Research & Insights

Is AI over-hyped in 2017?

By Joanne Chen, Jul 20, 2017

Over the next ten years, I don’t believe AI is overhyped. However, in 2017, will all our jobs be automated away by bots? Unlikely. I believe the technology has incredible potential and will permeate across all aspects of our lives. But today, my sense is that many people don’t understand what the state of AI is, and thus contribute to hype. So what can AI do today?

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5 Things New Analytics Leaders Should Do to Succeed

By Bill Franks, Thomas H. Davenport, Jul 19, 2017

Available to Research & Advisory Network Clients Only

There is a fair amount of management research suggesting that the first 90 days or so are the most important time of a leader’s tenure. It’s when you establish your reputation and it determines what people start to think about you in your role. It’s often hard to change those first impressions. Therefore, IIA held a webinar to discuss this very important period for senior analytics leaders like a Chief Analytics Officer, Chief Data Officer, VP of analytics, or similar senior role. This paper captures the key elements of the discussion between Bill Franks and Tom Davenport, which focused on five essential things new analytics leaders should do to set themselves up for success.

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Saving Retail

By Geoffrey Moore, Jul 18, 2017

Okay, so you know a sector is in trouble when there is a Web page in Wikipedia entitled “The Retail Apocalypse.” This post is not about how much trouble retail is in. This one is about how it can get out.

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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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Top-Down Implementation Process for Rapid Data Governance Adoption

By Benson Hsu, MD, MBA, Doug Nowak, MBA, Mark Wheeler, Jul 12, 2017

Putting a solid data governance structure in place is challenging, but Sanford Health, a large integrated health care delivery system spanning six states, describes how to accomplish the task quickly using a top-down process.

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Over the past two years, I observed a very distinct pattern between companies that successfully navigate the new digital world and those that fall behind. As it turns out, those who are emerging as the early leaders in the age of digital disruption share one thing in common – a clear statement of intent. This blog includes examples that have helped shape my thinking on this issue.

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Inquiry Response: Building a Data Science Team, Recruitment and Hiring

By Rumman Chowdhury, Jul 10, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

We’re a major player within a massively complex industry with a three-year mandate to build a data science practice to help us drive competitive advantage. How do we assess the gaps in our current talent pool and what are the considerations for new hiring and recruitment?

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Sticky problems keep our data scientists engaged

By Sarmila Basu, Jul 06, 2017

When you bring together a wildly diverse group of geniuses, the hard part isn’t finding work for them to do; it’s finding something that’s hard for them to solve, something so challenging that they get a little bit mad and a lot fired up. If not, they’ll get bored and they might wander off. That’s why it has taken me seven years to build my team: an eclectic mix of statisticians, economists, mathematicians, electrical engineers, biophysicists, and telecommunications specialists who are helping shape the way Microsoft uses data.

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Inquiry Response: Key Messages on the Importance of Analytics

By Dave Cherry, Jul 03, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

My organization works with many different business groups and leaders and we need to articulate why they should be thinking about analytics. We often hear from them that they’re already doing analytics, although they’re really just getting some dashboard information. Most of them don’t know what they don’t know.

Questions:

  • How do we make analytics real for them, get them excited and get them to think about data differently?
  • How have others communicated benefits when they just started out and don’t have any internal use cases to reference?
  • How can we explain how things are different in an analytically mature organization?

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