Research

Inquiry Response: Thoughts on Improving Customer Lifetime Value and Churn

By IIA Expert, Ahmer Inam, Apr 20, 2020

Available to Research & Advisory Network Clients Only

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?

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Inquiry Response: Beginning Considerations for Global Supply Chain Analytics

By Mark Molau, IIA Expert, Dec 23, 2019

Available to Research & Advisory Network Clients Only

Inquiry:

We’re a consumer products multinational, and we need to expand our supply chain analytics efforts. What should we be thinking about?

Response:

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2020 ANALYTICS PREDICTIONS AND PRIORITIES

By Thomas H. Davenport, Bill Franks, Drew Smith, Robert Morison, Dec 19, 2019

Each year, the International Institute for Analytics ends the year with a look at the latest analytics trends and the most pressing analytics challenges currently facing organizations. Our predictions are based upon our day-to-day work supporting and advising analytics leaders and organizations. We take advantage of the breadth of expertise and cross-industry perspectives we encounter every day from our clients, partners, and members of the IIA expert network. This is our 10th annual look toward the upcoming year, and our annual Predictions and Priority research brief and the associated webinar have become among IIA’s most popular content of the year. This year, we’ve stuck with our approach of augmenting each of our predictions with a specific priority for leaders to focus on as they attempt to address that prediction. As a result, each priority provides specific guidance as to how to best prepare for, and adapt to, its corresponding prediction.

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The Fuzzy Line Between Good and Evil Data Science

By Bill Franks, Sep 12, 2019

Available to Research & Advisory Network Clients Only

The vast majority of people building analytics and data science processes have every intention of being good and ethical. As a result, most potentially unethical and evil processes arise in situations where that wasn’t the intention. The problem is typically that proper focus and governance is not in place to keep analytics and data science processes on the side of good. On top of that, what is good and what is evil isn’t nearly as clear cut as we’d wish it to be.

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Mapping an Information Economy

By Doug Mirsky, Aug 16, 2019

Available to Research & Advisory Network Clients Only

Information Economies in Organizations

The data warehouse revolution began in 1991 when Bill Inmon published Building the Data Warehouse. Inmon observed, early in that book, that every organization has a naturally occurring information economy, and that most naturally occurring information economies were inefficient, duplicative and prone to produce suboptimal decisions.

This observation of Inmon’s has not gotten anywhere near the credit, or attention, it deserves. A decade’s worth of collective practice in advanced analytics should tell us that everything we know about real-world economies applies to our information economies. There is demand for information by people and functions in an organization, and there is a supply of (some of) that information. There is (some amount) of technical and procedural infrastructure – some kind of market — to bring demand and supply together in an organized way. That “market” infrastructure is often partial, fragile and in some cases ineffective. There are competitive alternatives (like cloud service providers and SaaS vendors), over- and under-regulation (various data governance models), excessive demand-side taxation (cost allocation strategies), failure to invest in infrastructure, and all other elements of economies.

When organizations are planning strategy-driven large-scale advanced analytics programs, they should begin their planning by characterizing their as-is information economy.

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Creating A Data Engineering Culture: What it is, why it’s important, and how, and how not, to build

By Jesse Anderson, Jul 31, 2019

Available to Research & Advisory Network Clients Only

Why do some analytics projects succeed while so many fail? According to Gartner analyst Nick Heudecker, as many as 85% of big data projects fail. However, the ROI from the other 15% that succeed is incredibly promising. With such a clearly high barrier to competency in executing big data strategies, there remains significant opportunity for first-mover advantage for enterprises that can crack the code to improving their outcomes.

So, what can organizations do to increase their chances of big data success? Part of the answer lies in creating a data engineering culture. This is the necessary foundation underpinning a big data analytics proficiency and enables companies to outperform the competition.

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Inquiry Response: Pricing Analytics Function in Retail Organizations

By Mike Gamage, IIA Expert, Jul 01, 2019

Available to Research & Advisory Network Clients Only

Inquiry:

What’s the right approach to pricing as an analytic function within a large retail organization? We refresh our elasticity models every few years, but is that enough?

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It’s No Game to Find and Keep Your Data Scientists

By Zachery Anderson, Jul 01, 2019

Available to Research & Advisory Network Clients Only

Companies face two common challenges in struggling to retain data scientists: They often feel underpaid and they frequently say their company’s analytics platform is behind the times. Those were typical comments at Electronic Arts (EA) in 2013 and 2014, which contributed to a 22% annual voluntary turnover rate among its data science job family.

But over the past five years, EA has steadily reduced its churn rate to 8%. These improvements occurred without major changes in compensation and without significantly upgrading the analytics platform.

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Portland 2019 Analytics Symposium Video: Matt Levinson

By Matt Levinson, Apr 17, 2019

Available to Research & Advisory Network Clients Only

Nike Gets Up and Running With Machine Learning and AI

Embarking on an AI journey starts with executive leadership and strategic vision. It requires alignment of the culture and capabilities. At Nike, the key elements have been business leaders wanting to be data driven, demanding deeper information, and being committed to enabling the organization.

The first step in getting up and running at Nike was unification of data science activities. For example, previous efforts were in silos by product group, such as a running app. This resulted in consumers having multiple Nike digital IDs. Having one ID per person was essential. Also important was unification of reporting so everyone at Nike was looking at the same numbers.

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Nearly 200 of IIA’s clients, analytics experts, and members of the analytics community gathered in Portland, Oregon this week for the spring Analytics Symposium. IIA also hosted its first Women in Analytics networking event, an interactive Analytics Workshop, and introduced two tracks of sessions to bring the most value to attendees. This blog covers key themes of the conference and highlights from each session.

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