Research

Evaluating the Risk of Analytics Partners for Your Enterprise

By Bill Franks, Jun 28, 2018

Available to Research & Advisory Network Clients Only

How do you choose the right vendor partners to move your analytics program forward? Large organizations today need to partner with vendors to successfully build, deploy, and maintain enterprise-level analytics programs. In today’s world, however, there are so many potential vendor partners that it is hard to know where to begin. An often under-addressed aspect of evaluating which partners to invest in is the need to look at the inherent risk a potential partner brings to the table along with its compelling offerings. This research brief provides a framework for evaluating the risks of potential analytics partners. By developing a consistent and complete approach to assessing risk, an organization will help itself make better investments and, therefore, improve its analytics performance.

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Cutting Through the Hype of Artificial Intelligence, Part 1

By Bill Franks, May 30, 2018

Available to Research & Advisory Network Clients Only

Artificial Intelligence (AI) is one of the hottest topics in the analytics space today. Within a two to three year span, AI went from relative obscurity to an extreme level of industry attention and media coverage. As a result, organizations that barely knew how to spell Artificial Intelligence a few years ago are now charging full steam ahead in pursuit of AI initiatives. In many ways, this is a good thing. After all, AI is quite powerful and has the ability to drive tremendous value if applied appropriately. However, this attention also has some negative consequences. Most notably, the topic of AI is so full of hype today that many organizations are struggling to separate what is real and achievable from what is pure hype and wishful thinking. This is the first of a two-part series on Artificial Intelligence. This brief will examine some critical aspects of AI to understand in terms of where the market is a bit over-exuberant and also how AI works in reality. It will also cover some strengths and pitfalls to be aware of.

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Data Management Platforms and Audience Building

By Michael Koved, May 21, 2018

Available to Research & Advisory Network Clients Only

To identify customers and potential customers, Marketers use a Data Management Platform (DMP) to build audiences and track campaign results. DMPs leverage sophisticated customer identification capabilities to enable Marketers to integrate disparate and specialized customer level data to build audiences, track results and gauge success. DMPs improve response rates and cost-efficiency through better targeting. This paper provides an overview of how DMPs work and share practitioner tips for leveraging DMPs and getting the best from yours.

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Organizing Analytics

By Robert Morison, May 16, 2018

Available to Research & Advisory Network Clients Only

This research brief describes and offers guidance on:

  • The fundamental goals of organizational structure
  • Six basic models for organizing analytics
  • Mechanisms for coordinating across organizational boundaries
  • Design variables that enable or constrain organizational shape
  • How analytics organizations commonly evolve
  • How to assess readiness for greater centralization
  • Structural variations driven by technological and business change
  • Questions to ask in planning your next structural move

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Enterprise AI Primer: Build on Your Strengths

By Thomas H. Davenport, Kris Hammond, Apr 16, 2018

Available to Research & Advisory Network Clients Only

This brief is based on the premise that there’s a general confusion when it comes to AI impact, strategy, investment options, and even terminology. A significant factor is that for many companies, AI can and should be viewed as a natural progression of their existing business analytics capabilities. We believe that positioning AI as a natural evolutionary outgrowth of analytics, thus benefitting from already established analytics capabilities, provides the best and easiest path for most companies to successfully “step into” AI.

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How to Self-Assess the UI/UX Design of Analytics Solutions

By Brian O’Neill, Apr 05, 2018

Available to Research & Advisory Network Clients Only

As internally developed analytics solutions become increasingly sophisticated, analytics teams are faced with many of the design challenges seen in commercial, analytics-driven software. After years of working with a variety of different clients on analytics-driven software products, the display of quantitative data, and dashboards, Brian O’Neill developed a set of axioms you can ask yourself to help you begin evaluating the design of analytics solutions for internal customers.

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Analytics Maturity Transition Guide: Stage 3 to Stage 4

By Robert Morison, Mar 14, 2018

Available to Research & Advisory Network Clients Only

Advancing the analytical maturity of an enterprise requires coordinated progress across a variety of capabilities. We track enterprise maturity with a 5-stage model, and we group capabilities into the five elements of the DELTA framework – Data, Enterprise, Leadership, Targets, and Analysts. These two models, introduced in Competing on Analytics and Analytics at Work, continue to stand the test of time. This guide focuses on the core DELTA components and presents context and recommendations for moving from maturity Stage 3, “Analytical Aspirations,” to Stage 4, “Analytical Companies.”

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Analytics Maturity Transition Guide: Stage 2 to Stage 3

By Robert Morison, Mar 07, 2018

Available to Research & Advisory Network Clients Only

Advancing the analytical maturity of an enterprise requires coordinated progress across a variety of capabilities. We track enterprise maturity with a 5-stage model, and we group capabilities into the five elements of the DELTA framework – Data, Enterprise, Leadership, Targets, and Analysts. These two models, introduced in Competing on Analytics and Analytics at Work, continue to stand the test of time. This guide focuses on the core DELTA components and presents context and recommendations for moving from maturity Stage 2, “Localized Analytics,” to Stage 3, “Analytical Aspirations.”

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Analytics Maturity Transition Guide: Stage 1 to Stage 2

By Robert Morison, Feb 28, 2018

Available to Research & Advisory Network Clients Only

Advancing the analytical maturity of an enterprise requires coordinated progress across a variety of capabilities. We track enterprise maturity with a 5-stage model, and we group capabilities into the five elements of the DELTA framework – Data, Enterprise, Leadership, Targets, and Analysts. These two models, introduced in Competing on Analytics and Analytics at Work, continue to stand the test of time. This guide focuses on the core DELTA components and presents context and recommendations for moving from maturity Stage 1, “Analytically Impaired,” to Stage 2, “Localized Analytics.”

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Operationalizing Analytics for Intelligent Fraud Detection and Case Management

By Michael Ames, Robert Morison, Jan 31, 2018

Available to Research & Advisory Network Clients Only

Fraud is widespread and continues to grow, especially online. It’s a major problem in a variety of industries and government agencies far beyond the familiar areas of financial and retail fraud, where credit card information is compromised and fraudsters use it for online purchases. The problem worsens as criminals get more organized and technologically sophisticated and operate at greater scale. As fraudsters innovate and scale up, fraud prevention and investigation become more challenging, and advanced analytics become a bigger part of the solution.

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