International Institute for Analytics

Inquiry:

We’re working on a new centralized data platform so that we can perform effective analytics with a quicker turnaround. We’d like to verify some aspects of a new architecture that we’re considering. Can you provide feedback?

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:

We’re concerned about potential bias in the data our risk models use for lending decisions because our data is primarily made up of white males. We’re already working on accuracy with respect to different sub-populations. What are we missing?

Inquiry:

We build models in-house to determine lending for people who buy our products. We rely on these algorithms for auto-approvals, and we’re concerned about model bias. How can we address this issue?

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?

Inquiry:

We have a customer loyalty program, and we want to improve our customer lifetime value (CLV) and retention, and also move more customers toward using loyalty cards. What are some interesting techniques and frameworks that could aid our efforts?

Inquiry:

I’m new to the analytics R&D space, and I’m not sure how to manage my new team and the work to ensure that the leadership sees us as valuable. Do you have any suggestions?

Join Christopher Bergh as he presents the seven steps to get these groups of people working together. These seven steps contain practical, doable steps that can help you achieve data agility through DataOps. This presentation will illustrate how to make changes to big data, models, and visualizations quickly, with high quality, using the tools teams love. We synthesize techniques from DevOps, Deming, and direct experience.

Inquiry:

We want to improve our operational analytics efforts starting with high-level skills, pipelining, and structure. We have a hub-and-spoke model and analytics teams in place. What is missing from our internal conversations?

Big Data and analytics are going to change in this COVID-19 era. In this Webinar, Jesse Anderson, leading expert in big data and related technologies and techniques, shares what he’s been telling his clients who call and ask, ‘Jesse, with all that’s going on what should we do now?’

Jesse explores a number of topics ranging from the implications of working from home to the tweaks that may be necessary to your current organizational models in the current economic climate.