Research

Inquiry Response: Tips for Small Data Science Teams and Targeted Marketing

By IIA Expert, Ahmer Inam, Aug 24, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

We have a small, centralized data science team that performs machine learning (ML) analysis for marketing insights into our various brands. Our data scientists are overwhelmed with their workloads. Do you have any organizational or Azure architectural tips that could help us to help them? Also, for marketing activities, how can we improve our individual targeting?

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Inquiry Response: Paid Promotion Optimization with Low Price Elasticity

By IIA Expert, Mike Gamage, Aug 10, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

We’re concerned about paid promotion optimization for a brand of products with low price elasticity. Currently we put them on discount when our competitors do. Now we’re wondering if this is the best strategy. How can we improve our data to make better decisions?

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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?

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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

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.

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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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