International Institute for Analytics

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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We’re in the process of gathering our IoT data into an enterprise data lake to facilitate our advanced analytics efforts. Our main challenges are dealing with proprietary file formats and getting the data ready for the data science team. What would you suggest?

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Our analytics organization is in a bind because we’re ready to scale up a proof-of-concept, but we don’t have the IT structure in place to help with the data mapping, cleansing, and conforming. What can we do?

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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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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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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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Our data science team works alongside a strict Agile data engineering team and we’re finding collaboration to be difficult. Do you have any suggestions?

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We’re finally at a point where we have data scientists within the sales organization who can work on advanced analytics. However, we’re struggling with how to get the business knowledge to the data scientists and move forward. How have you solved this dilemma?

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We are working on a data consolidation project in which we’re replacing four old ERP systems with a new ERP system. We’re concerned with managing customer data from the different systems and prepping ourselves for advanced analytics in the future.

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We’re working on a new centralized data platform so that we can perform effective analytics with a quicker turnaround. We’d like to verify some aspects of a new architecture that we’re considering. Can you provide feedback?