Research

Inquiry Response: Getting Started With Decision Modeling

By IIA Expert, May 21, 2018

Available to Research & Advisory Network Clients Only

Inquiry:

How does decision modeling factor into organizational design and our analytics efforts?

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Inquiry Response: Hiring for Rotational Development Programs

By IIA Expert, May 14, 2018

Available to Research & Advisory Network Clients Only

Inquiry:

We’re starting a rotational development program aimed at master’s level grads in the data science arena. The program is three years long with three one-year rotations in different business areas. We’re struggling with the program’s design and recruiting efforts and are hoping for some insights.

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Inquiry Response: Using Neural Networks for Item Matching

By IIA Expert, May 07, 2018

Available to Research & Advisory Network Clients Only

Inquiry:

We sell our products through retailers, and we’d like to do item matching between our products as we describe them in our brand descriptions and the point-of-sale descriptions used by the retailers, which differ from retailer to retailer.

We achieved 95% accuracy using a support vector machine (SVM) algorithm and were able to increase that to 98%-99% when we tried deep learning—convolutional neural nets. The challenge is that we have huge example sets and wonder how we can achieve scalability to train the set and maintain the accuracy.

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Inquiry Response: Managing Model Maintenance Tasks

By IIA Expert, Apr 23, 2018

Available to Research & Advisory Network Clients Only

Inquiry:

One of our challenges is that once we’ve deployed a model, it must be monitored and fed data. Unfortunately, the important work of model maintenance isn’t seen as a high priority within the enterprise and isn’t seen as a glamorous job by our analytics talent.

Do you have any advice for helping evolve the culture so that model maintenance work is acknowledged as valuable and can attract and retain high quality talent?

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Inquiry Response: Understanding Cybersecurity Roles

By IIA Expert, Apr 16, 2018

Available to Research & Advisory Network Clients Only

Inquiry:

Given the competitiveness in the cybersecurity (CS) space, we want to update our CS job descriptions and compensation to bring them in line with the industry. How should we approach this?

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Inquiry Response: Working the Right Business Problems

By IIA Expert, Apr 09, 2018

Available to Research & Advisory Network Clients Only

Inquiry:

We have our data lake in order so that we can perform more forward-looking analytics to drive business value. Now we’re struggling with how to start and what to prioritize first—any ideas?

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Inquiry Response: Career Development for the Analytics Team

By IIA Expert, Apr 02, 2018

Available to Research & Advisory Network Clients Only

Inquiry:

We’re working on our career development and career paths for our data science and analytics team. We have forty people, and we’re growing. Our concerns include low attrition and the fact that not everyone wants to be a manager.

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Inquiry Response: Planning for Data Governance

By Gregory Nelson, Mar 26, 2018

Available to Research & Advisory Network Clients Only

Inquiry:

We’re implementing a data governance program to support our larger analytics capabilities. What are some of the first steps to start building a data governance strategy? What “gotchas” should we keep an eye out for along the way?

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Inquiry Response: Enterprise Machine Learning Platforms

By Jennifer Prendki, Mar 19, 2018

Available to Research & Advisory Network Clients Only

Inquiry:

We are working on a platform to empower data scientists across the enterprise to build and manage their own machine-learning (ML) models. Do you have any ideas for how to approach this project?

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Inquiry Response: Advice on Risk Appetite Scorecards

By Doug Hague, Mar 12, 2018

Available to Research & Advisory Network Clients Only

Inquiry:

As part of our governance efforts, one of our charters is to look at the risk around data and establish a risk appetite scorecard. We have developed a basic metrics-type scorecard around the traditional areas such as data cleanliness, data accuracy, and integration. What else should we be thinking about?

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