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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Inquiry Response: When Data Science Team Meets Engineering Team

By Eddie Satterly, Nov 11, 2019

Available to Research & Advisory Network Clients Only

Inquiry:

We have a data science team that supports our engineers, with the goal of using analytics to help them make engineering decisions that produce better products for the field. Do you have any insights on how we can work with engineering?

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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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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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We’ve had technical people focused on the ingestion and management of data for decades. But, only recently has data engineering become a critical, widespread role. Why is that? This post will outline a somewhat contrarian view as to why data engineering has become a critical function and how we might expect the role to evolve over time.

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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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Modernizing Analytics for Law Enforcement

By Steve Shirley, Captain Steve Serrao, Robert Morison, May 29, 2019

Technologically, law enforcement is an exciting field these days. Vast new sources of electronic data and advanced analytical methods offer opportunities not only to resolve individual investigations in record time, but also to discover patterns of activity to exploit in crime prevention. To seize these opportunities, many agencies are modernizing their information and analytics platforms. To explore the pragmatic challenges and potential benefits of modernization, IIA spoke with Steve Shirley, Head of Customer Advisory for the SAS Justice and Public Safety Team, and Captain Steve Serrao, Senior Customer Advisor for the SAS Justice and Public Safety Team.

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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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