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Operationalizing Analytics

A leaders guide to creating effective analytics

Canada Post on the (careful) commercialization of data

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When Canada Post embarked on their transformational initiative to monetize address data as a value-add service to Canadian businesses, they did it under one condition: That privacy and protection would be a constant consideration. As a common data point across databases, address data is an integral part to any Master Data Management strategy. It’s powerful [...]

All Operationalizing Analytics Stories

The importance of processes and incentives according to “I Love Lucy”

For analytics to work, you must change the process and align incentives

In the final installment of SAS’ Laura Squier’s “Myths and Realities of Successful Analytics” series, she illustrates the importance of changing processes and aligning incentives through a familiar “I Love Lucy” scene.

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10 questions to ask before building an analytical model

Describe, predict and prescribe

“When building an analytic model, it’s easy to get caught up in the grandeur of what you might achieve. But enthusiasm won’t replace a rock-solid foundation,” says SAS’ Laura Squier in this post of the the “Myths and Realities of Successful Analytics” series. She gives some advice on how to get started.

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Data visualization: Taking the pain out of debt collection

Data Visualization

DirectPay uses data on payment behavior to streamline the collection process. To discover and understand what all that data is telling him, Colin Nugteren, Manager of Operations at DirectPay, chose SAS® Visual Analytics.

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Big data, big value … huge opportunity

The value of big data

The Centre for Economics and Business Research (Cebr) conducted two studies into the value of big data – perhaps the only studies of their kind to date. Graham Brough, CEO of Cebr, discusses the Centre’s forecasts and ways to get the most of your data in the future.

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8 professionals you need to build your analytics dream team

Getting the right data to measure human capital

In this installment of the “Myths and Realities of Successful Analytics” series, SAS’ Laura Squier names the eight professionals in her analytic dream team–and explains why they’re crucial to the success of any analytic project.

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Failing for the wrong reasons

Evan Stubbs

Insight without action is worthless. Evan Stubbs, author of The Value of Business Analytics, explains that all of the data and insight you have is worthless to your company unless it is acted upon.

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The “data artist” must balance creativity and control for effective predictive analytics

Senior Industry Consultant for Insurance, SAS

A better term for the “data scientist” may be “data artist,” says SAS’ Rachel Alt-Simmons. She explains how this role needs to embed a solution efficiently within a business process. And one way to do this is through the Lean methodology.

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Transform your business through a reliable deployment architecture

A Reliable Deployment Architecture

A reliable deployment system for analytic models is vital to successfully operationalizing analytics. Learn more in this article, part of a series on operationalizing analytics.

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Improve analytics through an industrial scale process

Creating an Industrial-Scale Process

If you’re looking to operationalize analytics, you can’t do it without a repeatable, industrial-scale development process. Discover what that looks like in this article, part of a series on operationalizing analytics.

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Analytics that work for your business (not the other way around)

Solving the Right Problem

The first step to operationalizing analytics is understanding and solving the right problem. Find out how in this article, part of a series on operationalizing analytics based on a recent white paper by James Taylor, Decision Management Solutions.

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