International Institute for Analytics

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:

We are working on a data consolidation project in which we’re replacing four old ERP systems with a new ERP system. We’re concerned with managing customer data from the different systems and prepping ourselves for advanced analytics in the future.

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?