Research

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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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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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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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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As businesses increasingly adapt to the realities of modern technology, data security has become a critically important component of any successful business plan. Business runs on data – whether it’s financial records, credit card numbers, medical records, email addresses or anything in between – and companies that fail to adequately protect that data leave themselves and their customers exposed to tremendous risk. As high-profile incidents at Target, The Home Depot and other large companies have shown, data breaches can incur millions of dollars in expenses and damage the trust of consumers. This blog is a more detailed look at the true cost of a data breach, as well as best practices for keeping digital data safe and secure.

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The Manufacturer’s Dilemma

By Geoffrey Moore, Jun 20, 2017

There is a lot of serious talk in America these days about improving the state of our manufacturing sector. Smart products, Internet of things, robotics, predictive maintenance—all great stuff. But none of it addresses the most fundamental challenge facing the sector: how to deal with a demand/supply inversion which has made the customer king.

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Last July, I suggested that well-established companies could no longer sustain competitive advantage through using their size and market reach as barriers to entry. The unprecedented assault of new waves of digital disruption have enabled companies of any size to penetrate some portion of well-established companies’ value chains. Taken together these disruptions are making what was scarce and expensive now ubiquitous and cheap as the chart below illustrates. Simply put, companies must now find totally new ways to compete or risk a major wipeout.

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Inquiry Response: Migrating to New Data Platforms and Data Sources

By Mark Molau, Jun 07, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

I’m interested to discuss methods/options for scaling analytics across our highly matrixed organization. We have done a lot of work building out our analytics strategy, but now need to get more tactical. How can we make this transition while maintaining the current systems, building future systems and constructing a bridge between the two?

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