Observations from AWS re:Invent

By David Alles, Dec 06, 2016

Amazon launched Amazon Web Services (AWS) 10 years ago with a similar customer-centric, expansion minded approach to information technology. Today, there is no greater force in the technology industry than AWS. AWS will collect over $13B in revenue in 2016, a 55% increase over 2015. It contributes more than 100% of the operating profits of its parent company, as the other business units operate at a loss. Well known for providing the underlying infrastructure for digital native companies like Netflix, Uber and Airbnb, AWS is now heavily targeting the enterprise IT market – specifically Big Data, BI and analytics.

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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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AWS re:Invent

By Jack Phillips, Nov 29, 2016

Can’t make it to AWS Reinvent in Las Vegas this week, but want to keep up with what’s going on? A quick reminder that IIA will be covering AWS Reinvent for our research clients, as we did for O’Reilly’s Strata conference in New York earlier this year. This coverage of the major analytics technology events is a new feature of IIA’s flagship Research and Advisory Network (RAN) service in 2016 and includes access to both a written “Cliff’s Notes” summary and a hosted phone discussion around the most important trends, announcements and new technologies being showcased at each event.

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Thanksgiving Reflection

By Jack Phillips, Nov 24, 2016

In the spirit of Thanksgiving, I’d like to reflect on the past year.

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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. Several recent films have featured robots with scary abilities to outthink and manipulate humans. In the economics literature, too, there has been a surge of concern about the potential for soaring unemployment as software becomes increasingly capable of decision making. Yet managers we talk to don’t expect to see machines displacing knowledge workers anytime soon — they expect computing technology to augment rather than replace the work of humans. In the face of a sprawling and fast-evolving set of opportunities, their challenge is figuring out what forms the augmentation should take. Given the kinds of work managers oversee, what cognitive technologies should they be applying now, monitoring closely, or helping to build?

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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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As analytics are embedded more and more deeply into processes and systems that we interact with, they now directly impact us far more than in the past. No longer constrained to providing marketing offers or assessing the risk of a credit application, analytics are beginning to make truly life and death decisions in areas as diverse as autonomous vehicles and healthcare. These developments necessitate that attention is given to the ethical and legal frameworks required to account for today’s analytic capabilities.

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How an Analytics Mindset Changes Marketing Culture

By Adele Sweetwood, Nov 08, 2016

The most significant culture shift today for marketing teams is adopting an analytical marketing approach. Change has been forced on us, due to the steady creation of new digital channels that have affected customer expectations. The problem is that while marketers are thinking differently about their data, in many cases they’re not acting differently based on what the data is telling them.

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Stuart William was in one of my former MBA classes at NC State in 2008, and graduated into one of the worst economies ever in May of 2009. Upon graduation, there simply weren’t any jobs available at all! During that period, he networked with as many people as possible, including a fundraising arm at Wake Tech. He met a colleague who worked at Carquest, and after several interviews, took a job there. He started in supply planning, overseeing over $100M of spend in batteries and other categories. He then went into global imports for the central purchasing group in Raleigh. He became a director at that point, working with sales planning, inventory planning, and financial planning, and pulling together the Sales and Operations Planning team, as well as introducing new products and eliminating obsolescence. This was a lot of planning, a lot of analytics, and a lot of work.

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