Research

Graph Analytics Use Cases

By Daniel Graham, Jul 10, 2019

Available to Research & Advisory Network Clients Only

Introduction In 1996, two computer science students — Larry and Sergei — were enthralled by the emerging internet. But finding anything on the undeveloped web was horribly difficult. Then came the “Aha!” discovery that academic web page citations (URLs) are a proxy for popularity. If many websites “like” the same web page, that page value is probably higher to researchers. So Larry and Sergei designed an algorithm called PageRank. It measured “link juice” — the strength between web pages. Google emerged from PageRank, web URLs and an advertising business model. This article explores the incredible value of “link juice.” Graph analysis turns the relational…

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Inquiry Response: Where Machine Learning Can Help with Decision Making

By Blake Johnson, Jun 10, 2019

Available to Research & Advisory Network Clients Only

Inquiry:

What are use cases where machine learning (ML) delivers the best decisions and performance? Vendors are peddling their ML solutions, and because it’s sexy right now, they’re gaining traction with executives being told outside ML algorithms will outperform on every data problem.

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Inquiry Response: Tips for Building Marketing Mixed Models

By IIA Expert, May 13, 2019

Available to Research & Advisory Network Clients Only

Inquiry:

After using an outside vendor to build our marketing mixed models, we’re going in-house to leverage our particular business expertise to improve the models. What should we be thinking about given that we use a Bayesian hierarchical time series model and we want to understand the impact of marketing spend at our stores nationwide?

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GE’s Path to Emerging Analytics Technologies

By Mano Mannoochahr, May 01, 2019

Available to Research & Advisory Network Clients Only

GE aspires to be an algorithmic business, but recognizes this transition will not occur overnight. It will occur in stages as the company develops new capabilities and implements multiple emerging technologies. This transition requires building solid foundational systems and encouraging broad experimentation and innovation using new analytics technologies.

Beyond getting experience with next-generation technologies, transitioning to an algorithmic business requires cultivating an enterprise-wide data culture and changing how people work throughout the company, particularly on the front line.

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Inquiry Response: Agile For Analytics

By IIA Expert, Apr 22, 2019

Available to Research & Advisory Network Clients Only

Inquiry:

We use an Agile-like methodology for analytics projects and are always looking for ways to improve our execution and speed. Do you have any advice?

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Portland 2019 Analytics Symposium Video: Jennifer Prendki

By Jennifer Prendki, Apr 17, 2019

Available to Research & Advisory Network Clients Only

Agile for the Data Science Team

Agile is a methodology and a way of working, originally created to improve the speed and results of software development. It emphasizes individuals and interactions over processes and tools; working software over documentation; and collaboration with customers and responsiveness to change. Benefits include greater predictability, adaptability, transparency, and accountability. Agile is known for working in “scrums”—which are teams with well-defined roles. Scrums engage in “sprints,” which are specified work in a set time period.

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Portland 2019 Analytics Symposium: Event Summary

Apr 17, 2019

Available to Research & Advisory Network Clients Only

IIA’s 14th Analytics Symposium was held in Portland, Oregon, on March 12, 2019. This Symposium brought together leading analytics thinkers on the future of AI, data engineering, and analytics, along with analytics leaders from different industries, functions, and geographies to share insights and best practices.

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Operationalizing Customer Analytics in Financial Services

By Robert Morison, Apr 04, 2019

Available to Research & Advisory Network Clients Only

This paper explores the processes and success factors for operationalizing customer analytics by drawing on the experiences of four varied financial services institutions: a large credit union, a full-service bank with a strong focus on retail customers, and two firms focused on small and growing business customers. We’ll profile each and then look across them for commonalities and lessons learned.

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Measuring Analytics

By Robert Morison, Mar 20, 2019

Available to Research & Advisory Network Clients Only

In November 2017, IIA published the multipart research brief “Improving Analytics Measurement.” Our objective was to take the pulse of analytics measurement practice, including finding patterns of metrics usage and identifying innovative and useful metrics. Consult the brief for a range of analysis and recommended actions on improving analytics measurement. For that research, 19 organizations participated in a predominantly qualitative survey of how they measure ¬the work of analytics in their enterprises (we did not ask respondents to exhaustively list metrics in use). But based on that research, we were able to develop and conduct a broader survey of 52 specific metrics in use. This companion research brief shares and provides commentary on the results.

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Nearly 200 of IIA’s clients, analytics experts, and members of the analytics community gathered in Portland, Oregon this week for the spring Analytics Symposium. IIA also hosted its first Women in Analytics networking event, an interactive Analytics Workshop, and introduced two tracks of sessions to bring the most value to attendees. This blog covers key themes of the conference and highlights from each session.

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