, will most certainly shift the role of care and wellness from provider to patient. Recent research from the International Institute for Analytics (IIA) has now quantified the significant gap in maturity between all healthcare segments and most other industries. But the research also reveals a discreet set of steps healthcare providers can follow to improve capabilities and move up the analytics maturity curve.

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Are Analytics Truly Self-Service?

By Thomas H. Davenport, Jul 25, 2017

I have been thinking about some of the changes over the last decade in analytics, coinciding with the revised and updated release of my book with Jeanne Harris, Competing on Analytics. The book is ten years old, and much has changed in the world of analytics in the meantime. In updating the book (and in a previous blog post about the updates), we focused on such changes as big data, machine learning, streaming analytics, embedded analytics, and so forth. But some commenters have pointed out that one change that’s just as important is the move to self-service analytics.

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Inquiry Response: Suggestions for Getting Data Scientists to Embrace Agile Methods

By Mark Haseltine, Jul 17, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

Our company has recently adopted a Scrum/Agile framework, which has caused some hiccups with our data scientists, who are used to managing their projects themselves. They tend toward perfectionism, which takes longer. Our goal is to build model minimum viable products (MVPs) faster, using two-week sprints for testing/incrementing the models. Part of the problem is that the data scientists don’t fully trust the process because of the loss of control to the Scrum master and also because of the continued perception that they have to produce perfect models the first time out. How can we get our data scientists to embrace the Agile process?

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Artificial intelligence has quickly become one of the hottest topics in analytics. For all the power and promise, however, the opacity of AI models threatens to limit AI’s impact in the short term. The difficulty of explaining how an AI process gets to an answer has been a topic of much discussion. In fact, it came up in several talks in June at the O’Reilly Artificial Intelligence Conference in New York. There are a couple of angles from which the lack of explainability matters, some where it doesn’t matter, and also some work being done to address the issue.

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