International Institute for Analytics

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.

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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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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?

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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?

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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.

Fusion centers have evolved far beyond their original role in counter-terrorism to safeguarding and serving the public on many fronts. They continue to evolve rapidly as they opportunistically and, in the COVID-19 pandemic, necessarily make their operations more virtual. The core challenges are also changing, but they still revolve around the three imperatives of integrating data, deploying technology, and building trust. Three experts – Juan Colon, National Director of Opioids and Illegal Drug Solutions, SAS Institute, John Gillon, Industry Expert, Public Security, SAS Institute, and Gretchen Stewart, Public Sector Data Science Director, Intel Corporation – share their experience and perspectives on how fusion centers succeed in their missions today and tomorrow.

Inquiry:

We have a project management office (PMO) for our data and analytics team. This is a new role and aligning with the businesses in a more formal way is a new concept for us. Do you have any best practices for working with stakeholders?