International Institute for Analytics

Inquiry:

We’re looking for guidance about how we can create a set of standards and guidelines for use of systems and tools. Our data scientists and analysts are drifting away from expectations of how aspects of the environment should be used.

Inquiry:

We have a large Agile team working on multiple projects with shared resources. We run one sprint for all of them, with one scrum master. It’s not working well. Is there a way to make it work?

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 have a new centralized R&D team. We’re still in the developmental stage, and we want to build a centralized platform to promote collaboration. We have a new leader and we’re concerned about positioning ourselves for continued support and funding.

Inquiry:

We recently joined enterprise analytics and BI into one team. As we merge into one corporate support function, what should we focus on, especially when it comes to serving business lines and functions that also have embedded analysts?

Inquiry:

We are relying more and more on digital B2B2C to reach our consumers. As we ramp up e-commerce, how can we apply analytics and what kind of investment should we make? What did you do in your company?

Project prioritization is one of those activities that seems simple and straightforward on the surface, but scratch at it just a little and hidden complexities are quickly revealed. While there is general agreement that “good” prioritization contributes to the overall effectiveness of an analytics function, rarely is any effort taken to define “good” and map out an agreed-upon approach to get there. The obvious outcome of a prioritization process that lacks intentionality is general chaos — analytics teams are overwhelmed and expressing a need for more resources, business leaders are frustrated that their needs aren’t being met, the loudest voice often gets his or her way, and the enterprise isn’t optimizing the return on its investment in analytical talent.

Inquiry:

We’re in the process of ingesting data into our cloud data platform. What should we be thinking about? Do we need a central data governance team to make this work?

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?