Research

The vast majority of people building analytics and data science processes have every intention of being good and ethical. As a result, most potentially unethical and evil processes arise in situations where that wasn’t the intention. The problem is typically that proper focus and governance is not in place to keep analytics and data science processes on the side of good. On top of that, what is good and what is evil isn’t nearly as clear cut as we’d wish it to be.

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Mapping an Information Economy

By Doug Mirsky, Aug 16, 2019

Available to Research & Advisory Network Clients Only

Information Economies in Organizations

The data warehouse revolution began in 1991 when Bill Inmon published Building the Data Warehouse. Inmon observed, early in that book, that every organization has a naturally occurring information economy, and that most naturally occurring information economies were inefficient, duplicative and prone to produce suboptimal decisions.

This observation of Inmon’s has not gotten anywhere near the credit, or attention, it deserves. A decade’s worth of collective practice in advanced analytics should tell us that everything we know about real-world economies applies to our information economies. There is demand for information by people and functions in an organization, and there is a supply of (some of) that information. There is (some amount) of technical and procedural infrastructure – some kind of market — to bring demand and supply together in an organized way. That “market” infrastructure is often partial, fragile and in some cases ineffective. There are competitive alternatives (like cloud service providers and SaaS vendors), over- and under-regulation (various data governance models), excessive demand-side taxation (cost allocation strategies), failure to invest in infrastructure, and all other elements of economies.

When organizations are planning strategy-driven large-scale advanced analytics programs, they should begin their planning by characterizing their as-is information economy.

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Mastering the Art & Science of Storytelling

By Brent Dykes, Jul 26, 2019

Available to Research & Advisory Network Clients Only

Analytics experts love data. But just presenting raw data or even insights derived from data isn’t good enough. To create business value from data requires that analytics professionals develop skills at data storytelling. This entails telling persuasive stories, tailored to a specific audience, that combine data, narrative, and visuals effectively.

Why Storytelling?

Human beings love stories. In fact, author Philip Pullman has written, “After nourishment, shelter, and companionship, stories are the thing we need the most in the world.” And scriptwriting expert Robert McKee has said, “Storytelling is the most powerful way to put ideas into the world today.”

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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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