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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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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In financial services and other highly regulated industries, regulatory compliance depends more than ever on a company’s analytical capabilities. With increasing regulatory requirements and scrutiny, reliable analytics are essential to the timely accuracy in reserves calculations, stress tests, due diligence, and regulatory compliance. The analytics tools and technologies employed, the flexibility of the technology platform, and the comprehensiveness of the overall analytics program all can accelerate, or compromise, the business processes for regulatory compliance.

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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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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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Finance Must Ramp Up Role as Analytics Leader

By Thomas H. Davenport, Nov 15, 2016

The fictional crime-solver Sherlock Holmes once referred in a conversation to “the curious incident of the dog in the night-time.” A Scotland Yard detective replied, “The dog did nothing in the night-time.” Holmes retorted, “That was the curious incident.” In the field of analytics, the equivalent of the dog that didn’t bark is the relatively low level of adoption of advanced analytics in finance and accounting functions. Despite being a quantitative field by nature, finance has trailed other functions like marketing, supply chain, operations, and even human resources in employing advanced analytics to make key decisions.

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Like many other Americans who went to bed on election night prematurely, I learned about Donald Trump’s stunning victory in the U.S. presidential election on my phone early in the morning. The result was unambiguous but shocking and hard to process, especially at 5 a.m. But also like many other Americans, my shock wasn’t driven by a lack of awareness of Americans’ prevailing anti-establishment mindset and desire for change that tilted the vote (I’ve resided in three of the four key Midwestern states that “flipped”), but by the disconnect between the final result and the longstanding, data-driven expectation we had overly trusted.

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