International Institute for Analytics

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?

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 plan to transition to a data lake to generate future business value by enhancing analytics and reporting. Unfortunately, we’re struggling to see how to get this done and the end value.

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?

Inquiry:

We’re finally at a point where we have data scientists within the sales organization who can work on advanced analytics. However, we’re struggling with how to get the business knowledge to the data scientists and move forward. How have you solved this dilemma?

Inquiry:

Internally, we’re struggling with our internal communications plan for analytics. We never get beyond high-level discussions. Can you help us think more specifically about communications for BI reporting and a new advanced analytics mandate?

There is a tendency to jump into something called a “data strategy” without understanding what it is at its core. In IIA’s view, at the center of a data strategy is a schematic of an entity’s information economy1 with in-depth awareness of the constituent needs on the demand side of the economy — the data consumers. Because ultimately the primary purpose of a data strategy is to solve questions about how to improve the availability, timeliness and quality of data, in that order of priority, for the constituencies demanding it.

Inquiry:

I’m charged with driving analytic strategy, starting with formalizing an enterprise analytics function. We have scattered pockets of analytic capability. How do I ease these teams into the new paradigm?