Research

Three Paths for Aligning Analytics to Business Strategy

By Daniel Magestro, Jack Phillips, Feb 27, 2017

Available to Research & Advisory Network Clients Only

As organizations strive to build their analytics capabilities, an unexpected challenge has plagued many efforts: The activities of analytics teams and the investments made to support them aren’t in sync with what executives expect or desire. On the surface, it might have seemed straightforward for “business analytics” to be in sync with the business’s strategic needs. After all, the decision to invest in the first place was driven by the business’s needs, right?

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Revisiting Common Obstacles to Analytics Success

By Robert Morison, Daniel Magestro, Feb 13, 2017

Available to Research & Advisory Network Clients Only

In 2013, IIA published a popular and useful research brief in response to a client’s general question: What are the common hurdles encountered when putting analytics to work in a business, both in developing analytical models and applications and in building enterprise analytical capability? For us, this question is so central to IIA’s mission of helping organizations navigate the many challenges to achieving analytics maturity, that we decided it was time for an update and a re-evaluation of common obstacles faced by analytics leaders and practitioners. This update is informed by major forces and trends in play over the past few years.

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Last month, I wrote about why simply making predictions isn’t enough to drive value with analytics. I made the case that behind stories of failed analytic initiatives, there is often a lack of action to take the predictions and turn them into something valuable. It ends up that identifying and then taking the right action often leads to additional requirements for even more complex analyses beyond the initial effort to get to the predictions! Let’s explore what that means.

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Analytics as a Service: The “Buy vs. Build” Decision

Jan 18, 2017

Available to Research & Advisory Network Clients Only

The idea of competing on analytics – building and sustaining competitive differentiation from innovative use of advanced predictive and prescriptive analytics – has moved, in less than a decade, from a revolutionary notion to a strategic commonplace.

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Amazon Web Services Moves Aggressively into Big Data, BI and Analytics Services

By David Alles, Jan 16, 2017

Available to Research & Advisory Network Clients and Professional Members

There were some significant big data and analytics anniversaries in 2016. The year marked the 10th anniversary of the release of Hadoop, which we recognized in our event summary “Strata + Hadoop World 2016: Developments in Analytics on the 10th Anniversary of Hadoop” (link). 2016 also marked the 10th anniversary of the launch of Amazon Web Services (AWS) and the fifth anniversary of the first AWS re:Invent conference. IIA covered AWS re:Invent 2016 in Las Vegas Nov. 28 to Dec. 2, 2016. No other conference today is more associated with the rapid development and growing adoption of cloud computing. The first re:Invent conference in 2012 had 4,000 attendees. Five years later, this sold-out conference grew to 32,000 attendees spread across four of the largest properties in Las Vegas.

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How the Machine Learning of Today is Driving the Artificial Intelligence of Tomorrow

By Andrew Pease, JOSEFIN ROSÉN, Robert Morison, Dec 22, 2016

Available to Research & Advisory Network Clients Only

Machine learning is hot and for good reason. The components — big data, computing power, analytical methods — are in place, and compelling applications are multiplying. To capitalize on the technology, organizations must build experience. They must also proceed pragmatically with one eye on the business and the other on the ethical implications of the algorithms deployed and the decisions automatically made. To explore the opportunities, challenges, and success factors of machine learning today and tomorrow, IIA spoke with Andrew Pease, Principal Business Solutions Manager, Global Technology Practice at SAS Institute and Josefin Rosén, Principal Advisor Analytics, Nordic Government at SAS Institute.

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Improve New Product Development with Predictive Analytics

By Thomas H. Davenport, Dec 13, 2016

Recently on this site, one of us wrote about the new product development analytics used by Netflix. In a nutshell, the company classified the key attributes of past and current products or services and then they modeled the relationship between those attributes and the commercial success of the offerings. This produced a predictive model that provides the company with guidance about how likely a new product or service is to be successful.

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Many times when I speak with analytics managers or business people interested in analytics, they tell me that performing some analytics on data is not the primary problem they have. “We have to get the analytics integrated with the process and the systems that support it,” they say. This issue, sometimes called “operational analytics,” is the most important factor in delivering business value from analytics. It’s also critical to delivering value from cognitive technologies – which, in my view, are just an extension of analytics anyway.

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Will Your Evolving Business Intelligence Strategy Make It or Break It?

By Andy Bitterer, Tapan Patel, Robert Morison, Nov 29, 2016

Available to Research & Advisory Network Clients Only

Business intelligence has come a long way – from assistance with report generation to self-service platforms for discovery and analytical insight. As technological capabilities and business aptitude with information continue to advance, the next generation of BI will be even more capable and valuable to the enterprise. To discuss today’s success factors and tomorrow’s opportunities, IIA spoke with Andy Bitterer, Senior Director of Business Intelligence Product Management at SAS, and Tapan Patel, Principal Product Marketing Manager at SAS.

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Just How Smart Are Smart Machines?

By Thomas H. Davenport, Nov 22, 2016

The number of sophisticated cognitive technologies that might be capable of cutting into the need for human labor is expanding rapidly. But linking these offerings to an organization’s business needs requires a deep understanding of their capabilities. If popular culture is an accurate gauge of what’s on the public’s mind, it seems everyone has suddenly awakened to the threat of smart machines.

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