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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The DataOps Transformation: 7 Steps To Prevent The Great War of Data Scientists

By Christopher Bergh, Mar 31, 2020

Available to Research & Advisory Network Clients Only

Join Christopher Bergh as he presents the seven steps to get these groups of people working together. These seven steps contain practical, doable steps that can help you achieve data agility through DataOps. This presentation will illustrate how to make changes to big data, models, and visualizations quickly, with high quality, using the tools teams love. We synthesize techniques from DevOps, Deming, and direct experience.

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Big Data and Analytics in the COVID-19 Era

By Jesse Anderson, Mar 26, 2020

Available to Research & Advisory Network Clients Only

Big Data and analytics are going to change in this COVID-19 era. In this Webinar, Jesse Anderson, leading expert in big data and related technologies and techniques, shares what he’s been telling his clients who call and ask, ‘Jesse, with all that’s going on what should we do now?’

Jesse explores a number of topics ranging from the implications of working from home to the tweaks that may be necessary to your current organizational models in the current economic climate.

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CAO Perspectives: Analytics Team Actions in Response to COVID-19

By Doug Hague, Mar 25, 2020

Available to Research & Advisory Network Clients Only

My Immediate Response:

  1. ALL HANDS ON DECK FOR OPERATIONAL REPORTING
    There will be new reporting needs and the frequency of information will need to speed up. Look for options and data that will be impacted by the current crisis. You will need to figure out how to quickly deliver information even if not perfect. Do the best you can, provide any glaring risks. Executives are starving for information. Make sure to provide the information to the most senior people first. Analytics teams can assist operations teams, data science people should even jump in as they have some of the best skills even if they are not using their modeling skills. Work with IT to pull information more quickly. Accept that you will be reporting off of sandboxes that are not production quality, get the exceptions from Risk and IT. You may need to time shift your team to earlier in the day as the executives will want things first thing when they come in if possible. Blend new data sets together to provide insights. Make sure to grab the customer feedback as even simple things like a word cloud may help.

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Inquiry Response: An Enterprise-Wide Agile Transformation

By IIA Expert, Mar 23, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

With our Agile transformation, there’s friction between IT and Analytics regarding project management responsibilities, and with the data scientists who don’t like Agile. How can we implement our Agile transformation to limit friction?

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Inquiry Response: Reconciling Headcount to Build HR Analytics

By IIA Expert, Mar 16, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

Headcount is currently calculated in a number of different ways, and before we can move on to more interesting HR analytics we need to reconcile the headcount numbers across the enterprise. What’s the best way to work through this initial hurdle?

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Analytics Applications Lifecycle Framework

By Doug Mirsky, Mar 02, 2020

Available to Research & Advisory Network Clients Only

Most analytics organizations at large companies do not own the entire lifecycle of their analytics applications. Instead, often-distributed analytical applications teams have to work with their partners in IT/IS organizations, as well as seek requirements, advice and consent from legal, compliance and governance functions within their organization. The essentially distributed nature of these work streams frequently results in inefficiencies or even full-on breakdowns in progressing an analytics application from proof of concept (PoC) to production. Success in distributed environments requires an overt, agreed-upon, stepwise plan along with the emotional intelligence to navigate the necessary conversations with your colleagues to build that plan.

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Inquiry Response: Caveats When Deploying An Automated ML Tool

By IIA Expert, Jan 20, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

We’re planning an enterprise rollout of an automated machine learning (AutoML) tool like DataRobot. What are some of the caveats that we should watch out for?

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A Decade of IIA

By Thomas H. Davenport, Jack Phillips, Jan 08, 2020

Available to Research & Advisory Network Clients Only

We co-founded the International Institute for Analytics in 2010. Since it’s now 2020, our sophisticated math skills tell us that IIA has been around for about a decade—although our first full year of operation was in 2011. We thought it might be interesting to reflect on the state of the field that IIA addresses and how it has changed over time.

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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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