International Institute for Analytics

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We have extensive loyalty club data and perform extensive membership analytics. What should we be thinking about when it comes to data monetization? What types of data governance are necessary for our current state? Beyond membership analytics, what are some common use cases we should consider focusing on next?

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We are focusing on improving our digital user experience and would like to speak with someone who can help us better understand what data is available for us to collect at this point in time.

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We recently joined enterprise analytics and BI into one team. As we merge into one corporate support function, what should we focus on, especially when it comes to serving business lines and functions that also have embedded analysts?

Tax agencies manage the essential flow of revenue to governments. That core mission has expanded even as their resources remain constrained. And they face the challenges of any major financial services organization – serve customers, operate efficiently, prevent fraud, and adjust to changing economic and social conditions. To explore how data and analytics can assist agencies in meeting those challenges and forwarding their missions, IIA spoke with Shaun Barry, Director of Government Fraud & Security Intelligence Solutions at SAS Institute, and Deborah Pianko, Principal, Government Fraud & Security Intelligence Solutions at SAS Institute.

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We’ve been challenged to identify a monetary value for our predictive models. Do we stick to talking about their potential value or is there another way to approach leaderships concerns about ROI?

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We recently launched a new talent management system and are working to define job descriptions for data analysts, data scientists, and data engineers. We’re struggling with mapping existing roles to new roles and ensuring we’re on the right track with necessary change management that goes with this. Could you help us think through this?

Complying with the Long-Duration Targeted Improvements standard presents major challenges to the business processes and information systems of insurance companies. At the same time, it offers them the opportunity to make rapid progress in modernizing those processes and systems to deliver management insight of unprecedented value. To explore the two sides of the LDTI coin, IIA interviewed Naxine Chang, FSA, MAAA, North America Insurance Strategy Head at SAS Institute.

A recurring theme among analytics and data science leaders is the concern of not being able to keep up with all of the rapid change taking place – both individually and as a team. In years past, it was possible to stand up an organization largely made up of analytics generalists who would handle initiatives end to end. In today’s world, it is necessary to utilize a range of specialists focused on either specific methods or specific points in the lifecycle of an analytics and data science initiative. This talk will discuss the trends driving the need to evolve your organization’s talent model, new roles you need to consider implementing, and how they all fit together.

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We supply propensity models to the marketing team to help predict customer behavior for our various brands. Beyond this, what can our analytics team be doing to support them?

Inquiry:

We’re developing a customer data platform and are challenged by the wide variety of ways our customers interact with us. How are other organizations trying to solve this problem?

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