International Institute for Analytics

Inquiry:

With everyone working remote these days, do you have any suggestions for ensuring new employees feel connected to the team? Also, how can we keep our employees engaged in general?

Inquiry:

We are organized in a modified hub-and-spoke way with enterprise data scientists in the hub reporting to the hub, and analysts in the business reporting to the business. In the hub we find it difficult to manage the cross-functional, enterprise-scale projects and the more ad hoc needs of senior leadership.

Inquiry:

I’ve put together a list of responsibilities for the data engineer we’d like to find and hire, and I’d appreciate your advice. I lead analytics for enterprise operations—procurement, manufacturing, engineering—and we plan to migrate data and analytics to Azure.

Inquiry:

We recently launched a new talent management system and are working to define job descriptions for data analysts, data scientists, and data engineers. We’re struggling with mapping existing roles to new roles and ensuring we’re on the right track with necessary change management that goes with this. Could you help us think through this?

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.

Inquiry:

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

Inquiry:

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?

Inquiry:

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

Inquiry:

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?

Inquiry:

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?