Research & Insights

Inquiry Response: Galvanizing Enterprise Support for Analytics

By IIA Faculty, May 26, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

We want to improve our analytics capabilities across the organization, but need better ways to justify continued analytics expansion to the enterprise. What are some ways to begin to get buy-in from leadership for our analytics initiatives?

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On the Road: Data Down Under

By Jack Phillips, May 25, 2017

As the leader of a world-class service organization, I’m on the road a lot meeting with research clients and prospective customers alike. Each week I try to meet or speak with at least one team at IIA’s ever-growing group of high-performing research clients. I thought it was time to start sharing some field notes of what I’m seeing and hearing. Last week I finished a trip to New Zealand and Australia, visiting with 10 companies from four distinct sectors in three major cities.

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Corruption is essentially the abuse of entrusted power for private gain; it uses a company as a tool for personal gain which is contrary to the official or fiduciary duty of the organization. Companies serious about reducing fraud within their walls need to recognize that regardless of their size and type, corruption is one of the most pervasive and impactful fraud types. It can occur in any department/division making purchases or from the other side of the business transaction in the company sales cycle.

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Inquiry Response: Scaling Models and Expediting Run Times

By Josh Gray, May 18, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

An analytics team working with dashboards at the C-suite level would like to know how they could expedite run-time for large scale models—pulled queries into a Spark-type infrastructure.

  • Considering only SQL and dashboard queries, is a single digit response time possible? What can we expect?

  • What should be considered when using data sets that are larger than our in memory for machine learning models?

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Any new leader in any field will have to face several challenges in the first few months on the job if he or she is to succeed. On May 11, IIA hosted a webinar where co-founder Tom Davenport and I discussed some of the challenges analytics leaders face and what they can do to ensure success. While the action steps apply broadly, we focused on how they apply specifically within the realm of analytics. This blog explores the key themes at a high level.

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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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Balancing Analytics Agility and Stability

By Bill Franks, May 11, 2017

There have been many science fiction stories (as well as video games!) that revolve around the tradeoffs between powerful, strong, hard to harm combatants and those that are small, nimble, but easy to harm. Both have their merits and both can be useful in different situations. However, the same profile doesn’t work best in every situation.

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Understanding Power in the Digital Economy

By Geoffrey Moore, May 09, 2017

We are all stakeholders in the economic systems within which we live and work, and the better we can understand their dynamics, the more likely we are to navigate them successfully. For the most developed economies of today, this means understanding the transition from an industrial to a digital economy, and specifically, how economic power is migrating from familiar to unfamiliar sites.

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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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Move Your Analytics Operation from Artisanal to Autonomous

By Thomas H. Davenport, May 02, 2017

Many organizations today are wondering how to get into machine learning, and what it means for their existing analytics operation. There are many different types of machine learning, and a variety of definitions of the term. I view machine learning as any data-driven approach to explanations, classifications, and predictions that uses automation to construct a model. The computer constructing the model “learns” during the construction process what model best fits the data. Some machine learning models continue to improve their results over time, but most don’t. Machine learning, in other words, is a form of automating your analytics. And it has the potential to make human analysts wildly more productive.

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