International Institute for Analytics

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:

We’re developing a data science job matrix that should clearly spell out the career paths within our new data hub. What worked and didn’t work for others?

Inquiry:

We want our supply chain team to expand their efforts beyond demand forecasting. Where are the low-hanging fruits where a centralized advanced analytics team could provide value? Alternatively, is demand forecasting actually a good use of our resources?

Inquiry:

What are some best practices for writing third-party vendor contracts to ensure that we receive the best possible transactional data? One challenge is that we don’t pay for the data we’re receiving.

As companies seek to use advanced analytics to drive improved decision-making, knowing how and when to embed those analytics in business processes, in order to eliminate human variance and interference, becomes a critical skill. Embedded analytics raises a host of questions–how do you know your embedded analytics will make consistently superior decisions when compared to your company’s best decision-makers? And how do you address the naturally-arising concerns decision-makers have about being “replaced” by algorithms? Join Marc as he lays out the issues walks through the process and describes a framework for navigating this difficult transition.

Inquiry:

As a global manufacturing company with factories and distribution centers in many countries we’d like to do more with supply chain analytics. How do we get started?

Several companies have been highly deliberate in the development of their operating models and consider these models to be essential to driving sustained above market performance (1). A couple of the more famous ones have been studied for their unique and effective operating model, including Disney with its “guest focus” and IKEA with its extreme focus on engineering all parts of its value chain to relentlessly reduce costs for lower prices. And digital companies are no different with Alibaba being renowned for its model that is designed to ensure that as much of the transaction (from marketing to purchase, to shipping to payment) stays in the Alibaba ecosystem.

Inquiry:

As a media subscription service, we have a lot of customer data, but are behind the curve when it comes to using it for personalization to improve customer experiences. What should we look at to become more customer-centric?

As with all analytics, People Analytics starts with understanding the problem you’re trying to solve. How do you match the challenges in your people processes to your wider company ambitions, and how do you use analytics to improve those people processes in a way that improves your business? As with all data related to people, whether customers or coworkers, how do you do so in a way that respects privacy?

Join Dr. Tim Munyon, Associate Professor of Management at the University of Tennessee and Managing Partner at Red Castle Human Capital as he shares insights around these questions and more based on his research and client experience focused on people analytics.

Inquiry:

We’re preparing for a digital transformation — shifting from account-centric to customer-centric. The leaders like the idea in spirit, but how do we get them to understand what this really means so we can begin to take real action?