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

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We’re building a team to focus on using machine learning to enhance our end user experience. Many of our analysts aspire to be data scientists, and we would like to fill data scientist roles with the talent we have where possible. How should we delineate roles and responsibilities of a data analyst vs. a data scientist?

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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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Headcount is currently calculated in a number of different ways, and before we can move on to more interesting HR analytics we need to reconcile the headcount numbers across the enterprise. What’s the best way to work through this initial hurdle?

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We’re growing our analytics team from 10 to 25 people in the next year. In addition, we’re transitioning to an Agile approach and aiming toward operationalizing RPA. The current team consists of two data engineers, one project manager, one designer, three BI/visualization analysts, one QA specialist, and two product owners. Do you have any advice that will help ensure the team’s success?

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We’re just getting started with HR analytics, and we’re struggling with building a baseline reporting capability. There is an HRIS team; should HRIS own the analytics? Where should we start with our initial HR analytics projects? We use Workday for human capital management; can we use it for analytics too?

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We’re planning an enterprise rollout of an automated machine learning (AutoML) tool like DataRobot. What are some of the caveats that we should watch out for?

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.

Inquiry:

We just formed a Center of Excellence (CoE) for our data science community. We’re finding ourselves challenged by the translator role. Should we up-skill current members, bring in external people from the business, or not have anyone at all?