Research

A Guide for Writing Analytics Job Descriptions in a Competitive Marketplace

By Jenny Schmidt, Oct 10, 2018

Available to Research & Advisory Network Clients Only

As a hiring manager or analytics leader, writing job descriptions to fill key analytics positions can be challenging. The data science and analytics environment changes at lightning speed and high demand for analytics professionals has created a highly competitive marketplace for attracting and retaining them.

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Modern Software Concepts for Analytics and Data Intensive Systems

By Casey Rosenthal, Oct 03, 2018

Available to Research & Advisory Network Clients Only

This research brief explores a handful of mental models as they relate to the current state of development in the software industry, particularly at companies with vast scales in terms of request traffic, data to work with or market share. Software as an industry in its own right is becoming increasingly complex; this march toward complexity is no less true for all other industries that depend on software or that are trying to implement complex analytics including AI. The question of how to manage complexity is one of the biggest issues we currently face.

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Migrating Analytics to the Cloud: It’s About Time

By David Macdonald, Robert Morison, Sep 26, 2018

Available to Research & Advisory Network Clients and Professional Members

More business applications, including both everyday and advanced analytics, are moving to cloud-based platforms. They seek the advantages of agility, innovation, and cost-effective high performance. But many cloud migrations fail to gain all these potential benefits. To maximize the performance and value of analytics in the cloud, weigh the options, and choose the right approach to migration.

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Cutting Through the Hype of Artificial Intelligence, Part 2

By Bill Franks, Sep 19, 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.

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DELTA Plus Model & Five Stages of Analytics Maturity: A Primer

By Thomas H. Davenport, Sep 11, 2018

Available to Research & Advisory Network Clients Only

The purpose of this research brief is to summarize the key elements of DELTA Plus and Five Stages of Analytics Maturity, and discuss how these two frameworks can be used to understand analytical maturity in your organization. Two new components were added to the DELTA model, creating the DELTA Plus model. The DELTA Plus Model Framework encompasses the five foundational elements of a successful analytics program (Data, Enterprise, Leadership, Targets, and Analysts) and introduces two new elements (Technology and Analytical Techniques) required for high performance.

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Data and Analytics Trends Impacting Marketing - PART 1:  Channels and Personalization

By Michael Koved, Aug 30, 2018

Available to Research & Advisory Network Clients Only

Analytics advances are accelerating at an unprecedented pace, and perhaps nowhere more quickly than in marketing. Advances in computing hardware, software applications and delivery channels enable marketers to deliver timely and relevant messages to customers in ways that were unimaginable five or 10 years ago. New data sources are becoming available, and innovative ways to use data to shape the customer experience continue to push the envelope of possibilities. However, keeping track of the advances and being able to decipher which are relevant to your reality has a direct inverse relationship to the rate of change.

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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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