Research

CAO Perspectives: Ideal Analytics Organization

By Doug Hague, Nov 13, 2019

Available to Research & Advisory Network Clients Only

To set the stage, the analytics organizational structure I’m presenting below pertains to an analytics organization between 60 and 120 people; this is the size that seems to be a sweet spot for an effective and efficient team (large enough to have specialized skill sets, but small enough to effectively demonstrate the benefits of the team). Moreover, I’m presenting such an organizational design in consideration of an analytics effort at an established, traditional corporation, not a digital native. Digital natives will break down differently with more need for data science and data management. With 60 to 120 people, I prefer a centralized organization with P&L Analytics/Ad Hoc Analysis dotted-lined to their business partners.

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Detroit 2019 Analytics Symposium Video: Tom Davenport

By Thomas H. Davenport, Nov 05, 2019

Available to Research & Advisory Network Clients Only

Many companies are dipping their toes into artificial intelligence, but only a few are attempting to put AI at the core of their strategies and business models. As with analytics, taking a leading position on AI is likely to be rewarded with competitive success. Tom will describe what companies that aspire to be “AI First” do, and how other companies can learn from their pioneering approaches.

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Detroit 2019 Analytics Symposium Video: Panel Discussion

By Drew Smith, Guy Lehman, David Dittmann, Nov 05, 2019

Available to Research & Advisory Network Clients Only

As companies reach a level of effective analytics maturity, the challenges shift from “How do we make this work?” to “What do we need to start doing now to stay ahead and prepare for tomorrow?” Join IIA’s New Executive Director of the Analytics Leadership Consortium as he leads a discussion with two of the leaders in their industries to explore their thoughts on preparing for what’s coming next.

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Detroit 2019 Analytics Symposium Video: Nick Curcuru

By Nick Curcuru, Nov 05, 2019

Available to Research & Advisory Network Clients Only

Artificial intelligence has become the hottest commodity in recent years, and business, academia, and government have embraced it to propel complex use cases. As AI becomes more woven into the fabric of organizations (and its criticality increases), enterprise infrastructure becomes essential. AI is only as strong as its weakest link. The ability to build out use cases, deploy into production, scale, and secure all relies on the supporting solutions and infrastructure. There are many different decisions to make when choosing the right solutions and infrastructure: On-premises or off? GPUs or CPUs? Which storage system and framework to use? The list goes on. Drawing on real-world considerations, use cases, and solutions, Nick Curcuru discusses different decisions—and the associated considerations and best practices—you need to exercise to build and deploy a successful AI.

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Detroit 2019 Analytics Symposium Video: Alistair Croll

By Alistair Croll, Nov 05, 2019

Available to Research & Advisory Network Clients Only

The lifespan of a company on the S&P 500 and Fortune 500 has plummeted from nearly 70 years to around 15. And attempts to innovate fail more than 95% of the time. But the best companies survive by balancing a portfolio of innovation approaches. Based on 10 years’ research and interviews with corporate innovation leaders around the world, this talk offers a model for managing and measuring new initiatives that is concrete enough to put to work immediately.

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A Framework For Analytical Approaches

By Elliot Bendoly, Sep 20, 2019

Available to Research & Advisory Network Clients Only

Any effective journey in analytics involves multiple touch points. Multiple stages of understanding. Multiple vantage points through which data is considered, and extracted intelligence scrutinized. Each step we take is designed to help fill in the blanks. Part of this effort involves outlining the structure of the problems we face, while the rest involves identifying strong solutions to those problems.

It’s a bit like putting together a complex puzzle … of an intricate maze … with multiple possible exit points. On top of that, not all the pieces are immediately available, or never will be, or will be blurry at best. And in contrast to regular puzzles, there aren’t really any obvious borders.

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The Fuzzy Line Between Good and Evil Data Science

By Bill Franks, Sep 12, 2019

Available to Research & Advisory Network Clients Only

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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