Research

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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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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Inquiry Response: Path Toward Advanced Analytics for the End User

By Mark Molau, Jun 27, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

Everyone talks about dashboards and tools, but tools often produce a one size fits all solutions. Our quest is to produce a solution that appeals to multiple end users, effectively socialize the benefits, and push for adoption. We hope to use predictive analytics for process optimization – our integrated delivery systems are heavy on processes – so optimization here could yield great value.

Questions:

  • How do you start to move into a more predictive analytics culture and encourage the application of advanced analytics?
  • Where should we start to ensure the different end user audiences are receiving useful information/insights in a way that is most valuable for them?
  • What approach should we take to ensure that the tools we create get socialized, engaged, and adopted?

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Enhancing Decision Support Analytics in the Digital Era

By Stephan Kudyba, Thomas Ott, May 17, 2017

Available to Research & Advisory Network Clients and Professional Members

Visualization techniques enable users to overcome the tedious activity of examining detailed data corresponding to functional areas and provides them with an easy to comprehend view of performance attributes, providing timely decision support from digital resources. However, despite the advantage of robust graphics, visualization often suffers from a major limitation. As users consume information from visual platforms, they often take the next step in the decision support process, which entails inquiring as to what the factors or variables are that drive performance metrics. In other words, what is causing KPIs to move? In statistical terms, performance metrics are dependent or target variables and users quickly seek to understand the driver or independent variables that affect dependent variables.

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