Rank #1: 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 #2: 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 #3: 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 #4: 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.
Rank #5: EP12: Business Meeting Data Analytics with Teem, Workplace Analytics Platform
How efficiently is your time used in meetings? If you needed to make decisions on the number of conference rooms you need make on an analysis of scheduled meetings, how accurate would it be? For architectural firms, this is crucial. Are rooms being created that aren’t needed? Think about calendar listings. When the company shuts down a project that held team meetings every Wednesday morning, does anyone bother to delete that meeting? Dal Adamson is a product manager at Teem and he has been trying to fix the meeting management nightmare so companies what they do and don’t. Exactly how much time do we waste in meeting that don't help the company? Listen in to hear the answer.
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: EP3: How Farmer's Insurance Rebranded using Data Analytics
There is little in corporate marketing that is more emotional …. more right brain …. than choosing a new logo. It’s the subliminal messaging from the colors, the shape and everything else that matters. How do executives make that kind of choice without being overly swayed by their own emotions? Mike Linton, the chief marketing officer at the Farmers Insurance Group, thinks he has the answer and it was to let analytics make much of the decision. Well, analytics layered on top of customer surveys and literally hundreds of logo variations. Linton used to be the CMO at eBay and Best Buy—as well as a brand manager at Procter & Gamble. Mike joins us today to talk about how good software is at taking the emotions out of an emotional decision.
Rank #8: EP26: Banking Analytics at Bank of America
Predicting cashflow is certainly common enough, but what about predicting the costs ASSOCIATED with cashflow? What are the specific financial impacts of moving from check to ACH? Should you offer a discount to encourage preferred payment methods? What are the costs of matching invoices and fixing invoice typos? Rodney Gardner, head of global receivables at Bank of America Merrill Lynch, talks banking analytics.
Rank #9: EP5: Improving Patient Satisfaction, RFID Patient Data Analytics
Senior management at the cardiovascular center at the University of Utah had a problem. They knew that patients were unhappy with the service, but they didn’t have enough objective pieces of data to fix it. In an ambitious hospital experiment, they RFID tagged every single person involved. That meant tagging every nurse, doctor, orderly and clerical staff—and, yes, every single patient. These were active RFID tags so the software knew who was standing near who and for how long. They were where they were standing. It gave an objective picture of how many people were waiting for an X-Ray. It found how long injured patients had to wait and how long physicians spent with each patient. It counted how many seconds after a patient alarm sounds and when help arrives. And who arrived last. Steven Tew, the cardiovascular center’s administrator, is running the experiment, tracking 175 patients a day and more than 50 employees. And doing it all with 1,000 active RFID tags, costing about $20 each. Steven joins us today to explore this intriguing analytics goal.
Rank #10: EP7: Facebook Targets Emotions for Advertising
A leaked document showed that Facebook targeted audience members that felt "overwhelmed", "stressed", "anxious", "nervous", "stupid", "silly", "useless", and "failure" with relevant ads that fit their emotional profile. We talk with Ron Guymon, Chief Data Scientist at Numetric about the technical aspects of data science like this (no moral discussions or judgment, just technical).
Rank #11: EP8: The Petabyte Problem - Is More Data Always Better?
There’s an old-school analytics thought that the more data that is analyzed, the more accurate will be the results: the answers, the conclusions, the recommendations. But there’s also the signal to noise ratio issue, which holds that the more irrelevant data that is being examined, the more difficult and time-consuming it is to find those answers. And that brings us to the Petabyte Problem. Today’s companies are collecting exponentially more data than a few years ago. Even worse, much of that data is happening in hidden corners of the company, such as on mobile devices and in the cloud. End result? It can makes analytics far more challenging. Indeed, it can make knowledge management a nightmare. Put simply, companies no longer know what they know—and are therefore condemned to repeatedly and expensively solving the same problems over and over. Michael Stevens is the chief operating officer at AccessData. Michael joins us today to try and figure out a way around the Petabyte Problem.
Rank #12: EP14: Kaiser Permanente Convergent Medical Terminology and Incompatible Datasets
Incompatible databases bedevil every company’s analytics efforts and healthcare companies are no exception. The $60 billion Kaiser Permanente managed care company is exploring whether it’s found a way around those incompatible datasets courtesy of an approach developed at the University of Oxford. It’s dubbed the Resource Description Framework Oxford and nicknamed RDFOX. Alan Abilla is the chief of medical informatics and innovations for the Convergent Medical Terminology team within Kaiser Permanente
Rank #13: EP10: Does Steve Ballmer's USAFacts Actually Help?
When former Microsoft CEO Steve Ballmer rolled out his extensive government database in April, it marked the first time much of that data had been easily available to the public. And for USAFacts, the word "easy" is key. The $10 million effort attacked some 30 years of data from 70 municipal, state and federal agencies. The goal was to make the mountains of information easily accessible by the lay public. In many respects, that's the essence of data analytics. It's about helping users spot the patterns and deviations from a massive amount of numbers. That requires understanding the user—and the questions the user is likely to want to ask—as well as the data. Turning back to Balmer's USAFacts effort, did it in fact make those terabytes of data easily accessible to the public? Or did it needlessly limit that access to a handful of categories that its developers thought up? To figure this out, we're turning to Dave Johansen …. VP of engineering at Numetric.
Rank #14: EP17: Data Analytics to Assess Health of a Customer with Hal Halliday from Infusionsoft
Outsourcing sales and customer support to a third-party is a common tactic for growing companies, but it can be quite unnerving. Are your customers and prospects being properly taken care of? Are your renewal and acquisition rates lower than they would be if your internal people handled it all? In one sense, that’s a classic what-if query. The problem is it’s almost impossible to know what customers and prospects would have done differently with different people. Infusionsoft, a Goldman Sachs backed company, tried an intriguing way to make those projections. They dubbed it a Customer Health Score and it looks at the number of logins, contacts added, tags created, appointments sought and more than a dozen other variables. With us today to better understand this process is Hal Halladay, the chief people officer at InfusionSoft. Hal, how does exploring those metrics tell you what the customer or prospect would have done differently with different people?
Rank #15: EP16: Why Overconfidence in Algorithms is Bad for Data Analytics with Professor Jeff Camm
The dictionary defines optimal as best, as in “the optimal approach is the best approach.” But when it comes to analytics and making the right decision, Jeff Camm argues that optimal is very often far from optimal. Camm is the associate dean of business analytics at the Wake Forest School of Business. Camm points to misplaced confidence in algorithms. In his research, he found that the best analytics recommendation for most enterprises is almost NEVER the choice that is mathematically ideal. Jeff, in a world of almost pure science, how can that be?
Rank #16: EP19: Neiman Marcus Omnichannel Retail Marketing
One of the oldest concepts in omnichannel retail marketing is single view of the customer. That simply means recognizing the shopper as she moves from desktop to mobile device to calling a call center to visiting a physical store. But shoppers share desktop devices with family members and many use multiple mobile devices throughout the day.
Rank #17: EP20: HR Analytics at Memorial Sloan Kettering Cancer Center
When Human Resources executives discuss analytics, topics turn to employee evaluations and various indicators of happiness levels. But for Roy Altman, the manager of HR Analytics for the Memorial Sloan Kettering Cancer Center, he wanted to understand how employees work together. He saw an almost total absence of metrics about how employees mesh together. All of the HR analytics explored employees as individuals, never as part of a group.
Rank #18: EP11: How Jefferson Health Reduced Payer Insurance for Chemotherapy patients from 22 days to 5 days
In the hospital business, few financial arrangements are as critical—and fraught with complexities and red tape—as insurance payments. Other than deep discount on the rates, the biggest headache is payment speed. Yes, say insurance cashflow to any hospital CFO and watch the involuntary shudder. In Philadelphia, executives at Thomas Jefferson University and Jefferson Health, found a way to use analytics to accelerate chemotherapy insurance payment authorizations from 22 days to five days. And this reduction in insurance payment latency also increased the speed of patient care - all through the use of data analytics. Learn how they did it.
Rank #19: EP6: Interoperability, Data Incompatibility, and Impact on Veterans at the US Department of Veteran's Affairs
Nowhere is the proper analysis of data more life-saving—literally—than in healthcare. And identifying and improving patient safety standards is a critical part of the mission of the U.S. Department of Veterans Affairs. The problem: an absolute lack of compatible systems—and, no, interoperability by mapping doesn’t work. Keith Campbell is the director of informatics at the U.S. Department of Veterans Affairs and is been trying to slay the incompatible system monster. To answer these questions, we turn to Keith Campbell, MD, PhD, who is the director of clinical informatics at the United States Department of Veteran's Affairs.
Rank #20: 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. David Dittman is the director of business intelligence and analytics services for Procter & Gamble. Dittman argues that C-levels must start using analytics for more strategic decisions, and they must do it now.