Research

Inquiry Response: Focus Here When Merging Analytics And BI To Support The Business

Jun 14, 2021

Available to Research & Advisory Network Clients Only

Inquiry:

We recently joined enterprise analytics and BI into one team. As we merge into one corporate support function, what should we focus on, especially when it comes to serving business lines and functions that also have embedded analysts?

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Inquiry Response: Graph Databases for Customer Data Complexity

Feb 08, 2021

Available to Research & Advisory Network Clients Only

Inquiry:

We’re developing a customer data platform and are challenged by the wide variety of ways our customers interact with us. How are other organizations trying to solve this problem?

Response:

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Inquiry Response: Foundations For A Digital Customer Experience

Jan 18, 2021

Available to Research & Advisory Network Clients Only

Inquiry:

We’re developing an advanced digital customer experience strategy that needs to be able to support B2B, B2C, and B2B2C. What should we look out for as we prepare our organization and our data for this evolution?

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Value and Opportunity: An Executive Guide to Procurement Integrity

By JEN DUNHAM, Laurent Colombant, Robert Morison, Jan 13, 2021

Procurement Integrity (PI) represents a broader problem and bigger opportunity than most businesses recognize. Comprehensive PI programs continuously validate purchasing transactions, using data and analytics to trace patterns, spot anomalies, and reduce fraud, waste, and abuse. The problems uncovered range from occasional opportunistic fraud to ongoing organized fraud, from duplicate invoices and other improper payments to regular kickbacks, from conflicts of interest to ongoing collusion with suppliers. Continuous monitoring of anomalies in procurement and supplier due diligence processes reveal potential problems, including data issues and process breaches, and help focus the efforts of audit and other investigative staff.

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2020 ANALYTICS PREDICTIONS AND PRIORITIES

By Thomas H. Davenport, Bill Franks, Drew Smith, Robert Morison, Dec 19, 2019

Each year, the International Institute for Analytics ends the year with a look at the latest analytics trends and the most pressing analytics challenges currently facing organizations. Our predictions are based upon our day-to-day work supporting and advising analytics leaders and organizations. We take advantage of the breadth of expertise and cross-industry perspectives we encounter every day from our clients, partners, and members of the IIA expert network. This is our 10th annual look toward the upcoming year, and our annual Predictions and Priority research brief and the associated webinar have become among IIA’s most popular content of the year. This year, we’ve stuck with our approach of augmenting each of our predictions with a specific priority for leaders to focus on as they attempt to address that prediction. As a result, each priority provides specific guidance as to how to best prepare for, and adapt to, its corresponding prediction.

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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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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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Building the Analytics Organization at Michelin North America

By Robert Morison, Aug 28, 2019

Available to Research & Advisory Network Clients Only

In the context of a major corporate reorganization, the Chairman/President and leadership of Michelin North America recognized the need for an organization dedicated to analytics. Over the course of 2018, the company formed an analytics department with innovative and integrated structure, methods, and values. The department reports centrally, with members sharing identity, purpose, and key objectives. Most of the staff are embedded in the business in cross-functional, autonomous, long-lived “squads” aligned with major processes and business domains. Led by product owners and scrum masters, the squads develop and maintain analytics products rather than executing projects. Communities of practice foster experience sharing and learning across the department. The leadership team and management processes focus on enabling the squads to create business value.

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Mapping an Information Economy

By Doug Mirsky, Aug 16, 2019

Available to Research & Advisory Network Clients Only

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.

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Creating A Data Engineering Culture: What it is, why it’s important, and how, and how not, to build

By Jesse Anderson, Jul 31, 2019

Available to Research & Advisory Network Clients Only

Why do some analytics projects succeed while so many fail? According to Gartner analyst Nick Heudecker, as many as 85% of big data projects fail. However, the ROI from the other 15% that succeed is incredibly promising. With such a clearly high barrier to competency in executing big data strategies, there remains significant opportunity for first-mover advantage for enterprises that can crack the code to improving their outcomes. So, what can organizations do to increase their chances of big data success? Part of the answer lies in creating a data engineering culture. This is the necessary foundation underpinning a big data analytics proficiency and enables companies to outperform the competition.

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