Research

How to Hire and Test for Data Skills: A One-Size-Fits-All Interview Kit

By Tanya Cashorali, Aug 08, 2018

Available to Research & Advisory Network Clients Only

Given the potential benefits of data science and analytics to any organization and the limited talent pool of skilled resources, it’s critical to examine your interviewing process and determine whether it’s truly designed to source a top-tier data team. With the recent demand for data analytics and data scientist skills, it has become an increasingly daunting task for managers to adequately test and qualify candidates. This research brief includes a simple data exercise that can be completed by any candidate for a data-related role, regardless of experience or education levels. This test has been administered to dozens of candidates across a variety of industries.

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Attributes of a Data Program: A Practical Guide

By Gregory Nelson, Jul 25, 2018

Available to Research & Advisory Network Clients Only

The ever-growing volume of data challenges us to keep pace in ensuring that we use it to its full advantage. This research brief addresses the tools, technologies, methods and processes useful in designing a data program that is both relevant and actionable.

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Valuing Analytics: Return on Investment (ROI) and Returned Business Value (RBV)

By Doug Mirsky, Jul 17, 2018

Available to Research & Advisory Network Clients Only

What is the value of analytics? This research brief discusses methods of assigning value to advanced analytics efforts and communicating it to stakeholders.

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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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