Enterra Insights

Data Philanthropy and Global Resilience

"The digital revolution of the first decade of this new century has brought many wonders," writes Robert Kirkpatrick, "yet it has also has ushered in a bewildering array of unanticipated consequences. We now find ourselves in a volatile and hyperconnected world where risk has been globalized." ["Data Philanthropy: Public & Private Sector Data Sharing for Global Resilience," Global Pulse Blog, 16 September 2011] Supply chain and insurance professionals certainly share Kirkpatrick's worldview. They know that events thousands of miles away can have cascading effects that ripple around the globe. Kirkpatrick continues:

"Our tightly-coupled systems have become interdependent, creating feedback loops so complex that predictive analysis is of little use. There is a kind of butterfly effect at work in the world. Crises emerge suddenly without warning, interact to amplify one another's effects, and reverberate across the globe, increasing the burden of poverty, disease, and hunger among the world's most vulnerable communities. All too often, by the time we understand what is happening, it's too late to act."

I'm not certain that it's ever "too late to act"; but, Kirkpatrick's point is still valid. As fellow travelers on planet Earth, we need to become more resilient. He believes part of the answer to becoming more resilient lies in big data analysis. He explains:

"The same technologies that connect us to one another have also turned all of us into prolific producers of data, and this new data may hold the keys to mitigating much of the volatility and uncertainty that now confronts us. We are now swimming in an ocean of digital data, most of which didn't exist even a few years ago. The private sector is already using real-time analysis of this data to understand the changing needs of its customers. One of the defining challenges of the second decade of this century will be for the public sector to learn how to tap into this new 'unnatural resource' to understand the changing needs of citizens and respond with agility."

Understanding and insights are certainly important as we look to future. In an earlier post (Going Long with Big Data), I discussed an article by Samuel Arbesman, a senior scholar at the Ewing Marion Kauffman Foundation and a fellow at the Institute for Quantitative Social Science at Harvard University, who argued that we not only need to tap new data, like Kirkpatrick suggests, but also much older data that takes a long look back in history. Arbesman isn't suggesting that we should steer our ship by looking at its wake; he's simply making the point that insights can come from data both new and old. Kirkpatrick may be concentrating on new data because the thrust of his article is that policymakers and planners need access to big data in order to make the world more resilient. Gaining access to new information, he knows, is much more problematic than gaining access to what Arbesman calls long data. Kirkpatrick explains:

"Today there is a tremendous amount of useful data available online, from user-generated content such as news, blogs, and social media, to the structured data being shared through open data initiatives. Yet increasingly, the real wealth of data out there is what is known as 'massive passive data,' or 'data exhaust.' It's the personal information corporations collect about what products their customers buy and about how they use digital services. It is the digital trails we leave behind, merely by going about our daily lives. It is the data that powers business, which the World Economic Forum has described as a new asset class. ... Analysis of this data exhaust holds an even greater potential than public Web content to help policy makers gain a real-time understanding of human well-being."

Paul Doscher, CEO of LucidWorks, calls data exhaust "dark data" because it often hides in places that are inaccessible by most search engines. ["Searching for Dark Data," SiliconANGLE, 11 February 2013] Kirkpatrick notes that gaining access to this data is not just technically difficult but philosophically and legally difficult. "The businesses that collect this data see the value in maintaining exclusive access to it," he writes, "a real-time understanding of their markets allows them to respond to emerging trends with the agility needed to compete in today." Companies don't easily relinquish their assets. Just as importantly, Kirkpatrick notes, "There are often a host of legal constraints related to protection of privacy and secondary use." There is a reason that private enterprises hold big data dear -- it contains treasure that can be unlocked by analysis. Kirkpatrick's argument is that government policymakers should also hold big data dear and start mining it for golden insights that can make the world a more resilient place. The challenge, he writes, is that "the data that could help give them the additional agility needed to meet the challenges of governance in the 21st century is accumulating behind corporate firewalls. When public policy lags household reality by months or years, particularly in today's dynamic socioeconomic landscape, the consequences for vulnerable communities can be severe." What Kirkpatrick would like companies to do is share their data with governments. I'm sure such a suggestion causes many corporate executives to roll their eyes in disbelief. Kirkpatrick, however, believes data sharing can be done in a way that protects both the proprietary information that companies cherish and the privacy of individuals. He calls his scheme "data philanthropy." He explains:

"While, architecturally, it's not clear what form or forms data philanthropy could take, we like the term as it drives home the message that shared data is a public good. We have begun exploring ideas and research that might uncover a viable framework of how data philanthropy actually functions, i.e., how data generated in the private sector can readily be shared with the public sector. At least two ideas are already being robustly debated: A data commons where some kinds of data are shared publicly after adequate anonymization and aggregation; and an alerting network where more sensitive data that can never be shared publicly is nevertheless analyzed by companies behind their firewalls for specific smoke signals. There are other models emerging as well."

To bolster his case, Kirkpatrick provides a few "threads" of evidence from thought leaders that show progress in this area. He then writes:

"Down the road, it's not impossible to imagine some version of each of these converging in an interesting way, but in the meantime, we need a pragmatic approach that can be applied today in both developing and industrialized economies. Private sector participation is central to this effort, and we are eager to engage with potential partners. What has been a pleasant surprise is that, based on our initial talks, the private sector agrees that what we are proposing is not simply a new form of charity or CSR, but a sensible strategy for business risk mitigation, particularly when investing in emerging markets. ... So how do we take data philanthropy from concept to operational reality? We are talking with a number of business leaders in companies that take a sustainability-oriented, double- or triple-bottom-line view of their market, and as I've indicated data philanthropy hasn't been a hard sell—at least conceptually. They have expressed a willingness to engage with us in a series of collaborative research projects exploring what utility their data might have for giving policy makers a real-time understanding of the well-being of communities on the path of economic development, an important goal for both public and private sectors."

While Kirkpatrick is primarily focusing on data sharing from companies, Prasanna Lal Das and Giulio Quaggiotto are exploring "the growing willingness of individuals to 'donate' personal data for the public good." ["Would you give up your personal data for development?," UNDP blog, 25 February 2013] They ask a series of questions they would like to explore, including:

• Could this be a promising venue to explore for development organizations?
• Should we start a 'donate your data' campaign targeting individuals, rather than corporations?
• Is some type of individual data likely to be more useful/practical to start with?
• Should we perhaps steal a page from the corporate book and follow the example of, say, companies like Fitbit that encourage individuals to share their health data?
• And what about the Quantified Self movement: a potential ally?
• Or is a more appropriate role for us to push for policy change so that we can have open, collaborative trust frameworks between individuals, governments and companies that would encourage the (willing) sharing of personal data?

In the end, they admit, "This approach raises a whole range of questions and challenges – from privacy to prior informed consent, all the way to personal safety and security." Nevertheless, data philanthropy is certainly an idea worth pursuing if we are going to make the world a more resilient place in which to live.

Big Data and the Revolution in Marketing

"Driving analytics against big-data is causing marketers to rethink what they do from the ground up," writes Kim Davis. In fact, Davis believes that big data is fomenting a revolution in marketing. ["Big-Data Drives a Marketing Revolution," Internet Evolution, 18 February 2013] Davis argues that "marketers, from the CMO down, need to change in order to deal with a 'highly instrumented, highly connected, highly intelligent world economy.'" He asserts that even in a sector roiled by revolution, there are three fundamental marketing principles that need to be kept in mind. They are:

• Know your customer.
• Know what to market, and how to market it.
• Protect the brand promise.

Davis writes, "Essentially, these principles survive in a data-driven economy, but each is transformed by contact with analytics." That is especially true when it comes to getting to know your customer. Davis explains:

"Firstly, customers are no longer a faceless mass, the behavior of which can be more or less predicted and understood using classic market segmentation techniques. Online activity, not least over mobile channels, has made behavior so transparent that consumers emerge as individuals, demanding a specifically tailored commerce experience. Secondly, analytics allows us to systematize engagement across multiple channels -- from call centers to apps -- so as to maximize the value for the consumer at every point of contact. Thirdly, the benefits -- and risks -- of social media provide ample motivation for brand and culture to be authentically one. In other words, an organization's culture should be such that it's organically represented by all employees engaging with real and potential markets over social channels. Brand value can live or die in the global, social conversation. Predictive analytics can point the way to the next offer, the next action, and the next customer need, in a way that feels more like providing a service than marketing a product."

Targeted marketing all about providing "a specifically tailored commerce experience." In the era of big data, getting to know your customer requires good analysis. That's why Davis insists that buying the right "IT is no longer a cost, but a strategic investment." He concludes, "Marketers, armed with this view of customers as integrated individuals, are actually well armed to decide which customers they ultimately want. Beyond that ... they can begin to think about what they want their customers to become." Marketers know that the Pareto Principle is alive and well in the business sector (i.e., 80% of your sales come from 20% of your clients). Big data analytics can help adjust those percentages by helping identify potential high value customers.

Davis' colleague, Rich Luciano, insists, "Putting the customer in the driver's seat can actually provide a market advantage. ... It's good for the customer, too." ["Analytics: The Cornerstone for a Smarter Commerce Strategy," Internet Evolution, 5 December 2012] Luciano explains:

"The global conversation taking place on social media platforms, together with the ready access to those platforms afforded by the explosion in use of mobile devices, has transformed the relationship between business and consumer. In the first place, consumers are empowered by the extraordinary amount of product information available. Furthermore, in sharing and responding to this information, consumers are creating data packed with insights. When companies use business analytics tools to extract nuggets of usable information from this consumer data, it's the consumer who ultimately benefits through an improved commercial experience."

Luciano writes, "A good business analytics solution should place data directly in the hands of decision-makers." What he really means is that a good business analytics solution should place actionable insights (or intelligence) into the hands of decision-makers. Data and insights are not the same thing; but, Luciano knows that. He continues:

"One way this can be achieved is by integrating data from multiple sources and channeling clear, reliable information to accessible dashboards or reports. Arming employees with the information they need rewards businesses with increased revenues and reduced operating costs. Perhaps more importantly, it benefits customers by offering them the products their input has helped define, delivered in the way they prefer, and with after-sales service to which they best respond."

The key to targeted marketing, Luciano insists, is "getting a grip on the data." He concludes, "Business analytics is no longer something nice to have. It's the cornerstone of a strategy that will not only increase ... competitiveness, ... but improve the commerce experience for customers as well." In some circles, this is called "shopper marketing." According to Wikipedia, shopper marketing is "understanding how one's target consumers behave as shoppers, in different channels and formats, and leveraging this intelligence to the benefit of all stakeholders, defined as brands, consumers, retailers and shoppers." Hayden Sutherland believes that online and in-store consumer marketing efforts need to be more coordinated if shopper marketing is going to achieve good results. ["Multi-channel retail meets Shopper Marketing," Press 2.0, 20 February 2013] In order to achieve these results, Sutherland believes online and in-store marketing disciplines must come together to:

• Understand the roles that in-store and online now have
• Map the Paths to Purchase (both Digital to Store and Store to Digital)
• Grow customer loyalty and retain existing customers

He concludes, "In short, multi-channel retail/eCommerce and shopper marketing now need to merge into a single area of specialisation ... Multi-channel shopper marketing." One store that has done well merging retail and eCommerce channels is Macy's. Its MOM initiative (My Macy's localization, Omnichannel, and MAGIC selling strategy) has resulted in more satisfied customers and increased sales. ["Macy's Loves MOM, And Consumers Do Too," by Walter Loeb, Forbes, 27 February 2013] Loeb briefly explains the MOM strategy:

"For more than three years the company has worked on specific localization – 'My Macy's – which focuses on having each store feature merchandise that is relevant to customers who live and shop in that area. ... The customer has responded enthusiastically to the My Macy’s strategy. The Omnichannel strategy allows for extraordinary service to customers. In addition to warehouse fulfillment of purchases from in-store, on-line or mail order customers, Macy’s now has 292 stores that participate in the fulfillment of orders – to insure that the customer receives better service as purchases are delivered quicker than ever before. By the end of the year management expects to have about 500 stores participating in this program. Magic selling is the third key initiative. Through new training tools, associates are taught how to be more engaged with the customers and how to be more empowered so that they can make decisions on the selling floor. It is an important step ensuring more caring and responsive customer service."

Nichole Kelly, President of Social Media Explorer and SME Digital, believes that shopper behavior no longer conforms to the sales funnel theory that hypothesizes "someone comes into the top of the funnel and sales fall out the bottom." ["The Death of the Sales Funnel as We Know It," Social Media Explorer, 19 February 2013] She continues:

"Is that true in today's world? Do we start at the top and make our way through to the end? Or do we start at the top, leave, jump levels, come back, leave again, come back at the beginning and at some point come back and buy? Are we following a linear purchase pattern or an erratic path of engagement that sometimes results in a purchase? I think the assumption that any large percentage of buyers follow a pre-determined path to a sale online is incorrect."

Kelly agrees with Sutherland that online and in-store activities need to come together to get a true picture of a consumer's path to conversion. "It is extremely difficult to track the true path to sale," she writes, "when there are only online components. Throw in offline components and you're toast. ... If we truly want to understand this erratic ride our buyers are taking us on we should be demanding that tools start to keep track of EVERY touch point, at least online. ... Progress before perfection should be your new motto. Start getting better data with what you have now, but we should always be working towards perfection." She concludes:

"At the end of the day there are 'core' points in every sales process that most buyers go through. They may fall in and out of the funnel at various stages, but we still can focus our efforts on optimizing the path from one stage to the next, understanding we are simply modeling a process not the reality of the buyer’s progress. But I think a new model is needed and it doesn’t look like a funnel. Rather it looks like an illustration of the interworking of a neural network. ... This could illustrate where we are leading the buyer and the various paths a buyer could take to get there. It shows that the path to purchase is no longer linear; it is a network of touch points, decision points, and opportunities that are either taken or declined by the buyer. If we could track this it seems reasonable to assume that certain paths and certain conversions would glow brighter as they are working more effectively than others. It could also show the black hole where potential buyers go and never return. Could we then optimize our efforts to put others down that path over less effective paths? Honestly, I don’t know. But I will tell you, if I can figure out how to model this with real data in a way that can be analyzed I’ll be the first to try. For now, I’ll continue to use a model that people recognize understanding the flaws it holds, while I try to build a new model for the future."

I believe Kelly is correct that a new sales model is coming and that it will look a lot more like a neural network than a linear path or funnel. The key to success in digital path to purchase will be making the right offer at the right time to a consumer who just happens to be in a shopping mood. Sometimes consumers are pursuing a specific item and sometimes, as Kelly writes, they "still make purchases and purchase inquiries on a whim." Targeted marketing needs to make the most of both opportunities.

Big Data is about People and Behavior

With all of the hype surrounding big data, we should remind ourselves that what is most important is how it can be used to help us better understand ourselves, the decisions we make, and the actions we take. Much of the data that is currently being collected comes from mobile devices. "To say mobility is huge – gargantuan, even – would be an understatement," writes Chelsi Nakano. As a result, she notes, a lot of attention has been given to the hardware (i.e., smartphones and tablets). She believes that focus is changing. "At today's intersection of intelligence, computing and massive amounts of ubiquitous and networked data," she writes, "mobility is no longer about the hardware – it's driving an entire shift in human behavior." ["Mobility: It’s About Behavior, Not Devices," Conspire, 4 February 2013] Marketers obviously want to understand that behavior so they can cater to changing tastes. Nakano continues:

"Lisa Weinstein, President, Global Digital, Data and Analytics at Starcom MediaVest Group, had a similar comment at CES this year: ... 'I actually think that we have to take off the channel lens [from] mobile as a device, and think more about the consumer and behavior, and I think when you do that there's some really, really interesting implications about the way that consumers are interacting with different types of experiences in places – whether that be at retail or with friends in a social type of environment through their personal device. And so I very much believe that the device is important because of the personalization, but the behavior that it drives is actually a much deeper opportunity for how brands can intersect with consumers in new and different ways. ... Because the focus is now on our actions rather than the tools we use to carry them out, we're going to see even more objects – not just phones, tablets or computers – connected to the Internet, further augmenting this behavior and providing even more touch points for data and information exchange."

Nakano believes that all of this connectivity (i.e., the Internet of Things) "will not only cause a widespread demand for better ways to obtain data, but also highlight the importance of getting it to the right people in the right forms." In other words, the Internet of Things will only increase the need for technologies that turn data into actionable intelligence -- once again connecting data to people. Last fall Jim Stikeleather, Executive Strategist for Innovation at Dell Services, also stressed the importance of the human connection with big data. "Machines don't make the essential and important connections among data and they don't create information," he wrote. "Humans do." ["Big Data's Human Component," HBR Blog Network, 17 September 2012] He continued:

"Tools have the power to make work easier and solve problems. A tool is an enabler, facilitator, accelerator and magnifier of human capability, not its replacement or surrogate — though artificial intelligence engines like Watson and WolframAlpha (or more likely their descendants) might someday change that. That's what the software architect Grady Booch had in mind when he uttered that famous phrase: 'A fool with a tool is still a fool.' We often forget about the human component in the excitement over data tools. Consider how we talk about Big Data. We forget that it is not about the data; it is about our customers having a deep, engaging, insightful, meaningful conversation with us — if we only learn how to listen."

Stikeleather offered a few other insights about the human component and its connection to big data. The first insight is that "expertise is more important than the tool. Otherwise the tool will be used incorrectly and generate nonsense (logical, properly processed nonsense, but nonsense nonetheless)." Cliff Cate, Senior VP of Customer Success for GoodData, believes that analytical expertise needs to be employed primarily by firms that offer analytical solutions not by the companies that use those solutions. "Companies need solutions that enable them to use and customize their data easily," he writes, "because it is the whole team, not just the individual analyst, that knows the business best." ["Data Scientists Not Required: Big Data Is About Business Users," SmartData Collective, 5 February 2013] He continues:

"By offering business users intuitive data solutions, we bypass the need for the data scientist, who works in isolation. In fact, most data scientists are associated with the old school of business intelligence, where systems were so complicated that they needed someone with a data science background to run and get value from them. The new generation of solutions, on the other hand, is making it easy for business users to engage big data. An interdisciplinary team will see and use the visuals provided, and collaborate on the best decisions on a regular basis."

Stikeleather agrees with Cate when it comes to the importance of visualization. He writes:

"Humans are better at seeing the connections than any software is, though humans often need software to help. ... We have eons of evolution generating a biological information processing capability that is different and in ways better than that of our digital servants. We're missing opportunities and risking mistakes if we do not understand and operationalize this ability. Edward Tufte, the former Yale professor and leading thinker on information design and visual literacy, has been pushing this insight for years. He encourages the use of data-rich illustrations with all the available data presented. When examined closely, every data point has value, he says. And when seen overall, trends and patterns can be observed via the human 'intuition' that comes from that biological information processing capability of our brain. We lose opportunities when we fail to take advantage of this human capability. And we make mistakes."

Stikeleather asserts that "there are many other risks in failing to think about Big Data as part of a human-driven discovery and management process." He provides examples of insensitivity and unexpected consequences when big data tools are "over-automated." He believes that keeping the human component in mind is central to turning data into information and insight. He explains:

"Although data does give rise to information and insight, they are not the same. Data's value to business relies on human intelligence, on how well managers and leaders formulate questions and interpret results. More data doesn't mean you will get 'proportionately' more information. In fact, the more data you have, the less information you gain as a proportion of the data (concepts of marginal utility, signal to noise and diminishing returns). Understanding how to use the data we already have is what's going to matter most."

The staff at the Social Media Observatory agrees that value of data relies on what they call "the human algorithm." ["The Human Algorithm: Redefining the Value of Data," Social Media Observatory, 11 December 2012] The article states, "Everything we share, everywhere we go, everything we say and everyone we follow or connect with, generates valuable information that can be used to improve consumer experiences and ultimately improve products and services." The article continues:

"This 'big' data that will help businesses evolve and adapt in a new era of connected consumerism. More importantly, the study and understanding of relevant big data will shift organizations from simply reacting to trends to predicting the next disruption and adapting ahead of competition — thus, marking the shift from rigid to adaptive business models. From business to education to government and everything in between, without studying how the undercurrent of behavior is evolving, organizations cannot effectively adapt to new trends and opportunities. Change though, cannot be undertaken simply because of pervasive data."

Like other pundits, analysts at the Social Media Observatory emphasize that the goal of gathering and analyzing data is to gain actionable insights -- with emphasis on action. They assert, "Without interpretation, insight and the ability to put knowledge to work, any investment in technology and resources is premature." The article continues:

"The reality is ... that how organizations connected with customers yesterday is not how customers will be served tomorrow. Meaning, the entire infrastructure in how we market, sell, help, and create now requires companies to not only study data and behavior but also change how it thinks about customers. This is a bona fide renaissance and to lead a new era of customer engagement requires knowledge acumen. I refer to the confluence of data and interpretation as the human algorithm—the ability to humanize technology and data to put a face, personality, and voice to the need and chance for change. Data tells a story, it just needs help finding its rhythm and rhyme. ... Much of what we see today is important, but it's measuring activity not translating behavior into creativity or strategy."

According to the article, "The human algorithm is part understanding and part communication. The ability to communicate and apply insights internally and externally is the key to unlocking opportunities to earn relevance." It concludes:

"Beyond research, beyond intelligence, the human algorithm is a function of extracting insights with intention, humanizing trends ad possibilities and working with strategists to improve and innovate everything from processes to products to overall experiences. The idea of the human algorithm is to serve as the human counterpart to the abundance of new social intelligence and listening platforms hitting the market every day. Someone has to be on the other side of data to interpret it beyond routine. Someone has to redefine the typical buckets where data is poured. And someone has to redefine the value of data to save important findings from a slow and eventual death by three-ring binders rich with direction and meaning. ... Even though sophisticated tools can help track data points that can lead to these insights, it still takes a human touch to surface them and in turn advocate findings within the organization. It's the difference between insights, actionable insights, and executed insights. ... Those who don’t plug in and invest in technology's human counterparts are in turn making an investment toward potential irrelevance. But remember, data is just the beginning. Data must always tell a story and that takes a human touch to extract data, surface trends, and translate them into actionable insights across the entire organization."

Clearly, the Big Data Era is inextricably connected to technology. We need to remember, however, that big data also is inextricably connected to people.

Electronic Health Records: The Best is Yet to Come

There have been a number of articles published recently about the fact that savings that were predicted to flow from conversion to electronic medical records (EMR) -- sometime called electronic health records (EHR) -- have yet to be realized. Back in 2005, RAND Corporation analysts predicted, "EMR implementation and networking could eventually save more than $81 billion annually—by improving health care efficiency and safety—and that HIT [health information technology]-enabled prevention and management of chronic disease could eventually double those savings while increasing health and other social benefits." ["Can Electronic Medical Record Systems Transform Health Care? Potential Health Benefits, Savings, And Costs," by Richard Hillestad, James Bigelow, Anthony Bower, Federico Girosi, Robin Meili, Richard Scoville and Roger Taylor, Health Affairs, 28 September 2005] As Reed Abelson and Julie Creswell report, "The conversion to electronic health records has failed so far to produce the hoped-for savings in health care costs and has had mixed results, at best, in improving efficiency and patient care." ["In Second Look, Few Savings From Digital Health Records," New York Times, 10 January 2013]

The "second look" referred to in the headline of Abelson's and Creswell's article was a reassessment conducted by the RAND Corporation. In an article published in Health Affairs, Dr. Arthur L. Kellermann, one of the authors of a reassessment, stated, "We've not achieved the productivity and quality benefits that are unquestionably there for the taking." The most important emotion to draw from that statement is not disappointment but hope. Dr. David Blumenthal, who helped oversee the federal push for the adoption of electronic records, told Abelson and Creswell that "technology ‘‘is only a tool. Like any tool, it can be used well or poorly." The popular opinion seems to be that the tool is currently being used poorly. Dr. David J. Brailer, who worked in the Bush Administration, agrees with Blumenthal that "tens of billions of dollars could eventually be squeezed out of the health care system through the use of electronic records." He believes, however, that the Obama Administration committed a "colossal strategic error" when it it included a huge amount of money for record conversion in the stimulus package. "The vast sum of stimulus money flowing into health information technology created a 'race to adopt' mentality," he told Abelson and Creswell, "buy the systems today to get government handouts, but figure out how to make them work tomorrow.”

In the latest Health Affairs article, Kellerman and his colleague, Spencer Jones, claim there are several reasons that "electronic medical records didn’t play out as expected." ["Why electronic health records failed," by Sarah Kliff, Washington Post Wonkblog, 11 January 2013]

"To start, doctors didn't adopt electronic records as widely as expected. RAND researchers expected that about 90 percent of doctors would digitize their records, hitting levels seen in the United Kingdom and the Netherlands. But doctors have adopted at a much slower rate in the United States: Most recent research suggests about half of doctors have fully digitized their records. For hospitals, the number hovers below 30 percent. Even for the doctors that did adopt electronic records, many have run into a huge stumbling block with interoperability. The records they use at their office may not connect to a different software used at a hospital. Seventy-percent of doctors cited the lack of interoperability as a frustration with electronic records in a recent Bipartisan Policy Center survey. 'As a result, the current generation of electronic health records functions less as "ATM cards," allowing a patient or provider to access needed health information anywhere at any time, than as "frequent flier cards" intended to enforce brand loyalty to a particular health care system,” the RAND authors write."

That's a huge problem. Standardization is critical if the predicted savings are to be realized. One man that is trying to rectify that situation is a radiologist named Michael Zalis. He got so tired of "logging in and out of separate electronic health records at Massachusetts General Hospital" that he "co-founded and wrote the code for QPID, which stands for Queriable Patient Inference Dossier, a natural language search tool that extracts relevant clinical information from an EHR." ["Making Electronic Health Records More Efficient, by Zina Moukheiber, Forbes, 14 February 2013]

Moukheiber writes, "Tools that help piece together a complete portrait of patients are increasingly in demand, as clinicians try to sort out and analyze data stored in a digital health record, with the goal of delivering better care." To highlight that fact, Moukheiber reports, "QPID has grown by word of mouth, and is now deployed across Boston-based Partners HealthCare, which includes Brigham and Women’s Hospital. It is used by 5,000 clinicians in 15 departments, such as gastroenterology, anesthesia, and surgery. QPID registers an average 5 million search requests a month. ... With QPID, an anesthesiologist or a nurse at Mass General can pull in less than two minutes, says Zalis, key information, such as allergies, heart problems, and medications that could be buried in a report or a lab panel." The bottom line is that with standardization productivity and patient care both improve.

Before getting too excited and predicting that QPID is going to solve the problems currently being experienced, remember that Kellerman and Jones pointed out that some very large companies actually like the current arrangement because it "enforce(s) brand loyalty to a particular health care system." That shouldn't be too shocking since the money involved is enormous. For example, Julie Creswell reports that one of the large companies involved in EMR services, Allscripts, has more than doubled its annual revenue "from $548 million in 2009 to an estimated $1.44 billion last year." ["A Digital Shift on Health Data Swells Profits in an Industry," New York Times, 19 February 2013] According to Creswell, other large companies in the sector have seen similar increases in revenue. Clearly, those companies don't want to see those revenues fall. As a result, Creswell concludes:

"As doctors and hospitals struggle to make new records systems work, the clear winners are big companies like Allscripts that lobbied for that legislation and pushed aside smaller competitors. ... Current and former industry executives say that big digital records companies like Cerner, Allscripts and Epic Systems of Verona, Wis., have reaped enormous rewards because of the legislation they pushed for. ... Executives at smaller records companies say the legislation cemented the established companies' leading positions in the field, making it difficult for others to break into the business and innovate."

If the Obama Administration is serious about making healthcare affordable, it must find a way to get the industry to agree on standards and open the door for smaller, innovative companies offering better products and services. Bill Bithoney, from Truven Health Analytics, believes that Meaningful Use (MU) incentives could be the tool to create better standards. ["Digital Health Records: Lower costs, better quality – eventually," Healthcare Blog, 22 January 2013] He writes:

"Meaningful Use I, and later MU II, should be viewed as laying the groundwork for electronic health record interventions which may ultimately result in improved health care. ... True advances in patient health outcomes, quality of care, and cost can only be achieved when electronic health records are used to share information across the entire care continuum from hospitals and nursing homes to rehab facilities to primary care physician offices. ... When most medical professionals can do things like supply discharge summary data and other pertinent medical information in electronic formats across the care continuum, we'll begin to realize health and well-being benefits of the impending healthcare digital revolution."

I'm naturally an optimist. I believe that a decent system will eventually be worked out and anticipated improvements in productivity and patient care realized along with desperately needed healthcare savings. The reason I'm optimistic is that I believe that healthcare providers will eventually force companies to make it easier to share records so that mistakes are minimized. At that point, if a company can't play nicely with others it will be asked to leave the sandbox. Clearly, it won't be easy -- but it can be done.

Secure Information Sharing Has Never Been More Important

If you are concerned about cybersecurity, you probably read the New York Times' article about the Chinese military carrying out hacking operations. ["Chinese Army Unit Is Seen as Tied to Hacking Against U.S.," by David E. Sanger, David Barboza, and Nicole Perlroth, 19 February 2013]. Sanger, Barboza, and Perlroth report:

"Mandiant, an American computer security firm, [has tracked] for the first time individual members of the most sophisticated of the Chinese hacking groups — known to many of its victims in the United States as 'Comment Crew' or 'Shanghai Group' — to the doorstep of the military unit's headquarters. The firm was not able to place the hackers inside the 12-story building, but makes a case there is no other plausible explanation for why so many attacks come out of one comparatively small area. ' Either they are coming from inside Unit 61398,' said Kevin Mandia, the founder and chief executive of Mandiant, in an interview last week, 'or the people who run the most-controlled, most-monitored Internet networks in the world are clueless about thousands of people generating attacks from this one neighborhood.' Other security firms that have tracked 'Comment Crew' say they also believe the group is state-sponsored, and a recent classified National Intelligence Estimate issued as a consensus document for all 16 of the United States intelligence agencies, makes a strong case that many of these hacking groups are either run by army officers or are contractors working for commands like Unit 61398, according to officials with knowledge of its classified content."

If you assume that the Chinese military is only going after government and military secrets, you would be wrong. The writers report that the "Comment Crew has drained terabytes of data from companies like Coca-Cola." Of even greater concern is the fact that the Comment Crew and other such groups are increasingly focused "on companies involved in the critical infrastructure of the United States — its electrical power grid, gas lines and waterworks." They note that "one target was a company with remote access to more than 60 percent of oil and gas pipelines in North America." More on that story below.

Not surprisingly, Chinese government spokesmen deny that China is engaged in cyber espionage. The truth, of course, is that China and many other governments (including the U.S.) have active cybersecurity units. To make that point, Sanger, Barboza, and Perlroth report, "Working with Israel, the United States has used malicious software called Stuxnet to disrupt Iran’s uranium enrichment program. But government officials insist they operate under strict, if classified, rules that bar using offensive weapons for nonmilitary purposes or stealing corporate data." Plausible deniability has always been an important tool in any government's kit. The Mandiant report makes such deniability a lot harder for the Chinese government to sell. The article reads like a best-selling "whodunit" novel whose prime suspects are named UglyGorilla and DOTA. The tale takes readers down dark cyber alleyways that lead to the streets of Shanghai and the murky ties with Chinese military.

As noted above, however, military and state secrets are not the only targets of Chinese hackers. Coca-Cola databases were attacked as its "executives were negotiating what would have been the largest foreign purchase of a Chinese company." During that time, the "Comment Crew was busy rummaging through [the company's] computers in an apparent effort to learn more about Coca-Cola’s negotiation strategy." The tale continues:

"The attack on Coca-Cola began, like hundreds before it, with a seemingly innocuous e-mail to an executive that was, in fact, a spearphishing attack. When the executive clicked on a malicious link in the e-mail, it gave the attackers a foothold inside Coca-Cola’s network. From inside, they sent confidential company files through a maze of computers back to Shanghai, on a weekly basis, unnoticed."

As a result of all of this activity, "Obama administration officials say they are planning to tell China’s new leaders in coming weeks that the volume and sophistication of the attacks have become so intense that they threaten the fundamental relationship between Washington and Beijing." That's not a good thing. When the world's two largest trade elephants start charging one another, the earth is going to shake. As Sanger, Barboza, and Perlroth write, "Mr. Obama faces a vexing choice: In a sprawling, vital relationship with China, is it worth a major confrontation between the world's largest and second largest economy over computer hacking?" The answer is likely to be in the affirmative given that Comment Crew has attacked organizations holding vital information about critical infrastructure. Sanger, Barboza, and Perlroth explain:

"The most troubling attack to date, security experts say, was a successful invasion of the Canadian arm of Telvent. The company, now owned by Schneider Electric, designs software that gives oil and gas pipeline companies and power grid operators remote access to valves, switches and security systems. Telvent keeps detailed blueprints on more than half of all the oil and gas pipelines in North and South America, and has access to their systems. In September [2012], Telvent Canada told customers that attackers had broken into its systems and taken project files. That access was immediately cut, so that the intruders could not take command of the systems. ... Security researchers who studied the malware used in the attack ... confirmed that the perpetrators were the Comment Crew. 'This is terrifying because — forget about the country — if someone hired me and told me they wanted to have the offensive capability to take out as many critical systems as possible, I would be going after the vendors and do things like what happened to Telvent,' [Dale]. Peterson of Digital Bond said. 'It's the holy grail.'"

The New York Times is not the only media outlet that been writing about Chinese hackers. Bloomberg Business published an interesting video and article on the subject as well. That's really the point of this post: No organization is completely safe from serious hacking efforts. "In an ever-increasingly digital world," writes John Casaretto, "many have become immune to the news of ongoing threats that persist on the internet, breaches, privacy, attacks happen every day and once in a while one of them is significant enough to hit the news." ["One World Labs Takes on Data Leaks in the Dark Web," SiliconANGLE, 18 February 2013] He continues:

"Sadly, even organizations fall under this false sense of security, feeling their risk and their security is solid, that all that is going on with their information is known and secured. The bare truth is that the breaches and data leaks we hear about form only the tip of the iceberg of what is really going on out there. The 'Dark Web' is probably the best way to describe this, the places where search engines do not go and things you can only find if you are looking for it; it includes botnets, anonymous networking, C&C networks - in places all over the world, including the U.S."

Casaretto warns that "once your data is out there, you are at risk." He explains:

"For anyone that thinks that their four-walls are secure, think again. No amount of egress security, DLP, predictive security models if you were even that far ahead of the pack can account for everything given so many variables for data leakage. Among the many vectors are smartphones, web browsing, social engineering, the risk of leaked information may even come from your own IT staff. As it turns out that time and time again, information is accidentally exposed even in the most innocent of circumstances."

Technology can help. Casaretto reports that "One World Labs (OWL) has developed a one-of-a-kind software engine that seeks, indexes and collects information on a platform called Open Source Intelligence Gathering (OSINT)." He continues:

"With 1% of the deep web accessible to the common person, it goes where no one else can, indexing deep into the nether reaches of the net. These are the kind of places where information is incoming, largely never even seen, much less shared by Google and typically unbeknownst to the company whose information has leaked. From countless forums, file-sharing sites, listservs, IRC channels, ftp servers and more there is a constant, nefarious publishing and sharing of information that could have your name on it. ... OSINT is a fascinating engine, comprised of analytics, semantics, and Big Data elements. The platform is built on a distributed clustered framework. As you can imagine, the index itself is distributed, encrypted and built with the utmost security layers throughout. Access to the system is tight. Network design comes into play as many, many sites are not appreciative of an index that could be scraping their information. The operation engages its tasks through a complicated and non-static web of anonymous networking and thousands of running proxy configurations designed to avoid detection and maintain access to sometimes super-secretive environments. If a company were to assign a human to engage in this type of discovery, they could quickly after some training, find and discover at a rate of about 1 page per minute. The OSINT engine scrapes 75 pages per second and is tuned to detect across the semantics of the most prevalent languages, complete with variations."

Another technology that can help involves Secure Information Sharing (SIS). Enterra’s Secure Information Sharing technology can be utilized to facilitate the sharing of information across partner communities. This framework is founded on an automated rules management process that monitors source policies and the environment where they are applied (i.e., situational awareness) and an Attribute Based Access Control (ABAC) model that coordinates information/data sharing across a federated group environment. The SIS framework applies policy-driven automated rules to ensure individuals are able to quickly and securely access their data resources to effectively perform their missions. It strengthens existing layered security solutions that are often not enough protection from cyber attacks, especially when orchestrated and/or aided by foreign governments like Russia and China.

Scott Brinker's Epicenters of Targeted Marketing

In two recent posts (The Future of Big Data, Part 2 and Part 3), I discussed some observations about the connections between big data and marketing made by marketing technologist Scott Brinker. In a new post, Brinker discusses "another way to visualize the relationship between big data and the other innovations happening in the marketing department." ["3 epicenters of innovation in modern marketing," Chief Marketing Technologist, 28 January 2013] His new visualization places the customer at the center of three activities: customer analytics; customer experiences; and customer communications.

Although the customer is not shown at the nexus of these three activities, he or she is clearly present. Brinker's activities surround the customer with personalization, targeting, and a broader community. It's clear from his writing that Brinker is a very smart guy. It's also clear that he believes targeted marketing is going to be one of the defining characteristics of the modern business landscape. He starts his discussion about his three epicenters with customer communication. He writes:

"The first [epicenter] is customer communications and the revolution brought about by social media. Marketing communications has, rather quickly, moved from being a one-way broadcast to a more personal, two-way interaction with customers, prospects, and influencers, all interconnected together. Organizations have had to embrace operational and cultural changes to adapt — many are still wrestling with those changes. But while technology certainly triggered this wave of innovation in customer communications, the software in this space is probably the least technically challenging for marketers to adopt. There is great innovation in this sphere, but it's not primarily technical in nature."

Since customer communications can have such a significant impact on a company, Brinker is justified in making it one of his marketing epicenters. Although he appears to be a bit dismissive about technologies involved in customer communications, it should be remembered that he is only writing about the technologies involved in the communications not in the collection and analysis of the data involved. Dealing with that kind of unstructured data is not a trivial challenge; which is why customer analytics is the next epicenter he discusses. He writes:

"The second [epicenter] is customer analytics, using analytics and big data to better understand and measure customer opportunities. There is unquestionably a lot of value to be unlocked here, which is why the 'data revolution' in marketing is so big right now. However, these innovations are generally much more technical in nature. While there are some easy first steps here — high-level dashboards, basic web analytics, and data visualization tools such as Tableau — the technical water quickly gets deep. I picture it like a 'continental shelf' of shallow data analytics. Once you drop off the edge into the deep data ocean, the software is considerably more complicated and isn't nearly as plug-and-play. To really master customer analytics requires harder skills, such as statistics, modeling, predictive analytics, programming, data mining, etc. Acquiring these skills and integrating them into the marketing team will likely take more time and effort than the adoption curve associated with social media."

To highlight how challenging it is to deal with unstructured data, Amazon just awarded its grand prize in innovation to a Utah-based start-up company named ContactPoint. Last year the company "launched LogMyCalls, software that records incoming phone calls from customers and generates data that companies can analyze to update their marketing, develop sales leads, chart close rates and improve customer service." ["Utah's ContactPoint wins Amazon grand prize for innovation," by Paul Beebe, Salt Lake Tribune, 30 January 2013] The final epicenter discussed by Brinker is customer experience. He writes:

"The third [epicenter] is customer experience, delivering remarkable customer experiences at every touchpoint in the customer lifecycle. In our digitally-malleable world, marketers can now wield technology to craft customer experiences that are powerfully differentiated — on a scale that was impossible to conceive not too many years ago. I believe that the scale of this 'experience revolution' will dwarf the other two."

Providing an extraordinary customer experience is important whether or not that experience takes place online or in a traditional store setting. A Deloitte report entitled, The Next Evolution: Store 3.0, concludes that, in order to keep brick-and-mortar stores relevant and inviting for consumers, retailers must "deliver a tailored experience" for shoppers. The report states:

"It is an experience that begins before customers enter the physical store and continues long after they leave. Through the lens of the desired future customer experience, retailers should step back and ask themselves hard questions about where they are and where they need to go."

One of the reasons that experience is important is because brick-and-mortar stores have been suffering from a malady called "showrooming" that involves consumers going into stores specifically to examine products that they later purchase online. Allen Weiner, Research Vice President with Gartner, believes that content marketing is one way that traditional stores can provide shoppers with experiences and counter the showrooming trend. ["Can Content Marketing Beat Showrooming?, Gartner, 25 January 2013] He writes:

"For those businesses with adequate capital budgets, solutions such as Nearbuy Systems allow you to turn your Wi-Fi network into a proprietary content delivery mechanism which offers customers a personalized in-store shopping experience (like Barnes and Noble offers for customers who bring their Nooks to the store) via their mobile devices. Even better is the ability to track a customer’s in-store behavior such as how long he spent in a particular aisle."

Brinker believes that customer experience is so critical that the remainder of his article focuses primarily on that subject. He writes:

"There's already a growing customer experience movement in the marketing department — with the embrace of user experience and design (not just art/graphic design) professionals. But ultimately code is the clay from which most of these experiences are sculpted. It's with a mix of pre-packaged marketing software, custom developed applications, technical configuration, and plenty of 'script' glue to tie everything together that these experiences are actually built and delivered — leveraging the state-of-the-art in hardware technologies (e.g., the latest smartphones and tablets) and major web services (e.g., the latest capabilities in Facebook). Understanding how these technologies can be synthesized into compelling customer experiences — and having the technical capabilities to execute on those visions — presents a significant challenge for most marketing teams. But the opportunity is huge, strategically elevating marketing from communications to experiences. Marketing is still the champion of the brand. But the brand is now fully a direct function of customer experience."

Analysts at TIBCO Software agree with Brinker that technology lies at the heart of targeted marketing and the experiences it can provide. "Today," they write, "customer intent clues are widely available via comments on social networks, previous browsing behavior, email open rates, call center and sales department interactions and past receptiveness to offers and promotions. But this big data gold mine is spread across multiple channels that often fall outside a company's internal network landscape. And that means determining customer intent requires data analysis to mine for the insight that can bolster sales." ["4 Ways to Use Data Analysis to Pinpoint Customer Intent," Trends and Outliers, 30 January 2013] TIBCO analysts recommend four steps to help identify customer intent using data analysis. They are:

1. "Identify where the customer journey begins. 'Is it on the search engine or email message, or is it on the landing page? Often, the path to the source prior to the website visit is crucial to understand intent,' notes the ClickZ post. 'Were they on a competitor's site? Were they reading an industry article? Were they searching for free shipping offers?'
2. Collect all data from customer actions. Capture data from kiosks, call centers, mobile apps and web data. Hone in on product views, shopping basket additions, comments, searches, video views and help requests.
3. Study patterns. Determine which customers are reading or writing product reviews. When do they look at shipping information. What products are being compared or which product bundles are studied? After analytics uncovers patterns, companies can then tailor offers.
4. Use data as research in lieu of expensive surveys. 'Use your web data to assess what people actually do. Look for patterns in segments as well as feedback behavior – do reviews feature certain product characteristics? Highlight those in your marketing and up-selling, 'according to ClickZ. 'Web data can also improve your segmentation strategy, because it lets you segment on how (and potentially why) customers shop, not where they shop or who they are.'"

Brinker believes the end result (i.e., the "mission") is the "big experience." He concludes:

"Big experience is nearly unlimited in the innovations that lie ahead. They're not all technical in nature, but a significant portion of them are rooted in technology. To pursue these opportunities, marketing must expand its technical capabilities beyond data analytics. This is why marketing technologists, not just data scientists, should be a part of every marketing team's growth plans moving forward. Of course, some of the most fascinating areas in marketing are at the intersections of these three domains. For instance, the intersection between customer analytics and customer experience, where personalization and big testing are sprouting. Fundamentally, modern marketing is about combining all these innovations into a cohesive strategy and operational structure. That is why, although big data is big, it shouldn't distract marketing leaders from constructing an organization that is capable of orchestrating all of these innovations together into a powerful and differentiated brand for the 21st century."

In previous posts about innovation, I have made the point that a lot of (if not most) innovation takes place at the intersections of disciplines. Along these boundaries is where you find differing perspectives and new ways of combining existing ideas and technologies to create something entirely new -- like a big experience.

Big Trouble for Big Data, Part 1

Ray Gallagher reports that defense contractor Raytheon "has secretly developed software capable of tracking people's movements and predicting future behaviour by mining data from social networking websites." ["Software that tracks people on social media created by defence firm," The Guardian, 10 February 2013] This "extreme-scale analytics" system, that Raytheon calls Rapid Information Overlay Technology (or RIOT), reportedly gathers "vast amounts of information about people from websites including Facebook, Twitter and Foursquare." Raytheon claims that "it has not sold the software ... to any clients" but "acknowledged the technology was shared with US government and industry as part of a joint research and development effort, in 2010, to help build a national security system capable of analysing 'trillions of entities' from cyberspace." It certainly comes as no surprise that web-based activities are being tracked. The question is: Why has the discovery of Raytheon's system created such a stir?

According to Gallagher, it is the "controversial techniques that have attracted interest from intelligence and national security agencies, at the same time prompting civil liberties and online privacy concerns." He continues:

"The sophisticated technology demonstrates how the same social networks that helped propel the Arab Spring revolutions can be transformed into a 'Google for spies' and tapped as a means of monitoring and control. Using Riot it is possible to gain an entire snapshot of a person's life – their friends, the places they visit charted on a map – in little more than a few clicks of a button."

That kind of "big brother" tactic inevitably raises privacy concerns and could result in significant backlash. Such backlash would cause concern to any manufacturer or retailer that counts on big data analytics to help them connect better with their customers. Gallagher explains a little more about how Riot works and what it can determine. He writes:

"Riot pulls out this information, showing not only ... photographs posted onto social networks by individuals, but also the location at which the photographs were taken. ... Riot can display on a spider diagram the associations and relationships between individuals online by looking at who they have communicated with over Twitter. It can also mine data from Facebook and sift GPS location information from Foursquare, a mobile phone app used by more than 25 million people to alert friends of their whereabouts. The Foursquare data can be used to display, in graph form, the top 10 places visited by tracked individuals and the times at which they visited them."

It should be noted that "mining from public websites for law enforcement is considered legal in most countries. In February last year, for instance, the FBI requested help to develop a social-media mining application for monitoring 'bad actors or groups'." But Ginger McCall, an attorney with the Washington-based Electronic Privacy Information Center, told Gallagher that "the Raytheon technology raised concerns about how troves of user data could be covertly collected without oversight or regulation. 'Social networking sites are often not transparent about what information is shared and how it is shared,' McCall said. 'Users may be posting information that they believe will be viewed only by their friends, but instead, it is being viewed by government officials or pulled in by data collection services like the Riot search.'" Needless to say, Raytheon is not happy that the existence of Riot has been revealed. Gallagher continues:

"Jared Adams, a spokesman for Raytheon's intelligence and information systems department, said in an email: 'Riot is a big data analytics system design we are working on with industry, national labs and commercial partners to help turn massive amounts of data into useable information to help meet our nation's rapidly changing security needs. Its innovative privacy features are the most robust that we're aware of, enabling the sharing and analysis of data without personally identifiable information [such as social security numbers, bank or other financial account information] being disclosed.'"

Gallagher reports that last December Raytheon filed for a patent on the system. Although Raytheon is a U.S.-based company, the stir over the existence of Riot started with The Guardian article in the U.K. Another British newspaper, The Telegraph, picked up on the story and noted that one reason it has raised concern is that the British parliament recently passed a controversial "Communications Bill in Britain which would authorise the monitoring of phone calls, emails and internet usage. Ministers insist the reforms are vital for countering paedophiles, extremists and fraudsters but civil liberties have attacked the Bill's scope and branded it a 'snoopers' charter'." ["Warning over social networking 'snooping' technology," 10 February 2013]

The revelation about Raytheon's Riot system is not the first time that privacy concerns have been raised in connection with Big Data. Last June, for example, Quentin Hardy wrote, "Even without knowing your name, increasingly, everything about you is out there. Whether and how you guard your privacy in an online world we are building up every day has become increasingly urgent." ["Rethinking Privacy in an Era of Big Data," New York Times, 4 June 2012] Hardy reports that at a conference on Big Data held last spring at the University of California, Berkeley, Danah Boyd, a senior researcher at Microsoft Research, stated, "Privacy is a source of tremendous tension and anxiety in Big Data. ... It's a general anxiety that you can't pinpoint, this odd moment of creepiness." She then asked, "Is this moving towards a society that we want to build?" Hardy continues:

"Privacy is not a universal or timeless quality. It is redefined by who one is talking to, or by the expectations of the larger society. In some countries, a woman's ankle is a private matter; in some times and places, sexual orientations away from the norm are deeply private, or publicly celebrated. Privacy, Ms. Boyd notes, is not the same as security or anonymity. It is an ability to have control over one's definition within an environment that is fully understood. Something, arguably, no one has anymore. 'Defaults around how we interact have changed,' she said. 'A conversation in the hallway is private by default, public by effort. Online, our interactions become public by default, private by effort.'"

Obviously, Hardy was writing before the existence of Riot was revealed; nevertheless, he pointed out that a remarkably complete portfolio on you could be assembled using online data. He noted, "By triangulating different sets of data (you are suddenly asking lots of people on LinkedIn for endorsements on you as a worker, and on Foursquare you seem to be checking in at midday near a competitor’s location), people can now conclude things about you (you're probably interviewing for a job there) that are radically different from either set of public information." He continued:

"What is to be done? Ms. Boyd has made a specialty of studying young people's behavior on the Internet. She says they are now often seeking power over their environment through misdirection, such as continually making and destroying Facebook accounts, or steganography, a cryptographic term for hiding things in plain sight by obscuring their true meaning. 'Someone writes, "I'm sick and tired of all this," and it gets "liked" by 32 people,' she said. 'When I started doing my fieldwork I could tell you what people were talking about. Now I can’t.' That is a placeholder solution, and Ms. Boyd sees only one certainty for which we should prepare. 'Regulation is coming,' she says. 'You may not like it, you may close your eyes and hold your nose, but it is coming.' The issue is what the regulation looks like, and how well it is considered. 'Technologists need to re-engage with regulators,' she says. 'We need to get to a model where we really understand usage.' Right now, even among the highest geek circles, 'we have very low levels of computational literacy, data literacy, media literacy, and all of these are contributing to the fears.'"

Obviously, manufacturers, retailers, and marketers are all concerned stakeholders when it comes to the collection, storage, and analysis of big data. They should also be a part of any discussions involving future regulation. More importantly, they need to work with consumers with to establish cooperative measures for data sharing. I'll discuss more about that subject tomorrow.

What Follows Globalization, Part 2

In yesterday's post, I cited a number of pundits who worry that no clear model has emerged as the obvious candidate for what follows globalization. I noted that fifteen years ago Daniel Yergin predicted that "globality" would be the natural predecessor of globalization. He predicted that globalization would sweep away international barriers to trade and growth and "a new global reality" would emerge. As I noted yesterday, globality certainly doesn't seem to be the direction in which the world is heading; especially considering that regionalization is on the rise. "We talk of being a global community," writes Klaus Schwab, founder and executive chairman of the World Economic Forum, "but our institutions and behaviors tend to run counter to these currents." ["A 5-point plan for a new globalism," Fortune, 30 January 2013] He believes "Our new reality -- complex, interconnected, and faster than ever -- means that the need for global cooperation and global solutions has never been greater." He then asks, "So what criteria should underpin a global system for the 21st century?" First, and foremost, he believes, we need cooperation. He explains:

"Such a system must foster cooperation. We're all in this together. Governments, business, and civil society cannot do it on their own. Global issues are interrelated, and multi-stakeholder responses such as public-private partnerships bring innovative solutions to the table. They engage the passion, purpose, and networks of civil society with the resources and experience of business."

The concern of many analysts is that the world seems to becoming less cooperative and less connected instead of more cooperative and more connected (see my post entitled Globalization: Retreating or Transforming?). Joshua Cooper Ramo, vice chairman at Kissinger Associates, writes, "They forgot to tell us globalization has a reverse gear." ["Globalism goes backward," Fortune, 20 November 2012] Noting some of the trends discussed in yesterday's post as well as the fact that the Great Recession has turned many countries inward, he writes:

"Given that the idea of a more interconnected world has been at the heart of our assumptions about our future, such figures are, as [Pascal Lamy, head of the World Trade Organization] said, worrying. They echo in our politics as loudly as they do in our markets. People remember a world of protectionism, understand the consequences of no international cooperation, and nervously consider the icy historical proposition of 18th-century French economist Frédéric Bastiat: 'When goods don't cross borders, armies will.' Watching the country that in recent years has benefited most from an open world-trading order rebel against it is like hearing that people are looking for ways to opt out of gravity. But here it is: Two-thirds of Americans now believe more trade is bad for the country."

If that doesn't concern you, it should. Every credible analyst I know believes that global economic growth and prosperity depends on a rising middle class in emerging market countries. If America wants to glean a share of that growth and prosperity, it must embrace trade. Americans are too much in debt to continue to fuel a healthy and growing consumer economy on their own. We need to reach out to the millions of consumers being lifted out of poverty as a result of globalization. The second point of Schwab's path to globalism involves systemic action. He explains:

"A global system must also approach challenges in a systemic, integrated way. The issues on the global agenda are all interrelated, but our current system is too compartmentalized: the World Trade Organization for trade, the World Health Organization for health, and the International Monetary Fund for finance. We also have to establish the necessary interlinkages to create coherence. For example, how do we strike the optimal balance between the G-20 and the United Nations? How do we best engage nongovernmental actors? We need flexible networks -- more heterarchies, fewer hierarchies."

The business world is quickly learning that the future belongs to organizations that master horizontal processes and break down traditional corporate silos. Schwab insists that the same lesson needs to be learned on the global stage. Ramo undoubtedly agrees that global actions need to be coordinated and cross-functional cooperation fostered. He also believes that regionalization is more likely to see the kind of cooperation and coordination discussed by Schwab. Ramo writes:

"Networks are the essential metaphor of our age, what assembly lines were 150 years ago. And we've now moved from an era of extensive network development -- that 'Hi, nice to meet you' phase -- to something deeper, and often that means nearer and more local. This shift is being abetted by any number of practical factors: Prices of air and sea freight have inflated alongside fuel costs in recent years, for instance, putting a premium on closeness. ... But there's something else at work too: a sense that we may have gone too far, too fast, that we don't really understand the dynamics of the systems we've built. ... To be honest, we entered the first flush of globalization with the wrong tools. We didn't know how to manage interconnected bank risk, couldn't imagine the danger of asymmetrical threats like terrorism, and really believed that every time we tipped over a government in some other country, an American-style system would grow back in its place. ... The right answer is to pause, and to reengineer."

Schwab certainly agrees with Ramo that some re-engineering is required. His third point involves strategy-driven decision making. He writes:

"The system should be strategic, not crisis-driven. Most of our energy is currently absorbed by reactive rather than proactive measures. Managing crises instead of thinking about the future leads to defensive attitudes. We must adapt to a changing world, not defend outdated models."

Concerning this point, Ramo waggles an accusing finger at American nationalists. He can't understand why they have seemingly abandoned a national strategy that has helped maintain America's prominent role in world affairs. He writes:

"[The] logic of increasing returns tells us a lot about exactly what America needs to do next. Think of it this way: Every time you use a dollar bill, reach for a credit card, dial your cellphone, check your GPS -- nearly anytime you finger something with a network connection, what you are really doing is touching the outlines of a global system of interconnection that has been built with U.S. values. You're connecting to a system where we've been able to lock in the rest of the world. It's the reason that there is one Silicon Valley and one Wall Street -- and also why it is ridiculous that we are letting our dominance of those systems leach away not to external competition, but from the inside out. Whether it be ever more challenging visa regulations for engineers or ever greedier pollution of the use of finance, we're rotting away the basis of what has let us prosper. Maintaining a standard like the U.S. dollar as the global reserve currency is, in a world of locked-in systems, the greatest possible strategic advantage. Losing it would hurt our national security far more than almost any other international threat."

That doesn't mean that America has the right (or can) impose its will on the world. Ramo is arguing that smart Americans have been able to convince the world that the U.S. has a plan that is good for everyone. As the world has become more prone to crises, including within the U.S., the world isn't so sure that American leaders still have the vision or the will necessary to move the world forward in the right direction. Legitimacy is the fourth point discussed by Schwab. He writes:

"A global system must continually demonstrate legitimacy. Today, this goes beyond mandates based on democratic principles; it includes clear objectives and concrete results. We undoubtedly have a delivery problem. And since promised actions are not fulfilled, we also have a trust deficit with governments, international organizations, and business."

Conspiracy theorists always see something sinister in terms like "a global system." The fact of the matter, however, is that mail wouldn't move and planes wouldn't routinely and safely cross borders if there weren't international standards. In the virtual world, the World Wide Web wouldn't function if global standards weren't in place. Ensuring that organizations that set such standards have "legitimacy" is the best guarantee that countries, including the U.S., will have their voices heard and their interests considered when standards are adopted. The fact that so many people distrust (and at worst hate) politicians and government bureaucrats highlights the fact that organizational legitimacy is not going to be easy to achieve. Schwab's final point throws fuel on the fire of conspiracy. He writes:

"Our global governance system must embrace the notion of global citizenship. In an interconnected world, it is in the interest of nation-states to strive for solutions to truly global challenges, such as climate change. Today, we not only need a Charter of Human Rights, but must also expand this notion to include responsibilities."

The terms "global governance" and "global citizenship" are catalysts that heat up nationalistic fervor in many people. Nevertheless, people of every nationality do share the planet and actions on one side of the world can have consequences on the other side. There are numerous challenges that don't recognize borders and are blind to a person's ethnicity, culture, or religious beliefs. These challenges do require a coordinated effort to address. Schwab concludes, "Integrating these five criteria into our global system will be challenging, but if we don't, we will continue applying topical treatments to conditions that fundamentally require global cardiac care." The last thing we need is for the patient to die.

What Follows Globalization, Part 1

David M. Smick, editor of International Economy magazine, believes that the globalization model is crumbling and, with no apparent model to succeed it, he predicts that things are going to get very ugly. In fact, he predicts that things will get so bad over the coming four years that the Democratic Party could be out of power for a generation because President Obama was unlucky enough to get re-elected just as things are falling apart. ["What will replace the globalization model?" Washington Post, 16 October 2012] He writes:

"The risk stems from something more fundamental: The globalization model of the past 30 years is cracking up. And there appears to be no new model to replace it. Since April, an ugly economic world has turned uglier. The annual growth rate of total global exports has collapsed. Exports were a crucial engine in powering the U.S. economy out of the worst of the recession in the second half of 2009 and remain important for growth. Lately, even China and India, which were thought able to decouple from the weakness of the industrialized world, have fallen victim to the seizing up of global trade. The World Trade Organization is slashing its estimates for trade growth. The U.N. Conference on Trade and Development reports that economic growth is weakening worldwide."

The litany of evidence that Smick uses to make his case goes on to include failing trade negotiations, currency wars, rising nationalism, "and new software-based cost-cutting manufacturing technologies." He notes that the one remaining traditional flow of globalization that has been relatively untouched (i.e., the flow of capital) is "under worldwide assault." Smick feels compelled to conclude that the globalization model is all but dead. He continues:

"The U.S. economy can limp along under these conditions, but achieving the level of robust growth needed for full employment will be difficult. The rise of geopolitical tensions from globalization's collapse will increase U.S. investor nervousness, contributing to a debilitating risk-averse environment. It is difficult to underestimate the degree to which this flawed, sometimes frightening, good we call globalization has been the proverbial goose that laid the golden eggs. As a result, the public has unrealistic expectations about how much the economy can deliver in a post-globalization world."

I'm not quite as gloomy as Smick because, as I wrote in a previous post entitled Regionalization is on the Rise, regionalization is going to remain a prominent (if not increasing) feature of tomorrow's business landscape. This change will require businesses to adjust, but the adjustment shouldn't be too difficult. Regionalization makes more sense than globalization in many ways (e.g., ensuring production is closer to consumers and adapting products to fit regional tastes and lifestyles). Nevertheless, Smick concludes:

"Despite its flaws, globalization has been a wealth-creating machine. That is why the world's governments spent $15 trillion and central banks increased their balance sheets by $5 trillion in response to the financial crisis, essentially to try to save that machine. Yet the globalization model is cracking up anyway — and there's no replacement in sight."

The end of globalization portrayed by Smick is far different than the ending predicted by Daniel Yergin back in 1998. Fifteen years ago he popularized the term "globality" to describe "a hypothetical condition in which the process of globalization is complete or nearly so, barriers have fallen, and 'a new global reality' is emerging." Globality certainly doesn't seem to be the direction in which the world is heading; especially considering that regionalization is on the rise. Smick, however, doesn't offer any thoughts about what model he believes should succeed globalization if that model collapses completely. Eric Cazdyn and Imre Szeman agree that more thought needs to be given to this topic. In fact, they argue in their book entitled After Globalization that the "world is disabled by a fatal flaw: the inability to think 'an after' to globalization."

In many ways, Smick's observations mirror those of John Ralston Saul. Five years ago he was preaching that after three decades of globalization, the results are mixed. "These," he wrote, "include some remarkable successes, some disturbing failures and a collection of what might best be called running sores." ["After Globalization," The Tyee, 27 June 2005] He continued:

"That very clear idea of Globalization is now slipping away. Much of it is already gone. Parts of it will probably remain. The field is crowded with other competing ideas, ideologies and influences ranging from the positive to the catastrophic. In this atmosphere of confusion, we can't be sure what is coming next, although we could almost certainly influence the outcome."

Saul claims that the "Privatize, privatize, privatize" mantra that was continuously chanted by "Prophets of Globalization" was eventually replaced by calls for national rule of law. Like Smick, Cazdyn, and Szeman, Saul viewed the lack of a real understanding of what was going to follow globalization as troubling. He wrote:

"We are transiting one of those moments that separate more driven or coherent eras. It is like being in a vacuum, except that this is a chaotic vacuum, one filled with dense disorder and contradictory tendencies. Think of it as a storm between two weather fronts. Or think of those moments in fast-moving sports, like soccer or hockey, when a team loses its momentum and there is furious, disordered activity until one side finds the pattern and the energy to give it control. These moments tend to begin with denial on all sides. The confusion frightens those who thought they were setting the direction. And it disappoints those who criticized that direction. There is nothing decisive or noble about the situation. The options are not clear."

Saul, however, does not simply toss up his hands in the face of impending doom. He looks chaos in the eyes and sees opportunity. He explains:

"A period of uncertainty is also one of choice, and therefore of opportunity. We cannot know how long it will last. Probably not long. And those choices that set the future will come insidiously, in fits and starts. Some have already presented themselves and somehow been processed, without our fully registering that a determining step was taken. The shape of what comes next will therefore be decided - a conscious act - or it will be left to various interest groups to decide for us, or simply to fate and circumstance. It will probably emerge from a mix of all three. The soundness of the outcome will depend on the balance between these necessary mechanisms. The most dangerous disequilibrium will have favoured fate and circumstance over the other two. The most mediocre, interest groups. The soundest equilibrium would be led by conscious public decisions."

If Saul is correct, then the Great Recession hopefully snapped a few government and business leaders back to reality. As I noted above, regionalization (which is taking place within the larger framework of globalization) is likely to be one of the first follow-on models to globalization. It will not look anything like globality (as described above). It will rely on big data to help governments create smarter cities (in an increasingly urbanized world) and to help companies understand the nuances of a growing global middle class. That middle class needs to continue to grow if the global economy is going to expand and spur further prosperity. I agree with Saul that we are at a point of decision. He concludes:

"To believe in the possibility of change is something very precise. It means that we believe in the reality of choice. That there are choices. That we have the power to choose in the hope of altering society for the greater good. ... The conviction that citizens have such power lies at the heart of the idea of civilization as a shared project. And the more people are confident that there are real choices, the more they want to vote - a minimal act - and of greater importance, the more they want to become involved in their society."

Making communities, regions, and nations smarter and more prosperous requires the involvement of all stakeholders -- especially citizens/consumers. There are a number of analysts who believe that new technologies are going to transfer control over personal data to the individual. Saul certainly seems to be counted among that number. Access to that personal data will be critical if analytics are going to help us make smarter choices about the future. We have the opportunity to understand our world better and, thus, make better choices. What follows globalization? No one really knows; but, I don't believe the future needs to be scary. Tomorrow I'll discuss an article that has received a lot of attention. It involves a "5-point plan for a new globalism" suggested by Klaus Schwab, founder and executive chairman of the World Economic Forum.

The Future of Big Data, Part 3

In Part 2 of this series, I discussed some thoughts on the future of big data offered by marketing technologist Scott Brinker. He wrote that the "big leap" for marketing companies will be when they add "big testing" to "big data" to create a "big experience" for clients. He believes this will be so revolutionary that "it's worth expanding a bit on what's meant by 'big testing' and 'big experience' — and why they're such a big leap for most marketers." ["The big data bubble in marketing -- but a bigger future," Chief Marketing Technologist, 21 January 2013] Brinker first discusses big testing. He writes:

"A few months ago, I wrote a post, "We want to test bold, new ideas that always work." It highlighted recent research by the Corporate Executive Board showing that the far majority of Fortune 1000 marketers think their organizations are not effective at test-and-learn experiments. One reason why: at least half didn't think an experiment should ever fail. I believe this is the single biggest obstacle most organizations face: their culture and politics dissuade people from trying experiments because failure of an experiment implies failure of the tester. Who wants to stick their neck out in that environment? So either people don't test anything, or they test in a non-controlled manner so that outcomes are comfortably subject to caveats and interpretations."

In my posts about innovations and innovative companies, I've repeatedly pointed out that a fear of failure is a sure sign that a company doesn't have a innovative culture. In a TEDx Kyoto talk, Catherine Courage, a leader for a Silicon Valley product design group, noted that Thomas Edison failed to find the right filament for his light bulb a thousand times. Edison didn't see this as 999 failures, but 999 steps in a 1,000-step process to success. That's a very different mindset than most companies embrace. Brinker claims that even companies claiming to embrace testing often conduct only superficial tests that involve little risk. "Perhaps the greatest damage they do," he writes, "is that they give people the illusion of engaging in real experimentation. ... Big testing is qualitatively different." He then offers "three ways in which big testing earns its 'big' label"(as depicted in the following graphic):

Source: Scott Brinker

Brinker writes this about big ideas:

"First, it's about testing big ideas. Headlines and button colors are fine, but they barely scratch the surface. The real power of testing is unleashed when you use it to learn ways of engaging entirely new segments of customers or to pioneer innovative new ways of selling or delivering your product or service. It's about taking the surprising insights that big data may reveal and proving (or disproving) their value and efficacy. ... A good way to know if a test is truly meaningful or not: state out loud what the hypothesis is. If there is no hypothesis, or it's something patently banal — like 'chartreuse green buttons will have at least a 0.01% increase in clicks over forest green buttons' — then you're just rearranging deck chairs on the Titanic."

Brinker next talks about "big teams." Long-time readers of this blog know that I'm a supporter of cross-functional teams. Team members from different disciplines bring with them valuable perspectives that are not shared by others outside their field. Brinker writes:

"Big testing is not restricted to a tiny priesthood of test-masters who jealously guard the data, tools, or governance rights for running tests. Big testing empowers and encourages an open, big team of testers across the organization. Many different people are given the capability to run tests in the context of their particular work. ... Of course, this is usually best when there's a strong shared vision and some basic training — enough to keep people loosely coordinated and out of trouble. But harnessing a (relatively) large and distributed force is a source of great power — both in sheer manpower and in rich diversity of ideas."

When Brinker talks about "big teams," he emphasizes the "big." He calls it "massively parallel marketing." He claims, "Massively parallel marketing covers a much broader set of possibilities much faster, but it is a fundamentally different kind of organization than the traditional command-and-control hierarchy." He next discusses why big testing should be a big deal. He writes:

"Big testing is ... championed by executives from the top-down. Experimentation isn't a risk to your job. On the contrary, not experimenting — in particular, not experimenting with big ideas — will put you out of favor. I was at a big data conference ... where Gary Loveman, CEO of the Caesars casino empire, gave a presentation about how important meaningful testing is in his organization. 'There are three ways to get fired from Caesars,' he states matter-of-factly. 'You can steal from the company. You can harass a co-worker. Or you can fail to have a control group for an experiment.' That's making a big deal of testing. At that same conference, Hal Varian, the chief economist at Google shared that Google runs around 10,000 experiments per year, with about 500 experiments going on at any one time. Testing is an integral part of their company culture. It's worth pointing out that both of these companies are superstars when it comes to big data, but in both cases, they strongly emphasized the criticality of running real-world tests to capture the value buried in that data. As Hal Varian said simply, 'Experimentation is the gold standard of causality.' It tells you which actions generate the best response."

Brinker's final discussion is about providing a big experience for customers. Big experiences are customized, personalized experiences. They speak directly to the consumer rather than merely interrupt whatever he or she is otherwise doing. Brinker explains:

"The incredible potential of big data — and big testing, for that matter — is near worthless if an organization can't employ it in the service of delivering remarkable customer experiences. Big experience should be the big tent under which big data and big testing sing and dance."

Brinker believes that "there are three important connections between big experience and big data/big testing." He writes:

"First, data and testing give direction to the burgeoning customer experience movement. Customer experience is becoming the banner under which modern marketing marches to victory. It's why everything is now marketing. The tectonic shift of marketing's responsibilities, from mostly customer communications to increasingly lifecycle customer experiences, is one of the 5 meta-trends of modern marketing. It's an opportunity for marketing to shine at the highest level of the organization."

The dilemma concerning the creation of a big experience is the fact that "building amazing customer experiences is a lot of work, time-consuming, and expensive." Brinker obviously believes the effort is worth the result or he wouldn't have written his post. He continues:

"Using big data and big testing provide a structured and systematic way to hone in on the right experiences to build big. Designers and customer experience professionals can leverage these capabilities to identify rich, new pockets of opportunity for exploration, as well as to refine their productions along the way. Done properly — where big data and big testing are employed in the service of remarkable customer experiences — the relationship between these parts can be harmonious, not contentious. Data and testing minimize the downside and maximize the upside of pursuing big, new customer experience innovations."

Even if you are headed in the right direction, if you execute poorly you will lose your competitive advantage. Big testing, writes Brinker, is "only valid if the customer experiences in which it's executed are good." He continues:

"If you run a split-test of two concepts, say offer A (a price emphasis) and offer B (a quality emphasis), testing a hypothesis of which will motivate a particular customer segment more — but both experiences are kind of crappy — then the results of your test are useless. Probably both will perform poorly, but maybe offer A gets slightly more traction. But it's not unequivocally because the market prefers price over quality. The audience exposed to offer B may have found the offer of high quality, juxtaposed with an experience that stunk of low quality, to be incongruous and have no credibility. Superficially claiming 'quality' is not the same as truly exuding quality."

He concludes, "It's far better if big testing is a comparison of two very good customer experiences to see which is best." The final connection discussed by Brinker involves a customer focus rather than a product focus. He writes:

"Big data and big testing give us a way to construct amazing customer experiences around customers rather than products. ... Even though we know we're largely past the age of mass marketing — at least in terms of channels and touchpoints — the mindset and organizational structure of most businesses still revolves around fixed products and services. Even as they try to embrace a more customer-centric way of selling those products and services, they're still anchored to segmenting their market by product at their core. 'Most companies count their profits in terms of products, not customers,' remarked Gary Loveman at that conference in answer to the question of why most companies are so bad at using analytics to drive better results. The real vision of big experience is using data and testing to reconfigure our organizations around our different groups of customers, finding and favoring the most profitable and crafting ever more tailored experiences to each. I'm not saying that lightly — I appreciate how big of a challenge that is. But ultimately, that is how businesses will unlock the massive value that the big data movement is promising."

I'm a big believer in the kind of targeted marketing Brinker describes. In conclusion, Brinker offers a few afterthoughts about big data. His first observation is, "Data is fuel — invest more in the engine." He explains:

"In the face of all this data mania in marketing, the real revolution is shaping a data-driven organization. Wrangling big data is an important part of that, but big testing and big experience are the ways in which that data lead is turned into customer gold. While there is certainly cool technology available to power that entire 'big stack,' the biggest part of embracing this revolution will be shifting your organizational structure, behavior, and culture to truly leverage it. ... Data is like fuel. It's certainly valuable, and there's a big industry emerging around the extraction, refinement, and distribution of that fuel. Hey, Exxon Mobil is a $400 billion mega-corporation. But the big oil companies would be nearly worthless without the transportation industry. Because it's the engines and the cars and the jet airplanes that use that fuel to move the world. An engine without fuel is fairly useless; but a barrel of fuel without an engine is equally pointless. It's only the two together — the fuel and the engine — that unleash the potential of both. If you want to really harness the power of big data, build the organizational engine to use it."

All of the analysts cited in this 3-part series about the future of big data agree on one thing: It's not the data, but what you do with the data, that matters. I like Brinker's fuel and engine analogy. That's why at Enterra Solutions we refer to many of our technology services as data analysis engines created to obtain specific kinds of results. Over-hyped or not, the future of big data still looks bright.