Research

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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Inquiry Response: Managing an R&D Analytics Team

Apr 13, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

I’m new to the analytics R&D space, and I’m not sure how to manage my new team and the work to ensure that the leadership sees us as valuable. Do you have any suggestions?

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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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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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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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Inquiry Response: Moving Toward Real-Time Responsiveness

Sep 24, 2018

Available to Research & Advisory Network Clients Only

Inquiry:

We’re in the digital entertainment industry, which requires high responsiveness to feedback so that we can continually improve the user experience. What investments can we make that will scale, particularly in terms of supporting real-time analytics?

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IIA 2017 Spring Symposium Event Summary

By Jack Phillips, Apr 13, 2017

Available to Research & Advisory Network Clients Only

IIA hosted its first client-only Symposium of 2017 on March 14, 2017 at the VMware campus in Palo Alto, CA. Over 100 of IIA’s research clients gathered for the Symposium featuring five keynotes and two panel discussions. Given the location in the heart of Silicon Valley, the theme of the Spring Symposium was innovation, disruption, and the growing role of technology in shaping how analytics and data management are executed inside enterprises today.

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Three Paths for Aligning Analytics to Business Strategy

Feb 27, 2017

Available to Research & Advisory Network Clients Only

As organizations strive to build their analytics capabilities, an unexpected challenge has plagued many efforts: The activities of analytics teams and the investments made to support them aren’t in sync with what executives expect or desire. On the surface, it might have seemed straightforward for “business analytics” to be in sync with the business’s strategic needs. After all, the decision to invest in the first place was driven by the business’s needs, right?

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How the Machine Learning of Today is Driving the Artificial Intelligence of Tomorrow

By Andrew Pease, JOSEFIN ROSÉN, Robert Morison, Dec 22, 2016

Available to Research & Advisory Network Clients Only

Machine learning is hot and for good reason. The components — big data, computing power, analytical methods — are in place, and compelling applications are multiplying. To capitalize on the technology, organizations must build experience. They must also proceed pragmatically with one eye on the business and the other on the ethical implications of the algorithms deployed and the decisions automatically made. To explore the opportunities, challenges, and success factors of machine learning today and tomorrow, IIA spoke with Andrew Pease, Principal Business Solutions Manager, Global Technology Practice at SAS Institute and Josefin Rosén, Principal Advisor Analytics, Nordic Government at SAS Institute.

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