Enterra Insights

Big Data is a Big Deal in Healthcare

Sharon Terry, President and Chief Executive Officer of Genetic Alliance, asserts, "I find myself becoming increasingly optimistic that we are approaching a tipping point for the consumer movement in health." ["Big Data Is Good for Your Health," Forbes, 1 July 2013] Her optimism is fueled by the fact that supermarkets can provide personalized offers to their customers based on loyalty card and point of sale data. If supermarkets can get personal, Terry believes that healthcare providers certainly should be able to provide personalized service as well. If there is one area where personalization is desirable, healthcare is that sector. Terry insists that personalized healthcare "is a movement that will enable consumers to be more active participants in their own health, gain more personalized care and contribute to the acceleration of clinical research and the quest to ameliorate disease."

Another reason for optimism is that an enormous amount of data already exists in the healthcare sector and that mountain of data grows each day. The following infographic provided by Healthcare IT Connect provides a good overview of the Big Data big picture in the healthcare sector. ["Big Data is a Big Deal," by Zach Urbina, 15 May 2013]

Two things really stood out for me in that infographic. First, 80% of healthcare data is unstructured. That means that natural language processing is essential if any real insights are going to be obtained from the data. Second, I thought that the "6 ways that Big Data can transform healthcare" were particularly enlightening. I suspect that Terry agrees that Big Data will transform healthcare in all those ways. She notes that the healthcare sector has "a great deal of work to be done to catch up to [its] counterparts in the retail industry, but just as advances in online, mobile and social technology forever changed the face of shopping, those technologies, combined with breakthroughs in genetic and molecular science, are fueling unprecedented change in healthcare." Tibco analysts report, "Healthcare organizations are increasingly embracing big data to bolster the quality of care while reducing costs, according to a recent survey of senior level executives." ["Big Data Analytics: The Prescription for Better Patient Care," Trends and Outliers, 27 August 2013] Let's take a closer look at how Big Data can transform healthcare, starting with research.

Support Research: Genomics and Beyond

Dr. Bonnie Feldman writes, "Genomics is making headlines in both academia and the celebrity world. With intense media coverage of Angelina Jolie’s recent double mastectomy after genetic tests revealed that she was predisposed to breast cancer, genetic testing and genomics have been propelled to the front of many more minds. In this new data field, companies are approaching the collection, analysis, and turning of data into usable information from a variety of angles." ["Genomics and the Role of Big Data in Personalizing the Healthcare Experience," The Doctor Weighs In, 14 September 2013] For those unfamiliar with the field of genomics, Feldman explains:

"Genomics is the study of the complete genetic material (genome) of organisms. The field includes sequencing, mapping, and analyzing a wide range of RNA and DNA codes, from viruses and mitochondria to many species across the kingdoms of life. Most pertinent here are intensive efforts to determine the entire DNA sequence of many individual humans in order to map and analyze individual genes and alleles as well as their interactions. The primary goal that drives these efforts is to understand the genetic basis of heritable traits, and especially to understand how genes work in order to prevent or cure diseases. The amount of data being produced by sequencing, mapping, and analyzing genomes propels genomics into the realm of Big Data. Genomics produces huge volumes of data; each human genome has 20,000-25,000 genes comprised of 3 million base pairs. This amounts to 100 gigabytes of data, equivalent to 102,400 photos. Sequencing multiple human genomes would quickly add up to hundreds of petabytes of data, and the data created by analysis of gene interactions multiplies those further."

Feldman goes on to explain that the Holy Grail of medicine is to provide individualized treatments for patients. Perhaps the most transformative result of genomic research will be the prediction and treatment of diseases before they actually affect a patient. Feldman explains:

"Personal genomics – understanding each individual's genome – is a necessary foundation for predictive medicine, which draws on a patient's genetic data to determine the most appropriate treatments. Medicine should accommodate people of different shapes and sizes. By combining sequenced genomic data with other medical data, physicians and researchers can get a better picture of disease in an individual. The vision is that treatments will reflect an individual's illness, and not be a one treatment fits all, as is too often true today."

Terry puts it this way, "With access to more information than ever, consumers can take control of their healthcare in ways never before imagined." Elizabeth Rudd notes, "Health issues are increasingly monitored and recorded electronically creating large amounts of data about an individual's health in the process." ["Big Data- About You," Innovation Management.se, 13 February 2013] Although medical personnel prescribe the use of some of those devices, increasingly individuals are buying equipment to monitor themselves. They are part of group known as the Quantified Self movement. Wikipedia explains the movement this way:

"The Quantified Self[ is a movement to incorporate technology into data acquisition on aspects of a person's daily life in terms of inputs (e.g., food consumed, quality of surrounding air), states (e.g., mood, arousal, blood oxygen levels), and performance (mental and physical). Such self-monitoring and self-sensing, which combines wearable sensors (EEG, ECG, video, etc.) and wearable computing, is also known as lifelogging. Other names for using self-tracking data to improve daily functioning are 'self-tracking', 'auto-analytics', 'body hacking' and 'self-quantifying'."

As the healthcare sector aligns itself better in the Big Data era, all of these devices are likely to become part of the Internet of Things (a massive network that will involve machine-to-machine (M2M) communication). Members of the Quantified Self movement are likely to share their collected data with their primary physician, whose cognitive computing system will automatically track a patient's vital data and alert the physician if something worrisome arises. This kind of M2M monitoring will benefit both the healthy and sick. Terry concludes that each passing day "brings us one day closer to a time when our healthcare is as personalized as our commerce, which will empower all of us to participate more actively in our own health."

Tibco analysts note that, in addition to personalized healthcare, Big Data is transforming healthcare in other ways. "Data analytics," they write, "are being used for revenue cycle management, resource utilization, fraud and abuse prevention, population health management, and quality improvement." The infographic above claims that billions of dollars can be saved by leveraging Big Data analytics. With healthcare costs being a major concern in the U.S., such savings would be a blessing. That's why Big Data is such a big deal in the healthcare sector.

Getting to Know Emerging Markets, Part 2

In Part 1 of this two-part series, I noted that many respected analysts believe that the future of the global economy (and, therefore, the fortunes of many businesses) depends on emerging market countries and the consumers who live there. Many of those same analysts noted, however, that companies that believe they can use a one-size-fits-all strategy for emerging markets are going to be disappointed. Not only do emerging markets have peculiarities that make them unique, consumers living in those markets also have varying taste and lifestyle preferences. The only way to discover those variations and respond correctly to them is through the collection and analysis of Big Data. In this post, I'll discuss recommended strategies for getting to know the consumers in emerging markets and the conditions in which they live.

Boston Consulting Group analysts conclude that "multinational companies have the right priorities — emerging markets are the growth spots of the future — but have not fully put in place winning practices." ["Playing to Win in Emerging Markets," by Amitabh Mall, David C. Michael, Lori Spivey, Andrew Tratz, Bernd Waltermann, and Jeff Walters, bcg.perspectives, 13 September 2013] That's because they don't fully understand the consumers living in areas in which they hope to expand. Mark Harrington, the Chief Marketing Officer at ListenLogic, asserts that Big Data now provides the opportunity to obtain that understanding. ["How Marketers Are Finally Getting to Know Their Customers," Direct Marketing News, 24 July 2013] he writes:

"Having the ability to visualize millions of consumers based on their needs, attitudes, actions, and experiences delivers multidimensional insight to drive critical marketing components, ranging from promotions to product innovation. Marketers can gain deep understanding of what prospects and customers want, need, like, and dislike without ever asking a question. And they can do this on a continual basis to track markets shift in the always-on world."

One of the tools he suggests using is ethnography. Jessica Weber and John Cheng explain that ethnography involves "studying the customs of individuals and cultures." ["Making the Most of Ethnographic Research," UX Magazine, 5 August 2013] They continue:

"Ethnographic research offers several key benefits for defining a long term, multi-channel [user experience] strategy, including:

• Identifying user needs that have yet to be met
• Testing market demand for products that do not exist
• Providing a holistic view of a problem space
• Exposing opportunities for competitive differentiation

"The principal advantage of ethnographic methods is the ability to see the impact of the physical world on factors that could drive digital design. Ethnographic research is all about discovery of the unknown — disproving assumptions about user behavior and uncovering unexpected insights. Whenever you're in the field, something you see is going to surprise you, and those surprises are almost always at the root of innovation."

In previous posts, I've noted that providing a positive user experience at every touchpoint in a consumer's digital path to purchase is important because it is so easy to opt out of a purchase decision at any given moment. Weber and Cheng add:

"The user experience can be thought of as a composite of the user, the interface, and the context; context being an amalgamation of environment and situation. In ethnography, research is conducted in the field, where users' real-world behaviors and interactions with products and services take place, so that researchers can gain insight into how context impacts the user experiences."

Ethnographic research can also take advantage of Big Data and the refined segmentation that it can provide. Weber and Cheng conclude, "To design and develop optimal user experiences, companies must answer the right questions at the right time." Big Data that is analyzed by a cognitive computer system can actually help discover some pertinent questions that might have been overlooked and test hypotheses about them. Jodie Sangster, CEO of the Association for Data-Driven Marketing and Advertising in Australia, believes that global marketing in the future is going to be data driven. ["Where Is Global Marketing Going?" Direct Marketing News, 26 August 2013] "The future of all marketing and advertising around the globe is data-driven," she writes, "and at last, the value of measurable, accountable, customer-centric marketing has been realized." Nevertheless, Sangster agrees with Boston Consulting Group analysts that companies are still grappling with winning strategies. She explains:

"As organizations prepare themselves to ride the Big Data wave, most businesses are still struggling to centralize, analyse, and commercialize their own small data sets. It's not a local issue and it's not a new one, either: The IBM Global CMO Study first identified this struggle as the number one issue keeping CMOs worldwide awake at night in 2011. Another study released in Asia Pacific last year 2012 noted that Down Under, more than 50% of organizations felt ill equipped to grapple with the challenge of how to retrieve the value that's locked away in their data. This will continue to be the case for many more years."

Erin Haselkorn reminds us that "collecting data and analyzing it to find meaningful conclusions has always been part of how marketers go about connecting with consumers." However, with the advent of Big Data technologies, "their strategies have improved dramatically over time." ["Data helps marketers move beyond general stereotypes," Marketing Forward, 10 September 2013] As a result, he writes, marketing teams have been able to "transition away from broad stereotyping toward better targeted forms of data mining. ... We now have the capability to zoom in on the specific customer."

Although most new global middle class consumers are found in Asia, "the middle class in Latin America and the Caribbean region grew by 50 percent over the past decade." ["World Bank: Middle class grows by 50 percent in Latin America, Caribbean region," Fox News, 13 November 2013] It doesn't take a data scientist to know that tastes and lifestyles vary greatly in Latin American, Asia, and Africa — the three areas experiencing the greatest middle class growth. Regardless of where consumers are located, local conditions and culture will play a major role in their purchasing behavior. McKinsey analysts Maria Valdeviesa de Uster, Jon Vander Ark, and Wesley Walden assert that unless companies learn how to "act like a local" they will never succeed in emerging markets. ["Act like a local: How to sell in emerging markets," McKinsey & Company, September 2012] They offer three "imperatives" that will help companies "accelerate growth in emerging markets." They are:

1. "Get on the ground. Information on customers and the market is often hard to obtain. Successful companies invest in all the data sources and expert information available, but nothing beats getting a firsthand sense of how the market works by visiting local areas and resellers. This ground-level view also gives sales leaders a clear read of where the market is heading and lets them plan for it.


2. "Overinvest in the right partners. In developed markets, a company may have many capable potential partners. In emerging markets, finding a partner is a much more strategic endeavor. With limited choice, partnerships are for the long haul, which means companies must find the right capabilities and partners that share their values.


3. "Build talent for the long term. Annual growth in emerging markets can exceed 10 percent. That pace requires sales leaders to think creatively about how they will attract and retain the talent they will need to keep up."

Although I don't disagree with the "boots on the ground" imperative, I can't help but observe that the emerging world's embrace of mobile technologies means that every day more data becomes available for analysis. That data doesn't necessarily need to be analyzed in-country. And, I as noted in Part 1 of this series, emerging markets are likely to have large virtual marketplaces to accommodate this phenomenon. In addition to the fact that smartphone use is increasing, another reason that the number of virtual marketplaces is growing is that infrastructure in many emerging market countries is lacking or sub-standard. That makes building and supplying brick-and-mortar stores more challenging. Lack of infrastructure is one of the many challenges identified by McKinsey analysts. They conclude:

"Emerging-market infrastructure is often less developed, channels are fragmented, and cultural preferences often more complex and varied. Demand can be unpredictable, making the near-term return on sales investment uncertain, even if long-term growth prospects are extremely attractive."

Some of that uncertainty can be reduced by good data collection and analysis. One thing that most analysts can probably agree upon is that companies that get a late start in getting to know emerging markets are going to have a tougher time cracking those markets in the years ahead.

Getting to Know Emerging Markets, Part 1

"Emerging markets are more important than ever," according to Boston Consulting Group analysts, "and they make up a large share of many multinational companies' revenues and growth." Despite the importance of emerging markets, the analysts conclude that "multinationals have not mastered these markets." They reach this assessment based on a BCG survey that included "more than 150 top executives in multinational companies." Seventy-eight percent of the executives "expect to gain share in these markets"; but, by their own admission, "only 13 percent are confident that they can take on local competitors." ["Playing to Win in Emerging Markets," by Amitabh Mall, David C. Michael, Lori Spivey, Andrew Tratz, Bernd Waltermann, and Jeff Walters, bcg.perspectives, 13 September 2013] Trepidation about being able to take on local competitors likely comes from the fact that they understand that local competitors know a lot more about their consumers than the multinationals. The analysts note, for example: "On average, multinationals told us that only 9 percent of their top 20 executives are based in emerging markets; the vast majority of them are still located in mature markets far away."

McKinsey & Company analysts believe that any company that doesn't understand local conditions should be concerned about its ability to succeed in that market. "Emerging markets can be fertile ground for enormous sales growth," they write, "but each market has its own unique hurdles. Without a deep understanding of the local customer you are likely to trip over those obstacles — or abandon the market prematurely. ... To break into emerging markets and capture the potential, the best sales leaders have realized they have to think like a local." ["Act like a local: How to sell in emerging markets," by Maria Valdeviesa de Uster, Jon Vander Ark, and Wesley Walden, McKinsey & Company, September 2012] Understanding local conditions, culture, tastes, lifestyles, and preferences is obviously important for businesses; but, "deep understanding" can only be achieved through the use of Big Data analytics.

To make their point, the McKinsey analysts relate an apocryphal story about two sales people from a multinational apparel company who were sent into an emerging market to determine local conditions. "After scouting a few villages, the first salesman rang the head office. 'I'm returning on the next flight,' he said. 'We can't sell shoes here. Everybody goes barefoot.' Meanwhile, the second salesman was busy e-mailing the head of sales: 'The prospects are unlimited. Nobody wears shoes here!'" Which salesperson was correct in his assessment? Obviously, a lot more research and analysis was required to make that call.

The McKinsey analysts note: "Multinational corporations often make the mistake of importing approaches that work at home without making any adjustments." That could be a fatal mistake. On the other hand, they note: "Local players often underestimate both the resources and speed required to match market needs and compete with global players." In other words, both risks and opportunities are available in emerging markets. Only good analytics are going to help you turn a risk into an opportunity. In the past, only a "boots on the ground" approach was available to companies wanting to learn more about consumers in emerging markets. Recently, however, emerging market consumers have embraced mobile technologies, particularly smartphones, as their way to connect to the world. This connectivity means that mounds of data is now available for analysis. Mark Harrington, the Chief Marketing Officer at ListenLogic, writes:

"Knowing the customer' has been a battle cry for marketing for the past few decades. The difference today is that marketers can actually know their customers, without guessing, simply by leveraging digital ethnography to listen to what they're saying." ["How Marketers Are Finally Getting to Know Their Customers," Direct Marketing News, 24 July 2013]

That is true for both mature and emerging market consumers. Harrington continues:

"As social media expands exponentially, the massive volumes, incredible variety, and never-ending velocity of billions of daily discussions have become a textbook example of Big Data. As such, to harness the wealth of understanding from it — ranging from consumer insights to competitive intelligence — marketing, product, and brand teams are turning to advanced processing technology and complex concept models in place of first-generation monitoring tools and keyword lists. Brands are also relying on social media command centers to instantly distill relevant information and discover genuine insight within the mass of useless noise."

Jessica Weber and John Cheng agree with Harrington that leveraging ethnography is becoming an increasingly important tool in understanding customers. For a company going into an emerging market, I daresay it's an essential tool. Weber and Cheng write:

"Business stakeholders who are accountable for digital product innovation, strategic roadmaps, or multi-channel user experience are increasingly looking to ethnographic research. Although this research tool has its origins in 18th century anthropology — studying the customs of individuals and cultures — its application to technology innovation has never been more relevant. Applying ethnographic methods to digital experiences can yield myriad benefits that go beyond simply validating that something works or identifying opportunities for improvement." ["Making the Most of Ethnographic Research," UX Magazine, 5 August 2013]

Even though each emerging market has characteristics that make it unique, Jodie Sangster, CEO of the Association for Data-Driven Marketing and Advertising in Australia, asserts, "No matter where you are in the world, all marketers face the same issues to one degree or another." ["Where Is Global Marketing Going?" Direct Marketing News, 26 August 2013] She concludes:

"The future of all marketing and advertising around the globe is data-driven and at last, the value of measurable, accountable, customer-centric marketing has been realized. Although in the developing world there is still a focus on mainstream advertising, but with the advent of social media, increasing adoption of smartphones, and a developing multiscreen culture has resulted in a monumental shift in both thinking and marketing spend towards data-driven channels and techniques."

If you want a peek at where emerging markets are heading, look at a China. McKinsey analysts Elsie Chang, Yougang Chen, and Richard Dobbs, report, "Almost overnight, China has become the world's second-largest e-tail market, with estimates as high as $210 billion for revenues in 2012 and a compound annual growth rate of 120 percent since 2003." ["China’s e-tail revolution," McKinsey & Company, March 2013] They continue:

"Some 90 percent of Chinese electronic retailing occurs on virtual marketplaces — sprawling e-commerce platforms where manufacturers, large and small retailers, and individuals offer products and services to consumers through online storefronts on megasites analogous to eBay or Amazon Marketplace."

I believe that other emerging markets will follow this pattern because the infrastructure to support brick-and-mortar stores is expensive to build and infrastructure isn't exactly a strong suit in most emerging market countries. The good news is that all that digital activity means that there is going to be more and more data to analyze and more opportunities to get to know emerging market consumers better. If Pascal Lamy, Director-General of the World Trade Organization, is correct, emerging markets are going to be the future's movers and shakers. ["Emerging economies: 'shapers and makers' in changing landscape, Lamy tells Turkish University," World Trade Organization, 14 March 2013] He also made another interesting point. He stated:

"We are also seeing new trends in the way that goods and services are produced and traded. In essence there is a new narrative developing on trade which governments and businesses have to take notice of, and align their policies and priorities to. In WTO jargon, we have termed this 'Made in the World'. Increasingly, countries are trading in intermediates not final products. The concept of 'Made in Country X' is becoming obsolete. The old mercantalist adage of 'imports are bad and exports are good' is made irrelevant when we look at the evidence — today almost 60% of trade in goods is in intermediates and the average import content of exports is around 40%."

Although I have painted a rather rosy picture of emerging markets, there are challenges (in some cases, significant challenges) that must be addressed in emerging markets. Big Data analytics can help companies assess the risks associated with those challenges. In the second part of this series, I'll discuss how Big Data analytics can help companies gain the insights they need to be successful in emerging markets.

Do You Know Who Your "Influentials" Are?

"Online influentials who provide sole or 'exclusive' influence over consumers are the most valuable to companies," reports Robert Berkman. ["Valuing Influentials Means More than Just Counting Connections," MIT Sloan Management Review, 10 July 2013] Berkman makes that assertion based on "the finding from research conducted by Zsolt Katona, [an] assistant professor at the Haas School of Business, UC Berkeley." Berkman continues:

"Conventional wisdom, reflected by influence-ranking sites such as Klout, is primarily to count the number of a person's connections to assess his or her ability to influence others. But Katona's research has discovered that determining the value of a particular influencer is more complex, and that finding the value of an influencer depends on several underlying factors in the network structure of that individual with the target set of consumers."

The study's results sound similar to Malcolm Gladwell's conclusion that "the success of any kind of social epidemic is heavily dependent on the involvement of people with a particular and rare set of social gifts." He calls this "the Law of the Few." [The Tipping Point] Duncan Watts, a network-theory scientist, disagrees with Gladwell's conclusion about the Law of the Few. Watts insists that influentials play "no special role in trends at all." ["Is the Tipping Point Toast?" by Clive Thompson, Fast Company, February 2008] Mark Borden describes the debate this way:

"There has long been debate about what, precisely, defines influence. Dale Carnegie, author of How to Win Friends and Influence People, described it as the ability to arouse enthusiasm in others to change their behavior. Tipping Point author Malcolm Gladwell says it derives from identifying new trends and sharing them through connected networks. Network theory scientist Duncan Watts says targeting influencers is wasted effort, that starting a trend is essentially a random act." ["The New Faces of Social Media," Fast Company, 25 October 2010]

So who is correct? The folks at Crowdtap believe that Watts is the "knockout" winner. They "found that when it comes to purchasing decisions, the most influential recommendations come from people we actually know, either through the web or in person." ["Who Are the Real Online Influencers?" by Josh Catone, Mashable, 13 June 2012] Catone's article was accompanied by the following Crowdtap infographic.

Katona's study, which is entitled Competing for Influencers in a Social Network, concluded, "The most valuable person influences consumers who are not influenced by any other influentials online. A company that wants to invest time or resources in cultivating an influential person online should focus on those where the target consumers are being influenced in that product/service arena only by that person and not by anyone else." Do such influencers really exist? The study concedes, "Often, though, an influencer does not have an exclusive relationship with a set of target consumers. In these 'non exclusive' relationships, influencers who are most valuable are those where the sought after consumer/set of consumers has a very small set of additional competing influencers." Berkman reports that there was an interesting twist discovered during the study. "The next most valuable influentials are those who influence target consumers who have a very high number of additional competing influencers. ... The reason: if the target consumer has a high degree of additional competing influencers, a company will not need to invest as much money or resources to reach those consumers because they already have so many incoming connections informing them about all the options and they will buy the product that is best for them regardless of the firm’s efforts."

Berkman reports that those with "a middle range of additional competing influencers" were the least influential. Katona told Berkman that high, medium, and small were relative terms that "depended on the specific parameters set up by his model." Berkman then asks the $64,000 question: "What, then, are the practical implications of this study for social media marketers?" He concludes:

"The most important one is that firms should not just look at the number of connections an influential has when determining how many resources to devote to that person. Instead, they need to find out what the target consumer market's relationship is to the influential by taking into account their connections with any other competing influencers. Although widely available customer-influence network analyses sites and services like Klout do not take into account competing influencers and this overall network structures of influentials, Katona says that some telecom firms are now analyzing the connections between their customers and their competitor's customers on their own, and he says that it won't be long until other firms begin doing these analyses as well."

Jose Capelo is one pundit who believes in the power of influentials. He writes, "Social media influencers are fundamental to drive awareness and popularity for brands." He defines an influential as "a person or group of people, usually experts in a particular subject, which have gained recognition and credibility through their actions on Social Media. These actions include: frequent posts and of good quality, say what they think, and overall exerting personal persuasion. The influence is achieved by adding a personal brand, trust and experience." ["Importance of Social Media Influencers," Marketing Query, 18 July 2013] Russ Henneberry is another author who believes that some people have more online influence than others. He reports, "A study from Forrester Research confirms that 13.4% of U.S. adults online create 80% of the content that influences people. And 6.2% of these web users are responsible for 80% of the influence in social media." ["How to Find Influential People With Social Media," Social Media Examiner, 17 October 2012] Of course, not everyone who is in the 6.2% group of influencers is going to have influence in the same area. So, for any one industry or market, that number is considerably smaller. Henneberry asserts that "there are tools and processes to help you reduce the amount of noise in social media. You can use these to concentrate on the key influencers who can move the needle for your business. The goal is to find these key influencers and create a filter that allows you to communicate with them. This helps you develop a positive relationship with the influencers, which can grow your business."

Clearly, some consumers are influenced by "trusted strangers" they follow online. Although I tend to agree with Watts that the closer a consumer is to an influencer (e.g., a family member, friend, or associate) the greater the influence that person has on the consumer.

Getting a Handle on Returned Merchandise

Retailers and manufacturers never like to see products returned; but, it happens. Evan Schuman contends, "One of the worst parts about managing retail businesses is dealing with unknown future returns." ["Can Some Returns Be Predicted And The Associated Inventory/Revenue Impact Flagged?" Storefront Backtalk, 29 February 2012] The editorial staff at SupplyChainBrain adds, "Returns are a headache for any company that has to deal with them." ["How to Improve ROI on Product Returns," 15 December 2011] There are two principal reasons that products are returned: first, they are broken or defective; second, the customer changes his/her mind.

Concerning the first type of returned merchandise, the SCB staff writes, "Determining the fate of broken, defective or obsolete items while attending to the accompanying paperwork can be a costly and time-consuming process." Linda Olster explains why this so. She writes, "Throw in replacements, repairs, sending empty boxes for the return, getting the return back from the customer (to the right location), processing any credits, keeping track of where your inventory is … and all of sudden, you realize the process is not so simple or straightforward." ["Managing Returns: How a Transportation Management System Can Streamline the Process for You and Your Customers," Logistics Viewpoints, 2 February 2012] She goes on to explain how a good "Transportation Management System (TMS) can help to streamline the process and help you gain some unexpected 'returns' along the way." Pun intended, I'm sure.

Concerning the second type of return, Schuman believes that Big Data analysis can help predict which products will likely be returned with greater accuracy and confidence. For example, he writes that "a customer who purchases three of the identical shoe, but each one in a slightly different size" can be predicted, with a high degree of accuracy, to return at least two of the pair of shoes. He also believes "that any purchases from a customer who has a history of high returns" will likely be returned.

According to Associated Press reporter Jennifer C. Kerr, "Each year, consumers return about $264 billion worth of merchandise, or almost 9 percent of total sales, according to industry estimates. Many buyers aren't aware that some returns, with and without receipts, are being monitored at stores that outsource that information to a third-party company, which creates a 'return profile' that catalogs and analyzes the customer's returns at the store." ["Serial returners, beware: Retailers are tracking you," Today Money, 12 August 2013] As noted above, dealing with returns can be a costly and complex challenge. Kerr reports that tracking returns helps stores keep prices down for the majority of consumers by identifying "chronic returners or gangs of thieves trying to make off with high-end products that are returned later for store credit." Kerr also reports, "consumer advocates don't like it" (i.e., they don't like the fact that return histories are tracked).

The editorial staff at Consumer Reports ShopSmart magazine report that retailers track return data "to stop folks who steal, use, or wear items and then bring them back." ["What's the deal with ... The returns blacklist," July 2013] This kind of abusive and illegal behavior cost retailers "an estimated $8.9 billion in losses last year, according to the National Retail Federation." In addition to tracking returns, the magazine reports that "some stores now require identification for returns as a theft deterrent." The Federation claims that "62 per cent of U.S. retailers have a similar policy." ["Beware serial returners! How retailers are joining forces to catch out shoppers who bring back too many purchases," The Daily Mail, 21 November 2012]

The biggest player in the returns tracking field is a company called The Retail Equation. Kerr explains how the tracking process works by describing a Best Buy use case:

"—A consumer buys an item at Best Buy and later returns it. Even if the shopper has the original receipt and is within the time frame when returns are permitted, store policy requires that Smith provide a photo ID, such as a driver's license. Other stores, such as Home Depot, only require the ID if there's no receipt or if the item was purchased with a store credit.

"—The ID is swiped and then some information from the transaction is sent by the store to The Retail Equation. The company says the information captured from the ID typically includes the identification number, name, address, date of birth and expiration date.

"—The Retail Equation catalogs return activity by the shopper and creates a 'return activity report' on him with his returns at the store. If The Retail Equation determines that there's a pattern of questionable returns that suggests potential fraud, it would notify Best Buy, which could then deny returns by that shopper at the store for a period of time.

"The threshold for too many returns is determined by each retailer. The Retail Equation says the vast majority of returns — about 99 percent — are accepted."

SmartShop reports that The Retail Equation "doesn't aggregate information across retailers, report to credit bureaus, or accuse customers of theft or fraud." According to Kit Yarrow, a professor of psychology and marketing at Golden Gate University in San Francisco, even though retailers are seeing an increase in returns — "up 19 percent from 2007" — not all of the increase can be accounted for by fraud or serial returners. She claims that consumers are becoming "pickier shoppers" and are making more impulsive "purchases [of] tempting bargains. That means more returns." ["The Why Behind the Buy," Psychology Today, 4 September 2012] Nevertheless, Yarrow notes that returns are becoming an increasing problem for merchants. "For every dollar spent today, nine cents is returned. Consumers have a bolder mentality and higher expectations when it comes to returns."

According to Yarrow, the challenge of how to deal with returns has placed merchants in a conundrum. She explains:

"New procedures and policies [for dealing with returns] can feel confusing, even maddening, to shoppers. Authenticity, transparency and 'living up to promises' are important values to consumers. Retailers use imagery, emotion and symbolism to craft an enticing image — which becomes the personality of the store. That image is an unspoken promise of a particular type of shopping experience. It's the retailer’s job to ensure that every consumer touchpoint lives up to the promise of a store's image. The return — that moment when the customer effectively tells the store their sales transaction was a failure, that they found something better, or a better price — is an authenticity test. And according to my research, one that retailers often fail. When that happens, shoppers feel tricked."

It's easy to see how retailers must walk a fine line between trying to foster customer loyalty and protect themselves from fraud. A blogger calling herself Jeannieinabottle, provides a pretty good account of why stores dislike chronic returners. She writes:

"If you return items to the same store more than twice a month, you are a chronic returner. No one likes a chronic returner. First of all, it ruins the sales tracking system. If you make a major return and do not buy something to replace it, the numbers take a huge tumble in the system. That looks horrible for the sales associates and managers working at that time. Even if you do an exchange, imagine all the effort that went into the whole transaction just to break even? It is a frustrating process for everyone involved. I can't imagine why customers want to constantly take part in this process on a regular basis. If you are a compulsive shopper and feel guilty as soon as you get your purchases home, then seek some help. The endless cycle of shopping and returning is not helping you or the stores where you shop. If you never try the clothes on at the store, but instead take them home, you need to find a way to either A) successfully evaluate your size, or B) start using the dressing rooms. Also, it helps to make sure you want the product. Don't just make the purchase thinking you can just take it back if you change your mind. You should not be so indecisive that you are making returns constantly. I do understand that we all have to return items here and there. I just had to return a vacuum that broke after using it only two months. I know these things happen. Most managers and sales associates fully understand that. Even if a return is time consuming, it is still a necessary retail evil. However, when customers abuse the system and return items all the time, that is a real nuisance." ["Are You an Annoying Customer?" HubPages, 10 May 2013]

There are no silver bullet solutions to the returns problem. Both merchants and consumers have a role to play if returns policies are to remain reasonable and fair. With billions of dollars at stake, you know that retailers are going to protect themselves as much as they can from fraud and abuse.

Big Data Continues to Raise Privacy Concerns

"Every step in the big data pipeline is raising concerns," writes Cynthia Dwork, Principal Researcher at Microsoft Research, and Deirdre K. Mulligan, an is Assistant Professor of School of Information at Berkeley Law. ["It's Not Privacy, and It's Not Fair," Stanford Law Review, 3 September 2013] Those concerns include: "The privacy implications of amassing, connecting, and using personal information, the implicit and explicit biases embedded in both datasets and algorithms, and the individual and societal consequences of the resulting classifications and segmentation." They continue:

"Although the concerns are wide ranging and complex, the discussion and proposed solutions often loop back to privacy and transparency — specifically, establishing individual control over personal information, and requiring entities to provide some transparency into personal profiles and algorithms."

In the same issue of the Stanford Law Review, Jules Polonetsky, Co-Chair and Director of the Future of Privacy Forum, and Omer Tene, an Associate Professor at the College of Management Haim Striks School of Law, ask, "How should privacy risks be weighed against big data rewards?" ["Privacy and Big Data," Stanford Law Review, 3 September 2013] They continue:

"Big data creates tremendous opportunity for the world economy not only in the field of national security, but also in areas ranging from marketing and credit risk analysis to medical research and urban planning. At the same time, the extraordinary benefits of big data are tempered by concerns over privacy and data protection. Privacy advocates are concerned that the advances of the data ecosystem will upend the power relationships between government, business, and individuals, and lead to racial or other profiling, discrimination, over-criminalization, and other restricted freedoms. Finding the right balance between privacy risks and big data rewards may very well be the biggest public policy challenge of our time."

In a third article from that same issue, Joseph W. Jerome, a Legal and Policy Fellow at the Future of Privacy Forum, writes, "Big data is transforming individual privacy — and not in equal ways for all." ["Buying and Selling Privacy," Stanford Law Review, 3 September 2013] Eric Markowitz adds, "As the public grows more skeptical of data collection, digital privacy advocates finally find themselves in the spotlight — a position they've been craving for years. On the flip-side, tech companies have been dragged under their own spotlight — albeit one with a more critical hue. Now more than ever, people want to know: What, exactly, are you doing with all of their data?" ["The Data Privacy Debate Is Just Beginning," Inc., 21 June 2013] I agree with Markowitz that the data privacy debate is just beginning and that should worry a lot of companies.

Dwork and Mulligan believe that current efforts to protect consumer privacy "fail to address concerns with the classifications and segmentation produced by big data analysis." They explain:

"At worst, privacy solutions can hinder efforts to identify classifications that unintentionally produce objectionable outcomes — for example, differential treatment that tracks race or gender — by limiting the availability of data about such attributes. For example, a system that determined whether to offer individuals a discount on a purchase based on a seemingly innocuous array of variables being positive ('shops for free weights and men's shirts') would in fact routinely offer discounts to men but not women. To avoid unintentionally encoding such an outcome, one would need to know that men and women arrayed differently along this set of dimensions. Protecting against this sort of discriminatory impact is advanced by data about legally protected statuses, since the ability to both build systems to avoid it and detect systems that encode it turns on statistics. ... Rooting out biases and blind spots in big data depends on our ability to constrain, understand, and test the systems that use such data to shape information, experiences, and opportunities."

Polonetsky and Tene have different concerns. They believe "the current privacy debate methodologically explores the risks presented by big data, [but] it fails to untangle commensurate benefits, treating them as a hodgepodge of individual, business, and government interests." They go on to detail the benefits of big data across a number of domains. Jeff Bertolucci reports that the software industry shares the concerns highlighted by Polonetsky and Tene. "The Software & Information Industry Association (SIIA) wants policymakers to avoid 'broad policies' that limit the use of digital information," he writes. "While acknowledging the need to address privacy concerns, the software trade group says that stringent regulations in this area might slow the growth of the 'nascent technological and economic revolution' known as big data." ["Don't Let Privacy Fears Stifle Big Data, SIIA Urges," InformationWeek, 3 June 2013] He continues:

"The SIIA argues that big data is too important to the global economy to do otherwise. It points to recent Gartner estimates that predict big data-related research will spur $34 billion in IT spending this year. Looking forward, 'data-driven innovation,' or DDI, will help create 4.4 million IT jobs globally by 2015, including 1.9 million in the United States, Gartner says. Data collection and use is at crossroads, and decisions by policymakers could have an enormous impact on American innovation, jobs and economic growth,' said SIIA president Ken Wasch in a statement. Lawmakers must address privacy concerns regarding the storage and use of data, Wasch concedes, but he adds that they should do so 'without strict regulation that stifles economic opportunity.'"

It won't be easy to find the right balance between big data and privacy hoped for by Wasch. In the end, no one is going to be happy with any policy that is enacted. The SIIA believes that "policymakers should recognize that 'socially acceptable norms of privacy' are changing with technology. These changes should influence policy decisions pertaining to DDI." There is evidence that privacy norms are changing, especially among younger generations (but, that's the topic of another post). Nevertheless, even younger generations have concerns about privacy.

Some pundits believe that we are engaging in the privacy debate too late. They insist that privacy rights were abrogated years ago. For example, Rob Norman, Chief Digital Officer, GroupM Global, told conference participants in New Delhi that we now live in an Orwellian world. In the future, he told the audience, "Privacy will be redundant." ["The ‘privacy’ threat is real, digital marketers be aware," by Noor Fathima Warsia, Digital Market Asia, 30 May 2013] Warsia reports, "Norman was echoing Sun Microsystems, Chief Executive, Scott McNealy's words, famously said way back in 1999, during a product launch, 'You have zero privacy anyway, get over it.'"

Most analysts, however, don't seem to think that it's too late to address privacy issues surrounding big data. For example, Jerome concludes:

"If we intend for our economic and legal frameworks to shift from data collection to use, it is essential to begin the conversation about what sort of uses we want to take off the table. Certain instances of price discrimination or adverse employment decisions are an easy place to start, but we ought to also focus on how data uses will impact different social classes. Our big data economy needs to be developed such that it promotes not only a sphere of privacy, but also the rules of civility that are essential for social cohesion and broad-based equality. If the practical challenges facing average people are not considered, big data will push against efforts to promote social equality. Instead, we will be categorized and classified every which way, and only the highest high value of those categories will experience the best benefits that data can provide."

Markowitz adds, "Big Data may be hot — but it only works so long as consumers decide to cooperate and share their data freely. I'm not convinced that proposition is guaranteed in the future." There is a lot riding on the success of big data. That's why it remains critical that some accommodation be reached between use and privacy.

Sensing Food: The Role of Color

Back in the fall of 2000, three researchers, Lawrence L. Garber, Jr., Eva M. Hyatt, and Richard G. Starr, Jr., conducted a study on "The Effects of Food Color on Perceived Flavor." [Journal of Marketing, Fall 2000] Their conclusion was, "Food color's principal use is for flavor identification, not for strategic marketing communication purposes." They went on to write:

"There has been surprisingly little color research in marketing, and the impact of food color has been completely neglected outside of the food sciences; unfortunate, because marketers understand so little of the powerful yet complex effects of color at the point of purchase. Though food scientists have empirically examined aspects of the effects of food color, research on the topic remains sparse, is not theory-based, and, as a body, is limited."

Frankly, that statement surprised me. Food coloring has been around for a long, long time. Peggy Trowbridge Filippone reports:

"Ancient Romans used saffron and other spices to put a rich yellow color into various foods. Other natural foods such as carrots, pomegranates, grapes, mulberries, spinach, beets, parsley, and flowers were also used as food coloring agents. Our ancestors also used minerals and ores, such as azure (copper carbonate), gold leaf, and silver leaf, some of which were downright poisonous if used improperly. Elise Fleming researched cookbooks dating as far back as 1390 A.D., and has compiled an interesting list of food additives used hundreds of years ago with charming quotes in Olde English from sources in her informative treatise on the food coloring of yesteryear." ["Food Coloring History," About.com]

Certainly there is plenty of anecdotal evidence that food color affects taste. For a quick, but interesting report on the subject, click on this link to watch a short ABC story on the subject. During a study conducted a few years ago, "researchers found that color was more of an influence on how taste was perceived than quality or price information." ["More Than Meets The Tongue: Color Of A Drink Can Fool The Taste Buds Into Thinking It Is Sweeter," Science Daily, 16 February 2007] The article claims that JoAndrea Hoegg, from University of British Columbia, and Joseph W. Alba, from the University of Florida, "are the first to look at how individual attributes — such as color, price, or brand — can affect which products we prefer." The article continues:

"The researchers manipulated orange juice by changing color (with food coloring), sweetness (with sugar), or by labeling the cups with brand and quality information. They found that though brand name influenced people's preferences for one cup of juice over another, labeling one cup a premium brand and the other an inexpensive store brand had no effect on perceptions of taste. In contrast, the tint of the orange juice had a huge effect on the taster's perceptions of taste. As the authors put it: 'Color dominated taste.'"

An article on the Konica Minolta website states, "While many of us like to believe that we are not easily deceived, our sense of taste is often fooled by our sense of sight. This is because humans have certain expectations of how food should look. When a food's color is off or is different than what we expect, our brain tells us that it tastes different too. Long supported by scientific studies, we use visual cues from color to identify and judge the quality and taste of what we eat." The article goes to report on the results of an experiment conducted some 40 years ago:

"Published in Fast Food Nation, a more extreme study dating back to the early 1970s offers some insight into how color affects our appetite and perception of food. Subjects in the experiment were served what appeared to be a normal looking plate of steak and french fries. The room, however, was installed with specialty lighting that changed how the color of the food looked. Under this lighting effect, the participants thought the steak and fries tasted fine. Once the effects were turned off and lighting was returned to normal, it was revealed that the steak was dyed a blue color and the french fries were dyed a green color. Upon seeing this, many of the subjects lost their appetite and some became ill."

Another group of researchers, Charles Spence, Carmel Levitan, Maya U. Shankar, and Massimiliano Zampini, believe that there are "two qualitatively distinct research questions" involved in how color affects the taste of food. ["Does food color influence taste and flavor perception in humans?," Selected Works of Carmel Levitan, 2010] They explain:

"The first concerns the role that food coloring plays in the perception of the intensity of a particular flavor (e.g., strawberry, banana, etc.) or taste attribute (e.g., sweetness, saltiness, etc.). The second concerns the role that food coloring plays in the perception of flavor identity."

Some new age chefs are making a good living playing around with people senses by making food look like one thing and taste like another. However, as the ABC story highlighted, the most significant effect that color has on food is the perception it makes on taste attributes. An article from Techdirt reports, "There's one argument, which says that if you're eating food that needs to be colored, you're not eating food." Proponents of that argument point to so-called junk food to support their case. An experiment using uncolored Cheetos, which are naturally gray in color, found that consumers who ate them claimed that "their brains did not register much cheese flavor."

It's not just the color of food that changes our perception of taste or preferability; the color of dinnerware and packaging can also have an effect. Jessica Hullinger reports, "New research from the Journal of Sensory Studies says different colored cups can affect the perceived flavor of beverages. 'The color of the container where food and drink are served can enhance some attributes like taste and aroma,' said study co-author Betina Piqueras-Fiszman, a researcher at the Universitat Politècnica de València in Spain and the University of Oxford in the United Kingdom." ["How Your Cup's Color Changes the Taste of Your Drink," mental_floss, 7 January 2013] And the editorial staff at FoodBusinessNews, report, "It turns out the source of Hillshire Brands’ packaged lunchmeat problems was the product’s clear lid. By switching from the clear lid to a more pronounced red cap, the company has managed to start to turn the business around." ["Hillshire learns red means go," 12 August 2013] Sean Connolly, Hillshire's chief executive officer, told a group of financial analysts, "While consumers consistently articulated a preference for the clear lid in numerous quantitative tests, their actual purchase behavior noticeably changed when we made the switch. As we dug into the disconnect between what consumers were saying and what they were doing, it became clear there was a positive benefit to the red lid that the consumer could not articulate.”

Because we are sometimes unable to articulate why we behave differently than we think we do, I believe that Big Data analytics has a significant role to play in the world of sensory services. There is an interplay between all of our senses that affects how we perceive the world around us and how we enjoy the products we use. To learn more about how the senses affect taste, read my post entitled Enjoying Food: Taste, and Our Other Senses.

Digital Consumers and Their Path to Purchase

A recent study by GroupMNext and Compete indicates that "there are six distinct segments of consumers who exhibit similar behaviors and intent." The thread that ties these similar behaviors together is that they are all completed online. In other words, these segments all represent digital consumers (i.e., "consumers who utilized digital at least once in their purchase pathway"). ["The 6 Types of Digital Consumers and Their Paths to Purchase," by Tyson Goodridge, Compete Pulse, 30 May 2013] The six types of digital consumers are:

• "Basic Digital Consumers: These are not highly digital users. They are comfortable with Internet shopping and research, but they are not mobile or social and have the second-highest likelihood of buying offline.


• "Retail Scouts: These consumers have short journeys and prefer retail sites to brand sites. They use mobile, but are twice as likely to use it in the home as out. They are comfortable buying online but did not express a preference between online and offline.


• "Brand Scouts: Brand Scouts are the spiritual partner to the Retail Scouts except instead of having a favorite retailer, they have a favorite brand. When asked, 72% said they start their journey with a brand in mind.


• "Digitally Driven Segment: They use every digital tool at their disposal. They use social and mobile more than any other segment in the study, value convenience above all else and they do everything in their power to avoid physically going to a store. The Digitally Driven exist in good numbers already, but within five years this will be the dominant segment of consumers.


• "Calculated Shoppers: These shoppers seem to know they are going to make a purchase, but they are deciding which brand to choose. They are similar to the Digitally Driven Segment, but have no urgency to their purchase and they’re willing to take the time to get the best deal.


• "External Shoppers: These are non-mobile shoppers. They want the answers to, 'Should I buy?', 'What do I buy?' and 'What brand do I buy?' – all at the same time. These shoppers have no urgency to make a purchase and they do their research on desktop and laptop computers."

Just from reading those descriptions you should realize that there is no single path to purchase for the digital consumer. In fact, the study concluded that for External Shoppers there can be as many as three dozen steps involved in their path to purchase (see the image below).

The important thing for manufacturers and retailers to understand is that the digital consumer can jump on and off the path to purchase at any point. That means that they must provide a good touchpoint (i.e., a good experience) at every one of the jump on/jump off locations. Aaron Baar explains, "Omnichannel marketing is making the path to purchase so complicated the linear funnel model is obsolete. Today's consumer journey is more like an infinite loop, where shoppers are always discovering, considering and buying via multiple channels. So keeping them engaged is an ongoing process." ["The Funnel is Dead, Long Live the Loop?" Digiday, 25 July 2013]

Simon Van Wyk believes that the best way to ensure that consumers have a good experience all along their digital path purchase is to think like a customer. ["Thinking like a customer: the value of customer journey mapping," Marketing, 26 August 2013] He writes:

"Customer journey mapping and experience design are among the most powerful tools marketers can use for innovating and meeting customer expectations – allowing diagnosis of customer experience issues and formulation of innovative and differentiated brand experiences – but retailers are failing to come to grips with the modern shopper's path to purchase."

One mistake that Van Wyk believes too many companies make is lumping "mobile" technology marketing into a single category. He notes that recent reports indicate that consumers use smartphones and tablets for different reasons and in different contexts. That means that marketers need to design their approaches for those platforms differently. There is one way in which smartphone and tablet users are similar – they both prefer using their devices at home. Van Wyk explains:

"According to Nielsen, more than two-thirds of US smartphone shoppers and four out of five tablet shoppers use their devices most frequently in the home, often with one eye on the TV. So even though smartphones and tablets offer the convenience of shopping on the go, it seems like the vast majority of tablet and smartphone shoppers who make a purchase with their device prefer the comfort of their couch while they shop."

The big difference between smartphone and tablet users, Van Wyk reports, is that users of tablets are more likely to make purchases than users of smartphones. He writes:

"When it comes to actual purchasing, eMarketer says there's a stark contrast between the behaviour of smartphone owners and their tablet owning counterparts. Nearly 71 million tablet owners will make purchases via their device this year, compared with 53 million buyers using smartphones. This distinction is critically important and highlights why marketers need to think like a customer."

Yaakov Kimelfeld, Chief Research Officer at Compete, agrees with Van Wyk that mapping the consumer's path to purchase is important. "The explosion of digital channels," he writes, "the always-on media ecosystem, and the increasingly mobile consumer challenge even the savviest of digital marketers with the simple question: how can I reach the right consumer, at the right time, in the right place, and with the right message to influence his or her purchase decision? There is a way, and this time, it's the marketers (not the consumers) making the crucial decisions along the consumer path to purchase." ["Marketers Can Map the Consumer’s Journey To Purchase," Compete Pulse, 8 May 2013] Like Baar, Kimelfeld believes today's digital path to purchase is best represented by an eternal loop. "Consumers are all over the place," he writes. "It only takes a click to embark on a journey away from the brand – but it also only takes a single click to come back. Consumers can come 99% of the way towards the conversion, but then regress back to researching, simply because it’s easy to do." He concludes his article with three recommendations about how to map a consumer's digital path to purchase:

• "Start from the lowest common denominator – an individual consumer's path to purchase – and build from the bottom up. Each individual path is different, but the combinations are not infinite. A deep, hard look into the data trail consumers leave behind and a meaningful summarization can and should be attempted by any company interested in how their consumers really arrive at the purchase decision.


• "Cluster around behavioral variables. Ad exposure, search queries, visits to the brand website, visits to competitors, repeat visits, reading reviews, visits to aggregator stores, and purchase reviews are all typical touch points that consumers encounter on the way to purchase.


• "Extract the commonalities found in individual paths and identify a manageable number of distinct behavioral patterns. Every brand is different, but in our experience three to seven paths are sufficient to describe consumers in their progress towards the conversion and easy enough for a marketer to craft separate messaging strategies. This helps to engage consumers with the right message and impact the decision process at the right time. Keep a close eye on marketing costs and isolate the digital paths that are driving the most ROI for the business – then steer consumers towards them.

"This data mining approach allows marketers to extract prevailing path characteristics of their consumers and brands, and by revising their marketing strategies, influence consumers' actual journeys toward the purchase."

Understanding the digital consumer's path to purchase will become even more important as billions of new consumers in emerging markets begin shopping online. For most them, smartphones will be their primary access to online purchases. For more on that topic, read my post entitled The Growing Power of the Digital Path to Purchase.

Healthcare is Forecast to Get Even More Personal

Current debates about healthcare cover topics ranging from how to rein in costs to how to make it more personalized. With the emergence of Big Data analytic tools that can rapidly sequence DNA, test hypotheses, discover new relations, uncover fraud, and so forth, the healthcare sector sits on the cusp of a new era. Exactly where the road will lead remains unclear. What is clear, however, is that Big Data analytics hold the potential of making healthcare much more personal. This is particularly true when it comes to developing treatments for disease. Before this can happen, however, there needs to be more secure data sharing.

In an earlier post entitled Big Data and Better Health, I cited an article by Becky Graebe in which she notes that "four distinct big data pools exist in the US health care domain." Those pools are:

• Pharmaceutical R&D data
• Clinical data
• Activity (claims) and cost data
• Patient behavior and sentiment data

Graebe reports "there is very little overlap in ownership and integration of these pools, though that will be critical in making big strides with big data in health care. ["What is the future of big data in health care?," SAS Voices, 6 May 2013] Even if data integration occurs slowly, Hellmuth Broda believes that strides will be made in personalized medicines and treatments. ["Personalized Medicine and Big Data," Pondering Technology, 30 July 2013] He writes:

"While many doomsayers describe the Pharmaceutical industry as one where the golden days are over and where more and more enterprises are bound to be falling off the 'Patent Cliff' or get lost in space, many rather visionary companies have ignited their rocket boosters and are catapulting themselves onto firm ground again. This rocket booster consists of treatments for rare diseases and Personalized Medicine. The two chambered booster combines especially targeted diagnostics with targeted therapies and utilises cloud services alongside with Big Data analytics. This new method of transport brings with it new demands on research, clinical trials, production, logistics, information systems and the overall business model."

To achieve maximum benefit from Big Data analytics, Broda agrees with Graebe that more integration and cooperation needs to occur. He also lists a number of challenges facing the pharmaceutical industry. He continues:

"These challenges and more importantly how Pharma companies tackle them, are shaping the future of the industry, with some key trends already emerging:

• To solve complex tasks, 'coopetition' with other companies will become the norm
• The journey towards Personalized Medicine
• Race to Biologics
• Pairing of drugs with their accompanying diagnostics
• Stricter regulations on compliance
• New demands on privacy and security
• Big Data approaches yielding new insight into drug action correlations

"These challenges put new demands on governance, processes, business models and the information systems, which will build the foundation for these new endeavours. New trends in technology adaptation will support and enable these objectives."

The ability to gather and analyze Big Data is the common thread that weaves it way through all of the trends identified by Broda. One of the dilemmas that will confront the healthcare sector regarding personalized medicines will be cost. Broda explains:

"Drugs will become less generic (and low-cost) and more tailored for specific demographics in the future (and more high value/high cost). There are huge benefits to this, including the fact that these drugs are harder to copy so they retain their value better for the producer and they are also more effective as they can meet more specialist needs. While the current trend is looking at groups of people, maybe based on age or ethnicity, this will evolve in the 21st Century into truly personalised drugs on an individual basis reflecting the patient's genetic predisposition."

There are good reasons why doctors and patients alike would like to see more personalized medicines. As Broda explains, "This increasing personalisation of therapy will significantly benefit patients, by both raising efficacy and reducing side-effects." While drug companies may relish the thought of developing high value/high cost drugs, that's probably not good news for the masses. If Big Data analytics can be used to bring the cost of drug development down, a win-win situation could develop. It has been proven that profits can still be made by selling products to the so-called "bottom billion" consumers if products can be produced at the right price point in the right amount. Big Data analytics should be able to help pharmaceutical companies with that challenge as well. No one benefits when pharmaceutical companies go out of business or when medicines are manufactured in unsafe conditions in poorly regulated plants. For the immediate future, however, it looks like personalized medicines are going to benefit the rich more than the poor.

Broda makes the point that "drugs and their diagnostics walk hand-in-hand." As I pointed out in my earlier post (mentioned above), Big Data and cognitive reasoning systems are playing a role in diagnostics as well as drug development. A paper from the Institute of Medicine, entitled Making the Case for Continuous Learning from Routinely Collected Data, asserts that a "a learning health system" will be beneficial in a number of ways.

"The availability and reliability of large volumes of relevant longitudinal digital data from a variety of clinical and nonclinical sources are core features of a system that learns from each care experience, a learning health system. Common clinical repositories include data from electronic health record (EHR) systems used to manage patient care and claims data necessary for billing purposes. In some cases, data sources can be linked, using either institution-specific identifiers or matching algorithms, to create disease-specific patient registries that enable research. Integration of large pools of disparate clinical data from EHRs and claims is a major function of health information exchanges, which will be increasingly important to ensure seamless management of health information across institutions. Nonclinical sources of patient information may also include data from retail sales of over-the-counter medications, dietary supplements, walking and running shoes, and personal preferences and behaviors."

Although technology is important in the healthcare sector, professionals in that field can't afford to concentrate only on technology if things are going to get better. Like other business sectors, the healthcare sector needs to keep technology, people, and processes in balance. Deanna Pogorelc reports that Dr. Kevin Fickenscher, president and CEO of the American Medical Informatics Association, made that point at a conference earlier this year. ["The problem with big data in health? Too much focus on technology instead of people/process," MedCity News, 7 May 2013] Pogorelc reports that Fickenscher told conference participants, "One of the things that has been a problem in healthcare is that we tend to spend too much time talking about the technology and not enough time talking about the people and the process. So my personal bias is that while technology is important [...] if we don't deal with the people and process, we will not solve these other issues; we won't have good change management, and we won't have good implementation, which is where the value gets created from large data."

One of the newest fields being developed that will make healthcare even more personal is nanomedicine. "Nanomedicine, refers to highly specific medical intervention at the molecular level for curing disease or repairing damaged tissues." ["Nanomedicine," Associates Degree in Nursing] The article states, "Though in its infancy, could we be looking at the future of medicine? Early clinical trials certainly look promising." The article was accompanied by the following infographic.

Source: Nanomedicine: The Future of Medicine

It should be abundantly clear that both diagnoses and treatments for diseases are going to be more personal in the future. That should be a good thing; especially if costs can be held in check.

Fellow Travelers: Big Data and the Internet of Things

"Bland by name and superficially viewed as gee-whiz technology never to be realized, the IoT (Internet of things) has significant potential to transform business," writes Bob Violino. ["The 'Internet of things' will mean really, really big data," InfoWorld, 29 July 2013] Why, you might ask, is the Internet of things viewed as science fiction by skeptics? The simple answer is that it holds the promise of connecting billions of machines (everything from cars to refrigerators) that will hold virtual conversations with each other. In another article, Violino defines the Internet of things this way:

"The term carries a number of definitions. But in general, the IoT refers to uniquely identifiable objects, such as corporate assets or consumer goods, and their virtual representations in an Internet-like structure. ... In effect, these networked things become 'smart objects' that can become part of the Internet and active participants in business processes." ["What is the Internet of Things?" Network World, 22 April 2013]

Ericsson, the Swedish technology firm, has a vision of the world in which 50 billion devices are continuously connected and communicating. On its website, the company writes:

"The vision of more than 50 billion connected devices, based on ubiquitous internet access over mobile broadband, devices or things will be connected and networked independently of where they are. Falling prices for communication, combined with new services and functionality connecting virtually everything to serve a wide range of commercial applications, individual needs and needs of society. The 50 billion connected devices vision marks the beginning of a new era of innovative, intertwined, combined products and services that utilize the power of networks."

If that vision comes true, the IoT will clearly be much larger than the "human" Internet with which we are all familiar. Violino writes, "Promising unprecedented connectivity among objects and the gathering of massive amounts of data, IoT is poised to deliver significant business benefits to organizations forward-thinking enough to envision the opportunities and efficiencies IoT can reap." In fact, the amounts of data that will be generated by the IoT will be so massive that the term Big Data seems entirely inadequate to describe it. Evangelos Simoudis warns, "While our ability to collect the data from these interconnected devices is increasing, our ability to effectively, securely and economically store, manage, clean and, in general, prepare the data for exploration, analysis, simulation, and visualization is not keeping pace." ["Big Data and the Internet of Things," Enterprise Irregulars, 26 February 2013] He continues:

"The Internet of Things necessitates the creation of two types of systems with data implications. First, a new type of ERP system (the system of record) that will enable organizations to manage their infrastructure (IT infrastructure, human infrastructure, manufacturing infrastructure, field infrastructure, transportation infrastructure, etc.) in the same way that the current generation of ERP systems allow corporations to manage their critical business processes. Second, a new analytic system that will enable organizations to organize, clean, fuse, explore and experiment, simulate and mine the data that is being stored to create predictive patterns and insights. Today our ability to analyze the collected data is inadequate."

General Electric, which calls the IoT the "Industrial Internet," is convinced that this machine-to-machine network represents the future. It depicts it as shown below.

Source: General Electric

Bill Ruh, Vice President and Global Technology Director at General Electric, writes:

"Big Data, a term that describes large volumes of high velocity, complex and variable data that require advanced technologies to capture, store, distribute and analyze information, is at a tipping point, with billions being spent to turn mountains of information into valuable insights for businesses. But there is more to Big Data than numbers and insights." ["GE Insight: The Industrial Internet – Even Bigger Than Big Data," Financial Post, 31 May 2013]

Although Ruh believes that "Big Data is the lifeblood of the Industrial Internet," he also believes the Industrial Internet is "about building new software and analytics that can extract and make sense of data where it never existed before – such as within machines." Beyond that, he believes that machine learning will be used "to make information intelligent." To make this happen, he writes that "new connections need to be developed so that Big Data 'knows' when and where it needs to go, and how to get there." He continues:

"By connecting machines to the Internet via software, data is produced and insight is gained, but what's more is that these machines are now part of a cohesive intelligent network that can be architected to automate the delivery of key information securely to predict performance issues. This represents hundreds-of-billions of dollars saved in time and resources across major industries."

Obviously, machines aren't going to spontaneously connect. The process begins "with embedding sensors and other advanced instrumentation in the machines all around us, enabling collection and analysis of data that can be used to improve machine performance and the efficiency of the systems and networks that link them." ["Big Data Will Drive the Industrial Internet," by Thor Olavsrud, CIO, 21 June 2013] Olavsrud cites a paper written by Peter C. Evans, director of Global Strategy and Analytics at GE, and Marco Annunziata, chief economist and executive director of Global Market Insight at GE, entitled, Industrial Internet: Pushing the Boundaries of Minds and Machines. In that paper, Evans and Annunziata assert, "The compounding effects of even relatively small changes in efficiency across industries of massive global scale should not be ignored." They explain:

"As we have noted, even a one percent reduction in costs can lead to significant dollar savings when rolled up across industries and geographies. If the cost savings and efficiency gains of the industrial Internet can boost U.S. productivity growth by 1 to 1.5 percentage points, the benefit in terms of economic growth could be substantial, potentially translating to a gain of 25 to 40 percent of current per capita GDP. The Internet Revolution boosted productivity growth by 1.5 percentage points for a decade — given the evidence detailed in this paper, we believe the industrial Internet has the potential to deliver similar gains and over a longer period."

Mike Wheatley agrees with the folks at GE that the Internet of Things is more than science fiction. "There's no holding back the Internet of Things," he writes, "this is where the world's heading, and we're already seeing it in concepts ranging from smart electricity meters to IBM’s rather more ambitious Smart Cities initiatives. The basic fundamental holding IoT together is connectivity, a world in which machines with intelligent sensors are hooked up to the web, and able to deliver a stream of constant data." Critical to that connectivity, he believes, is cloud computing. ["Cloud Computing & The Internet of Things go Hand in Hand," Silicon ANGLE, 17 July 2013] He continues:

"The Internet of Things ... won't be made possible by a jumble of wires. What makes it possible is cloud computing, combined with the glut of sensors and applications all around you that collect, monitor and transfer data to where it's needed. All of this information can be sent out or streamed to any number of devices and services. ... Of course, this means that there's going to be an awful lot of data flying around out there, data that needs to be processed quickly. ... This is why the cloud is so important. The cloud can easily get a handle on the speed and volume of the data that's being received. It possesses the ability to ebb and flow according to demand, all the while remaining accessible anywhere from any device."

If Evangelos Simoudis is correct that currently "our ability to analyze the collected data is inadequate," then Wheatley might be a bit optimistic in his assessment of how easy it is going to be to manage the IoT. I do agree with him, however, that the cloud will be essential to the IoT's development. One thing that all analysts seem to agree on is that once the IoT is up and running, the amount of data it will generate will be humongous.