Research

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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A Comprehensive Recruiting Strategy for Analytics Talent

By Joe DeCosmo, Bill Franks, Ralph Greco, Dan Magestro, Jun 30, 2017

Available to Research & Advisory Network Clients Only

While we recognize that the data-driven transformation of business leads to macro-level systemic challenges and potential shortages in the workforce, we believe that individual businesses actually hold a wealth of power and opportunity to meet their specific needs for analytical talent. In fact, to us the analytics talent gap is less about a broad shortfall of analytics talent, and more about the specific gaps between business needs and the analytical skills to meet those needs that present challenges in every company.

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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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Enhancing Decision Support Analytics in the Digital Era

By Stephan Kudyba, Thomas Ott, May 17, 2017

Available to Research & Advisory Network Clients and Professional Members

Visualization techniques enable users to overcome the tedious activity of examining detailed data corresponding to functional areas and provides them with an easy to comprehend view of performance attributes, providing timely decision support from digital resources. However, despite the advantage of robust graphics, visualization often suffers from a major limitation. As users consume information from visual platforms, they often take the next step in the decision support process, which entails inquiring as to what the factors or variables are that drive performance metrics. In other words, what is causing KPIs to move? In statistical terms, performance metrics are dependent or target variables and users quickly seek to understand the driver or independent variables that affect dependent variables.

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A Talent Playbook for Analytics 3.0

By Emilie Harrington, May 08, 2017

Available to Research & Advisory Network Clients Only

Organizations often face key investment decisions in the analytics area when trying to decide whether to invest a given dollar in talent or in an emerging analytics technology. Analytics capabilities run on an expensive combination of fiber, blade servers, and apps that require significant investments, but hardware and software without the right level of talent will result in limited capabilities at best. By investing in the right talent first and treating workforce investments as the foundation of analytics capabilities, an organization can maximize the return on its invested capital – both the human capital and the technology infrastructure. These organizations can begin to differentiate themselves from competitors in the market and ultimately achieve analytic competitive advantage.

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Detecting Corruption with Analytics: A Roadmap

By Rich Lanza, May 01, 2017

Available to Research & Advisory Network Clients and Professional Members

This brief is intended for both internal auditors and information security practitioners who are eager to use quantitative data and analytics as a reliable tool to expose corrupt practices, and analytics professionals who are seeking a primer on the key application areas for analytics in the area of corruption.

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Three Paths for Aligning Analytics to Business Strategy

By Daniel Magestro, 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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