Research

Analytics Maturity Powers Company Performance

By David Alles, Sep 21, 2017

Available to Research & Advisory Network Clients Only

Does the development of enterprise analytics capability really drive superior company performance? IIA’s previous research briefs demonstrate that analytics maturity varies significantly between industries and across the top-performing companies in each industry. This follow on research brief uses IIA’s proprietary analytics maturity data – from leading companies like Amazon, Apple, Netflix and Google – and publically available financial and company data, to illustrate the positive association between analytics maturity and superior company performance.

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Inquiry Response: The Bridge Between Internal and External Reporting

By Adam Moore, Sep 11, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

We have an external reporting tool with a certified process and strong governance. Separately, we have management reporting data and analytics data, and for analytics we are standing up a data lake from disparate sources. My challenge is around the external tool and management reporting data. Right now we bridge the management reporting back to the externally reported figures, but I don’t see a need for this tie because the purposes are different. However, senior management has an emotional commitment to the bridge, so I’m wondering if that bridge is really important and whether you are seeing companies diverting from the need to build a bridge.

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“The real issue is that every business is now a tech business – whether it wants to be or not – and that means not just new skills and experiences, but a new outlook on opportunity and strategy.” This quote is from a recent Forbes analysis of the different business models companies can deploy and how each one is performing in the new digital world.

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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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The ever-increasing role of technology in the modern marketplace has made transactions quicker and more convenient than ever for both businesses and consumers. Unfortunately, it’s also invited a greater risk of payment fraud and other cybercrime. Fraudsters have access to a variety of sophisticated attacks that can cause tremendous harm in a very short period of time – a reality that requires advanced tools capable of rapidly predicting, detecting and responding to suspicious activity and adapting to a constantly evolving digital landscape. Perhaps no such tool is more powerful than machine learning, and businesses are increasingly turning to this technology to guard themselves against cyberattacks.

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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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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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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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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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