Rank #1: EP1: Emotional Response Data Analytics with IBM Watson
Patrick Gormley, from IBM Watson, shares new research in capturing customer emotional data from biological and physiological responses. Where to store billions of rows of data? How to find meaning in emotional responses from customers? Are all biological and physiological responses important? How to know which ones have commercial value?
Rank #2: EP25: C-Level Analytics at Proctor & Gamble
Large companies today are quite happy to use analytics for a wide range of tactical decisions, such as product assortment and identifying the most efficient distribution channel. But when it comes to bigger strategic decisions, C-level executives are more tentative. Their gut judgments are still their preferred decision-making mechanism. David Dittman is the director of business intelligence and analytics services for Procter & Gamble, which reported $65.1 billion in revenue last year. Dittman argues that C-levels must start using analytics for more strategic decisions, and they must do it now. David, why are so many executives so hesitant to trust analytics for the big decisions?
Rank #3: EP18: Location and Timing Based Marketing Analytics
All day long, mobile devices are being bombarded with shopping offers. Marketing analytics routinely explores which offers are likely to be accepted and which will be ignored. But does it make a difference if that sales pitch arrives when shoppers are sit
Rank #4: EP13: Sales Data Analysis for Sales and Marketing Operations
Nowhere is crunching numbers more revered than in a sales team, with all manner of compensation tied to deals closed and revenue booked. But, bizarrely, sales analytics today often tracks the wrong elements and thereby fuels wrong conclusions. Kevin Styers, the director of sales operations at ShopKeep, has spent years figuring out old sales
Rank #5: EP2: HR Analytics, Uncovering Hiring Bias with Data
The world of human resources and personnel has two very different sides. Half of the operation is data driven and lends itself perfectly to analytics. Envision tens of thousands of resumes pouring in the door …. each with achievements that have to be categorized. …. claims that have to be fact-checked. And then there’s the gut-feeling side. That’s the one where an experienced manager must decide if this person is likely a good fit for the company’s culture. Is the applicant honest? Might they be a perfect fit for a job that they did NOT apply for but a poor fit for the one they did?
Then there are the areas of extreme sensitivity. This is where biases of sexism or racism can play a role. Can analytics do a better job? According to Ben Taylor, the chief data scientist at HireVue, sometimes it can and sometimes it can’t. If the software is programmed by someone with subtle elements of racism or sexism, the analytics can mimic and extend it. What’s the answer? Ben Taylor joins us today to try and figure it out. So, Ben, let’s start with the analytics limit. When IS a human better?
Rank #6: EP15: Data Preparation and Cleansing - Why the Boring Stuff is Important with Barry Devlin
Interview with Barry Devlin, recognized as the Father of the Data Warehouse. In this podcast we discuss the role of data preparation, data cleansing, and the boring mundane stuff and why the boring stuff matters. A lot.
In just about every business analytics project, there are the fun parts and the drudgery parts. Most data scientists want to plunge in with the algorithms and the complex modeling, while rushing through mundane production tasks, such as data cleanliness and data governance. In short, they want to jump to the exploratory areas while blowing past production issues. But, like almost everything else in business, there’s a cost for avoiding the boring details. Barry Devlin, author and consultant with his 9Sight Consulting firm, has been leading in data warehouse work since 1985. Devlin argues that these corner-cutters are increasing how long projects take to get done and their financial cost.
Rank #7: EP9: Designing Data Warehouse for Business Intelligence
Imagine two restaurant chains (one pizza and the other ice cream) that recently had to fully redesign their data warehouse. One of them had more than 30,000 codes for coupons—and there were really only about 21 different coupons. Instead of changing the coding for existing coupons, they kept creating new ones, which were worded SLIGHTLY different. In the end, it made effectiveness and analysis worthless and it cost them thousands of person hours. This is a case where business intelligence wasn’t intelligent at all.
Rank #8: EP22: Using Predictive Analytics for Direct Marketing
Direct Marketing focuses on potential customers that are predicted as likely to buy. But is that in fact the best approach? Is it not a better approach to instead target those who are the most likely to be persuaded to buy? Is the point of a marketing campaign to increase purchases or to change minds—and THEN increase purchases. To figure this out, we have with us Eric Siegel is the founder of the Predictive Analytics World Tradeshow.
Rank #9: EP23: Machine Learning in the Fortune 100
Few things in business analytics have gotten the excitement and, yes, the hype as Machine Learning. It fires up the imagination of every sci-fi loving data scientist, with images of true thinking computers adapting and learning new tasks on their own. The good news is that Machine Learning works and it can indeed do all of that. The bad news is that it can’t do those things as well, as fast or as easily as the hype suggests. There are quite a few things that Machine Learning can do quite well, such as signal identification and noise removal based on both structured and unstructured data. But there is also a list of things that Machine Learning in 2017 CANNOT do, things such as automated complete data cleaning. Another thing Machine Learning can’t yet do is replace humans.
And yet, far too many data scientists today insist on using Machine Learning in areas where it makes little sense. And that frustrates today's guest. Jitendra Papneja has worked in analytics for multiple Fortune 100 companies and today serves as an analytics leader for a Fortune 100 consumer goods company.
Rank #10: EP4: Untangling 4 Electronic Medical Record Systems in 1 Hospital
Today’s typical hospital lives on its paperwork—even as an increasing percentage of that work no longer has anything to do with actual paper. But those electronic patient files—called either EHR for electronic health records or EMR for electronic medical records—bring them their own problems. For the Madison Memorial Hospital in Idaho, the problems come in the form of incompatible systems and difficult data exchanges. At that one hospital, there are at least five different EHR systems—and, no, they don’t talk with each other very well. That means a plethora of headaches for Troy Christensen, the hospital’s Chief Financial Officer. The top headaches? Duplicate bills and invoices that don’t match the contracted amounts.