Ethics and Safety of Analytics, Marketing Mix Modeling, Cognitive Computing, and more

Andrea Steffes-Tuttle Weekly Roundup

This week, we’ve collected articles that examine the three types of analytics and how they can be used to enhance the customer experience. We also look at the ethics and safety of predictive analytics on a large scale and consider what needs to be done in order for AI to move from experimental to practical in order to be immediately impactful for today’s companies. We learn how Marketing Mix Modeling can be used to determine how to best allocate marketing budgets and plan long term. And finally, we look at how using cognitive computing’s ability to process data quickly can radically transform the way we do things.

Descriptive Analytics, Prescriptive Analytics, and Predictive Analytics For Customer Experience

by Black Morgan, contributing writer for Forbes.com

As your company grows, your customer database grows. This growth can make it difficult to truly understand who these customers are and what they want. But you can get a better idea through the use of a few types of data analytics: descriptive analytics, prescriptive analytics and predictive analytics. Learn what happened, get recommendations for future actions or predict what will happen in the future… all through data. When used together, these three types of data can help you understand your customers and how best to interact with them, creating high-quality, customized experiences. Read on to find out how.

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Can Predictive Analytics Be Made Safe For Humans?

by Danny Crichton, contributing writer for TechCrunch.com

Data ethics is discussed, specifically as it relates to customer privacy. Massive-scale predictive analytics can do a lot of good. But if it isn’t used carefully, it can also do a lot of harm. Dennis Hirsch weighs in on building governance strategies to allow companies to use customer data ethically in order to enjoy the benefits without the negative costs. In order to do so, he focuses on four areas: privacy, manipulation, bias, and procedural unfairness. Additionally, China’s telecom giant Huawei’s expansion is discussed, including what that means for information security.

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Make AI Boring: The Road from Experimental to Practical

by Hilary Mason as commentary for InformationWeek.com

Most headlines focus on the excitement of artificial intelligence and machine learning technologies, fueling hype and mystery. But what if the focus was on understanding the technical reality of what’s actually possible? AI will drive the data age, but only if we make it boring and commonplace. It needs to become practical, repeatable, and scalable in order to drive real business results. We’re witnessing the industrial revolution of AI and in order for it to truly work, it needs to get a lot more boring.

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Three Data Inputs That Significantly Impact Marketing Mix Modeling Effectiveness

by Joshua Kowal, guest author for MarTechAdvisor.com

It’s an age-old challenge. How do you know which marketing efforts are actually driving profits and which are just wasting spend? Enter Marketing Mix Modeling (MMM). It is a marketing measurement approach that can address this challenge and provide marketers with the insights needed to determine how to best allocate their budgets and plan long term.

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The Cognitive Business Disruption

by Ohio University Online Master of Business Administration for Onlinemasters.ohio.edu

Cognitive computing holds the possibility of radical transformation by processing data much faster than humans. This article and infographic navigates the promise of this technology. Learn about the principles of cognitive computing, according to IBM. Understand the factors affecting the adoption of cognitive computing, and how to integrate cognitive computing in business. Learn what they are and how to best approach them.

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