International Institute for Analytics

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?

Inquiry:

We’re attempting to upskill our data scientists to instill good coding behavior and the tenets for model testing and system functionality for application delivery. It’s been a challenge. Do you have any suggestions?

Inquiry:

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?

Inquiry:

I’m writing an analytics strategy for digital transformation. We have multiple product lines, but I decided to start with writing the strategy, with use cases and a roadmap, for only one division. Should I include the other product lines too? How much detail should I include?

As with many terms in the analytics space, “self-service” tends to have many meanings, depending on the vendor using the term. Self-service is used to describe both business intelligence and (advanced) analytics, and is frequently co-mingled with a number of other terms, including “data democratization,” “citizen data scientist” and, more recently, “data literacy.”

In general, this collection of terms points in the direction of a conscious strategy to have more employees in an organization, with broader and deeper access to data, use those data sets to make better, more timely data-driven decisions with little or no intervention from a centralized BI or analytics function, or IT professionals.

Inquiry:

I’m in charge of our university partnership group that works on data science projects with affiliated universities. My challenge is the internal coordination piece of it. How can we get meaningfully data-based research questions surfaced in a large organization? And then how do we move them forward? This initiative is the brainchild of the CEO, which also adds a level of complexity.