Research

Value and Opportunity: An Executive Guide to Procurement Integrity

By Robert Morison, JEN DUNHAM, Laurent Colombant, 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: Tips For Building A People Analytics Capability

By IIA Expert, Jon Agnone, Dec 28, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

We’re new to HR analytics. What kind of projects should we look to tackle to show some initial wins?

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Inquiry Response: Measuring Promotion Success

By IIA Expert, Ashutosh Sanzgiri, Dec 07, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

How can we measure the lift from coupons used and what’s the best way to deploy the models to do it? We know we have lift coming in from various combinations of promotions, but we need a more granular view. Unfortunately, we don’t have access to web data, although we can track the coupons used in our brick-and-mortar stores. What do you think?

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Inquiry Response: Uniting Analytics And Technology Partners

By IIA Expert, Nov 16, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

The analytics team and IT partners team just merged and we’re running into a number of challenges. We have different definitions of what it means to put thing into production, and we’re not communicating cleanly with the business stakeholders. How can we make this work?

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Inquiry Response: Scaling A NLP Model To The Enterprise

By IIA Expert, Nov 09, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

We’ve developed a successful natural language processing (NLP) algorithm and think it would be applicable to other areas of the business too. How should we deploy something like this so we can share it more broadly?

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Inquiry Response: Getting Greater Value From The Team

By IIA Expert, Oct 19, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

We don’t currently have enough resources to tackle advanced analytics products. How can we help our leadership see and understand our needs? Also, how can we become more efficient with our large scrum team?

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Inquiry Response: Maturing Beyond Outlier-Based Fraud Detection

By IIA Expert, Oct 12, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

We’re exploring ways to mature our fraud detection models. At the moment we rely on outlier-based detection, but we don’t have a ton of label data. How can we advance our outlier testing until we generate enough labels to build ML models? What else might you suggest?

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Inquiry Response: Two Types Of Analytics Catalysts

By IIA Expert, Oct 05, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

As a manufacturing company with a new data and analytics operating model. How does the role of analytics catalysts fit into our organization?

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Inquiry Response: Working Towards Machine Learning Products with MLOps

By IIA Expert, Sep 28, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

As we work towards our goal of deploying more machine learning (ML) models into production, how do we position our MLOps needs to the organization? What needs to happen for MLOps to be successful?

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Inquiry Response: Making The Case For A Data Catalogue

By IIA Expert, Sep 21, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

We’re considering buying a data catalog but struggle with the value proposition. What’s the best way to build a business case around purchasing a data catalog? Which vendors should we look at? For implementation, what are we looking at from a time-work effort standpoint?

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