Enterra Insights

The Impact of Omnichannel Operations on the Supply Chain

At the end of my post entitled Surviving in the Omni-channel World, I indicated that, in a later post, I would discuss the impact of omnichannel operations on the supply chain. In that post, I quoted the editorial staff of SupplyChainBrain who wrote: "With more customers shopping online and on their mobile devices, it seems imperative that retailers offer different channels for fulfillment to not only keep prices low, but to remain competitive and foster customer loyalty." ["Multichannel Fulfillment Is The New Normal," 11 March 2013] Dave Kilzer, senior vice president of supply chain solutions with Idhasoft, told the SCB staff that many companies fail to understand the nuances between "all-channel" and "omnichannel" operations. He stated that omnichannel "describes a distributor's ability to provide an equally high level of service, regardless of the means by which product is purchased or shipped. All-channel can result in excess inventory, physically separated and unable to cross boundaries to meet the needs of the moment. Omni-channel, by contrast, makes it possible to hold inventory in multiple locations while treating it in one coherent 'bucket'." ["'The 'Omni-Channel' - and Its Implications for the Supply Chain," SupplyChainBrain, 11 July 2013]

As I understand it, the differences between all-channel and omnichannel operations described by Kilzer rest on a distributor's ability to tap all sources of supply regardless of where they are located. The article helps explain this:

"The two concepts involve different rules of allocation, and the supporting technology has to recognize that. It's getting to the point, says Kilzer, 'where much of fulfillment in e-commerce is now being fueled, not from a DC, but from the backroom of a retail location.' In essence, the distributor is reaching all the way to the store shelf. The setup requires pinpoint visibility throughout the distribution chain, including the ability to manage the 'black hole' that is created when a consumer takes product off the shelf and moves to the checkout stand. 'Those two seconds are how tightly inventory has to be tracked,' says Kilzer."

Clearly, successful omnichannel operations involve a number of technologies that can track supplies in real-time as well as predictive analytics that ensure supply is going to be able to meet demand. That's why Ann Grackin, CEO of ChainLink Research, told the SCB staff that "more dollars are being spent on operational challenges: customer experience touch points (single view of the customer, POS, store and web design); IT infrastructure challenges from web to wireless in the store; and supply chain – which includes inbound merchandise allocation, replenishment, inventory management, and fulfillment to the customer." ["Supply Chain Takes Its Power Position in the Retail Industry!," SupplyChainBrain, 11 March 2013] If, as the SCB staff claims, omnichannel fulfillment is the new norm, it should come as no surprise that the staff believes that the supply chain now sits in a new power position. To ensure that supply chain professionals make the most of their empowered position, the ChainLink analysts assert that those professionals need to ensure they have the following technology tools in their kit:

"• WMS –Various warehouse technologies to support inventory management and omnichannel fulfillment. The past model of web-only or catalog-only inventory infuriates potential buyers. Amazon has shown the way: pass-through shopping with inventory status from partners – merchants and manufacturers to locate the specific products – puts private label retailers to shame. Private label, who own the end-to-end – from manufacturers through to point of sale – still can’t provide seamless inventory locating and fulfillment. Yet Amazon can, without owning the back of the supply chain. Voila – collaboration and process mastery!

"• Source tagging and B2B transacting – RFID, bar-coding and EDI will continue to grow to provide seamless communications and visibility across the trading network.

"• Transportation and Trade – Logistics technology and process methods such as collaboration for carrier consolidation to reduce inbound coordination and bottlenecks.

"• Mobile and Wireless – Our research showed more mobile for supply chain than the shopper experience! End-to-end visibility, coordination with third-party services, direct store delivery, and same-day service to customers will grow. Wireless infrastructure in stores will grow not only to support mobile checkout and tablets for sales assistance but store operation such as inventory management and pricing.

"• Demand Management – Demand process and technologies continue to evolve. One method yet to master is how to make sense out of the great customer insights coming from social. There are solutions that provide these, but they are 'newbies.' Few traditional demand forecasting players provide these – at their peril. Retailers and brand companies are reaching beyond the traditionalists to access these.

"• Social Enterprise – There are three flavors of social: one for customer facing marketing and customer support; one for B2B collaboration; and one for knowledge sharing in the enterprise. The latter two we call Enterprise Social Networking. Retailers will begin to understand the differences and not just use social for marketing."

Research conducted by SD Retail Consulting concluded that, despite the impact that omnichannel operations will have on the future survival of businesses, many large companies have been slow to adapt. ["Largest Retailers Slow to Adapt to Needs of Omni-Channel Shopping Environment, Study Finds," by SD Retail Consulting, SupplyChainBrain, 7 June 2013] Some of the more significant findings from their research include:

"• U.S. trails U.K. for in-store pick-up of web-based orders: Only 29% of U.S. retailers surveyed have implemented in-store pick-up options, and a mere 24% are planning to unveil a pilot program by late 2013. These figures represent a stark contrast to U.K. retailers, where 78% of retailers surveyed have deployed in-store pick-ups. U.K. retailers continue to improve on the convenience of in-store pick-up programs, testing additional benefits such as free parking for customers who pick up during morning business hours

"• Mobile POS Is Rare: Only 18% of U.S. retailers have implemented mobile POS systems across a significant portion of their stores, and in most of those cases, retailers have only rolled it out to select groups of stores, rather than entire chains. Further, mobile POS is still typically utilized for only one or two specific uses (i.e., line busting or search/assistance within specific departments), rather than leveraging the full extent of its capabilities (CRM, labor scheduling, traffic counters, etc.)

"• Store staff are not getting effective cross-channel training: 80% of U.S. and U.K. retailers said they have not invested sufficiently in training their staff on how to handle multichannel customers in-store, whether on how to handle 'show rooming,' competitive price-matching, in-store pick-up requests, or addressing specific product knowledge customers may have gained from the web. Additionally, fewer than 25% of retailers surveyed indicated that their field management was providing the leadership necessary to drive improved productivity through their physical stores in this new multichannel environment

"• No store associate incentive and recognition for cross-channel selling: Less than 10% of retailers surveyed are currently compensating their associates in some way that recognizes their contribution to cross-channel sales. Retailers with cross-channel customers acknowledge that while the store may not ring the sale, their associates play a critical part in driving company top-line sales, yet methods for compensating employees for contributing to the sale by servicing the shopper in-store (before they actually transact on-line) have yet to be formalized."

What clearly pops out to me is that visibility and collaboration need to be improved dramatically if omnichannel operations are to succeed. In order to achieve the necessary level of collaboration, new key performance indicators need to be developed that take in account new digital paths to purchase that are being embraced by many consumers.

Web 3.0: Does Anybody Really Know What It Will Be?

Half a dozen years ago I wrote a post entitled Web 3.0 Still Advancing -- Even if People Don't Know What to Call It. I noted that Web 1.0 could be called the Information Web and that the Information Web morphed into the Social Web (commonly called Web 2.0). For most of the intervening years, people have been talking about the Semantic Web being the successor to the Social Web and they have been referring to it as Web 3.0. Back in 2009, however, Miguel Helft wrote about the amazing rise of "Twitter, Facebook, and other similar services" and how they "are increasingly becoming the nation's virtual water coolers. They spread information quickly, sometimes before the mass media do, and their ricocheting bursts of text and links become an instant record of Americans' collective preoccupations." Helft labeled the next evolution in the Web "Real-Time Search." ["How High Will Real-Time Search Fly?" New York Times, 24 October 2009] He didn't refer to Real-Time Search as Web 3.0, but he clearly saw it as the next step towards something new.

The following year Alice Truong interviewed Herman Lam, chief executive of Cyberport Management Company Limited, a government-owned company in Hong Kong that manages a development called Cyberport, which was supposed to be Hong Kong's silicon valley. During that interview, Lam did talk specifically about Web 3.0, but not as the Semantic Web. He simply called Web 3.0 "smarter computing." ["How to Make Web 3.0 Reality," Wall Street Journal, 7 December 2010] Lam told Truong:

"Web 3.0 is about how to make life even easier for us in the future. We believe with all this technological change that we could be looking at a paradigm shift and how people interact with the Web. When this changes, it opens up an opportunity for small startups, new companies and young entrepreneurs. ... In the world of Web 3.0, the Internet should know I won't be able to watch my favorite TV show. It should automatically record it and book a time slot for me to catch up on this show."

Lam's vision of Web 3.0 is certainly something different than Helft's Real-Time Search and certainly doesn't sound like traditional descriptions of the Semantic Web. It sounds more like a cross between a personal digital assistant and a smart TV. Daniel Nations probably got it right when he wrote, "The truth is that predicting the Web 3.0 future is a guessing game. A fundamental change in how we use the web could be based on an evolution of how we are using the web now, a breakthrough in web technology, or just a technological breakthrough in general." ["What is Web 3.0?" About.com] Nation's goes on to state, "Many people ponder the use of advanced artificial intelligence as the next big breakthrough on the web. One of the chief advantages of social media is that it factors in human intelligence." Certainly, the Semantic Web won't emerge without artificial intelligence technology. Concerning the Semantic Web, Nations writes:

"There is already a lot of work going into the idea of a semantic web, which is a web where all information is categorized and stored in such a way that a computer can understand it as well as a human. Many view this as a combination of artificial intelligence and the semantic web. The semantic web will teach the computer what the data means, and this will evolve into artificial intelligence that can utilize that information."

Another possibility raised by Nations is what he calls the "Ever-Present Web 3.0." He explains:

"Not so much a prediction of what the Web 3.0 future holds so much as the catalyst that will bring it about, the ever-present Web 3.0 has to do with the increasing popularity of mobile Internet devices and the merger of entertainment systems and the Web. The merger of computers as a source for music, movies, and more puts the Internet at the center of both our work and our play. Within a decade, Internet access on our mobile devices (cell phones, smartphones, pocket pcs) will be as popular as text messaging. This will make the Internet always present in our lives: at work, at home, on the road, out to dinner, wherever we go, the Internet will be there."

More recently the so-called Internet of Things has been put forth as a viable candidate for Web 3.0. ["Introducing Web 3.0: Internet of Things," by Xath Cruz, Cloud Times, 9 September 2013] As I've explained in previous posts about the Internet of Things, it's basically a machine-to-machine network. Cruz explains that it "basically refers to the concept of every gadget and appliance we own being interconnected via the Internet." Sarah Perez acknowledges that both the Ever-Present Web and the Internet of Things were potential candidates to be crowned Web 3.0, but notes: "None of these got to win the Web 3.0 branding." Perez nominates another candidate — the Ephemeralnet. ["The Rise Of The Ephemeralnet," TechCrunch, 30 June 2013] She believes the Ephemeral Net will emerge as people combine the desire to share their lives with friends as well as maintain a modicum of privacy. She writes:

"While some confuse the 'Ephemeralnet' with the so-called 'SnapchatNet,' in reality, it’s not only a new way to socialize online, it's a new way to think about everything. You can see the trend also in the rise of the (somewhat) anonymous and untraceable digital currency Bitcoin. Unlike traditional transactions, Bitcoin is decentralized and doesn't require banks or governmental oversight or involvement. And though it’s not entirely anonymous, there are already efforts, like Zerocoin, working to change that. ... At the end of the day, the Ephemeralnet may never get to become as defining a trend as Web 2.0 once was. Though it may find adoption beyond the demographics of its youngest participants, it will continue to share the web with the services that preceded it – services too big, too habitual, and too lucrative, to die off entirely."

Kevin Lindquist labels his version of Web 3.0 "'The Integrated Web' (Not 'The Semantic Web')." ["Web 3.0 is Here! Is Your Small Business Ready?" YFS Magazine, 30 August 2013] He explains:

"Many are pushing for this thing called 'The Semantic Web' where an app will be able to understand user interaction in such a way that it will not only return directly relevant results, but also indirectly relevant results. For instance, a search for 'showtimes 84003' returning what time movies are playing at the Cinemark in American Fork, Utah, including nearby places for dinner (because you are probably going on a date) or gas stations en route because it knows that your car is out of gas. These concepts are exciting, innovative, and forward thinking, but there is a critical evolutionary step missing in between the 'The Social Web' and 'The Semantic Web'. Instead of the telephone lines that we constructed between web apps and services during Web 2.0, Web 3.0 brings bridges and process to support the future of the app. Integrated services are the future, integrated applications of those services will take over the world. Being able to take one object, and pass it through multiple services to accomplish a task is that critical evolutionary step before 'The Semantic Web' becomes meaningful. Instead of a world where the app returns data about indirectly relevant things, what if it could instead automatically perform those indirectly relevant services for you upon approval? In other words, if you are going to show me 'The Semantic Web' show me a semantic web that can get things done without me needing to go between a number of different apps, web pages, etc."

I started this post with a reference to another post I wrote six years ago. Over those six years, there have been a number of technological advances. One thing that hasn't advanced, however, is agreement on what Web 3.0 should be called and what exactly its characteristics will be. In the end, that really doesn't matter very much. We will get what we will get — and we will probably like it.

Do You Know Who Your "Influentials" Are?

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

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

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

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

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

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

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

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

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

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

Big Data Continues to Raise Privacy Concerns

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Fellow Travelers: Big Data and the Internet of Things

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

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

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

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

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

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

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

Source: General Electric

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

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

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

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

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

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

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

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

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

Saving the World with Big Data, Part 2

In the first part of this discussion about how Big Data can help save the world, I cited a number of experts who explained why they see great potential in Big Data analytics for tackling big global problems. The areas in which they saw the most potential included: agriculture, finance and poverty reduction, healthcare, and disaster response. Let's take a brief look at how Big Data can help in each of those areas beginning with Agriculture.

Agriculture

"Farmers today produce three times as much food as they did 50 years ago using just 12 percent more land, thanks to new technologies and better farming practices," reports Prachi Patel. "But the global playing field isn't level. In Africa, farmers produce a fraction of what they could, according to the Forum for Agricultural Research in Africa, and most barely get by, struggling against infertile soil, drought, and diseases." ["Feeding the World With Big Data," IEEE Spectrum, 14 May 2013] It's clear from the title of his article that Patel believes Big Data has a role to play in solving the growing challenge of feeding the world's burgeoning population. This is important, he explains, because "helping farmers—in Africa and elsewhere — produce more will be key to lifting millions out of poverty and sustainably feeding a world population of 9 billion in 2050." He continues:

"Food-policy experts believe that a crucial step toward that goal is to give farmers, scientists, and entrepreneurs unhindered access to agricultural data which is generated at research centers worldwide. ... If these data sets are made freely available, the possibilities for their use are endless, says Piers Bocock of the CGIAR Consortium of International Agricultural Research Centers, in Montpellier, France. At [a] conference [held earlier this year], experts from universities and research institutions presented apps they've developed using data that's already publicly available. These included MyFarm, an Android-based country-specific multilingual app that helps train farmers to give agricultural advice to other small farmers, and Aqueduct, an interactive tool that provides high-resolution maps of water-related risks. In Africa, where even the poorest farmer carries a cellphone, open-data evangelists envision an incredible — and not completely improbable — scenario. 'Imagine this,' Bocock says. 'A woman standing in a field in Malawi has just borrowed money to start her own farm. What if an app on her mobile phone geo-locates her and then, from this ever-growing data ecosystem of knowledge, is able to identify the soil type and needs of that specific field, and then tell her where, locally, she can buy the seeds she needs and when to plant, harvest, et cetera?' Making such 'what if' scenarios a reality will require increasing amounts of free, accessible agricultural research data that's easy to use — not just by humans but also by machines."

Finance and Poverty Reduction

Gillian Tett discusses how Big Data can be used to make societies more resilient. For example, it can be used to "spot economic trends and predict looming problems in a beneficial way." ["Big data is watching you," Financial Times, 10 August 2012] She explains:

"Aid groups are not just tracking ... physical phones; they are also starting to watch levels of mobile phone usage and patterns of bill payment, too. If this suddenly changes, it can indicate rising levels of economic distress, far more accurately than, say, GDP data. ... [If any organisation] spots a sudden increase in certain keywords, this can also provide an early warning of distress."

Tett also sees a darker side to gathering financial information. She notes that "companies can use the data ... to develop credit scores for the poor." She believes such scores would lead to less access to credit than the poor already have. Mark van Rijmenam reports, "Cignifi, a Brazilian startup, for example developed a technology to recognize patterns in the usages of mobile devices. The system recognizes phone-calls, text messages and data usage and based on this information it can recognize someone's lifestyle and his/her corresponding credit risk profile." ["How Big Data Can Help the Developing World Beat Poverty," SmartData Collective, 2 August 2013] How that information is used will determine whether the poor will be helped or hindered in their efforts to escape poverty.

Healthcare

Several references in the first post in this two-part series touched on how healthcare can be improved through the use of Big Data. For example, van Rijmenam discussed how using Call Detail Records could be used to map changes in the slum population and, as a result, "direct latrine and water pipe building efforts for the benefit of the slums residents." As I've pointed out in previous posts, providing the poor with better sanitation facilities can dramatically improve their health. Tett provided an example how Big Data was used during the Haitian earthquake a few years back to prevent epidemics. She wrote:

"The population scattered when the tremors hit, leaving aid agencies scrambling to work out where to send help. Traditionally, they could only have done this by flying over the affected areas, or travelling on the ground. But some researchers at Columbia University and the Karolinska Institute took a different tack: they started tracking the Sim cards inside mobile phones owned by Haitians, to work out where their owners were located or moving. That helped them to 'accurately analyse the destination of more than 600,000 people who were displaced from Port au Prince”, as a UN report says. Then, when a cholera epidemic hit Haiti later, the same researchers tracked the Sim cards again, to put medicine in the correct locations – and prevent the disease from spreading. ... Medical researchers have learnt in the past couple of years that social media references to infection area are powerful early warning signal of epidemics – and more timely than official alerts from government doctors."

Mike Wheatley agrees, "'Spying' on people's public data can actually help medical professionals to save lives." ["Big Data’s Still On Track To Save The World," SiliconANGLE, 9 July 2013] He explains:

"Researchers at Johns Hopkins University have been ... downloading tweets at random and sifting through this data to flag any and all mentions of flu or cold-like symptoms. Because the tweets are geo-tagged, the researchers can then figure out where the sickness reports are coming from, cross-referencing this with flu data from the Center for Disease Control to build up a picture of how the virus spreads, and more importantly predict where it might spread to next. Of course, there are many countries where Twitter isn't all that popular and so researchers are forced to use more creative methods to track disease. One way of doing so, pioneered in Kenya, is through using the so-called metadata from cell phone calls to try and predict the spread of malaria. Now you might think that's an impossible task if you're not actually listening into people's calls, but you'd be wrong. Caroline Buckee, a professor at Harvard University's Center for Communicable Disease, hit upon the idea of assigning specific cell phone users to an 'area' based on the location their calls and SMS messages originated from. These areas where then rated according to the level of malaria risk, which was calculated according to reported cases of the disease. Using a mathematical model, the researchers were able to accurately predict someone's probability of becoming infected in each 'area'."

As the old medical adage goes, prevention is always better than cure – and a lot cheaper as well.

Disaster Response

The final area I want to discuss is disaster response. Just like in healthcare, prevention of disasters is the best course of action. Unfortunately, preventing natural disasters isn't possible. For that reason, mitigation is often best the course to follow. Early warning can sometimes help prevent natural disasters from taking an even larger toll in life and destruction. Wheatley explains:

"Saving lives is a worthy cause, but the motivation to save money can be just as powerful (if not more so), and once again Big Data is helping us to do so. ... We can't stop the forces of nature, but by preparing for the coming onslaught we can certainly minimize the damage it'll cause."

The onslaught of disasters can only be detected and mitigated if data is collected and analyzed quickly enough to make a difference. Tett notes that applications like Twitter and Facebook can often provide the necessary early warning. "References to food or ethnic strife," she writes, "may indicate the onset of famine or civil unrest." Earlier she noted that Big Data can be used to track displaced populations so that response organizations know both the extent of the problem and where they need to be to assist. Tett continues:

"Robert Kirkpatrick, a former IT expert who now runs the UN's Global Pulse unit, ... dreams of using these data to create the social media equivalent of 'metereological stations', which can test the winds of public debate, spot economic trends and predict looming problems in a beneficial way. Even if this idea sounds far-fetched, economists can already use this information to track how economies are developing in poor regions of the world with much more precision and timeliness than ever before. That mobile phone in my pocket, in other words, does not just connect me to my friends. It is now part of a shared human experience – and database – that spans the globe, and which is growing in depth and power each day. And that has implications most of us have barely begun to understand. It is both a sobering and exciting thought, whether you are now sitting on a holiday beach, in a humdrum office – or anywhere else in the world."

Big Data may not be able to save the world on its own; but it certainly is a good tool to have in the kit of organizations and governments that can help solve global challenges.

Saving the World with Big Data, Part 1

"We're not asking enough of Big Data, and we're still getting in its way," writes Stephen Collins, Chief Executive Officer of Bazaarvoice. "We should be using Big Data to do what we can't, not just help us do what we're already doing, better." ["Big Data needs big problems," Bazaarvoice: Blog, 29 July 2013] I like Collins' headline. It's optimistic about the potential of big data analytics. When it comes to big challenges, what could be bigger than saving the world? You might be asking, "Save it from what?" Good question. As you will read below and in the final segment of this series, proponents of Big Data have a number of things in mind. Collins believes that we need to establish a new paradigm for approaching problem solving. The old paradigm, he writes, involves defining the problem, then training the machine (i.e., the computer) to solve it. The new paradigm, he insists, should involve training the machine to define the problem. He explains:

"Right now, humans are still better at at least one thing: defining the problem. But that's not to say Big Data can't eventually outdo us in this arena, too. If we can train machines to solve problems, we can train them to define them as well. Once this happens, we close the circuit—the true power of Big Data is realized. The system starts working on its own, and better than ever before. Here are the conditions which must be met for Big Data to flourish:

1. It must be ubiquitous; accessing data from everywhere we let it.
2. It must be always on, constantly gathering, processing, comparing, and acting.
3. It must be empowered to act in the moment."

I certainly agree with the first two conditions; but, I would have to add a few modifiers to the third one. Empowering action implies that whoever is in charge of the computer has the authority to act. When it comes to solving the world's problems, there simply is no single organization or individual with that kind of authority. Does that mean that Big Data can't be useful in solving the world's challenges? Certainly not. Mike Wheatley writes, "Big Data's reputation has taken a bit of a battering lately thanks to allegations that the NSA is collecting and storing people's web and phone records, leading to a wider debate about the appropriateness of such extensive data-gathering operations. But this negative publicity detracts from the reality of Big Data today, which for the most part will only benefit society as a whole. There's more to these massive data sets than simply catching terrorists (or spying on law abiding citizens)." ["Big Data’s Still On Track To Save The World," SiliconANGLE, 9 July 2013] Analysts involved in the Global Pulse initiative, an innovation initiative launched by the Executive Office of the United Nations Secretary-General, agree with Wheatley that Big Data has the potential of addressing significant world challenges (as the following video demonstrates).

Mark van Rijmenam, founder of BigData-startups.com, also believes that Big Data is going to make the world a better place. He notes that "the Engineering Social Systems department (ESS) of Harvard has collected several inspiring use cases." ["How Big Data Can Help the Developing World Beat Poverty," SmartData Collective, 2 August 2013] He continues:

"Big data offers for example the possibility to predict food shortages by combining variables such as drought, weather conditions, migrations, market prices, seasonal variation and previous productions. Or what about the possibility to better understand the dynamics of slum residents using mobile data to develop predictive models to better serve the poorest? For example using [Call Detail Records (CDR)] information to map changes in the slum population and direct latrine and water pipe building efforts for the benefit of the slums residents. Time-series analyses performed on CDR combined with random surveys can lead to better insights about the dynamics of rural economies and provide insights on how governments should respond to economic shocks in rural and poor environments. The World Bank shows an example where big data is used to ensure the right distribution of the right medicines to the right location at the right moment in time. A pilot programme called SMS for Life improved the distribution of malaria drugs at a health facility level in rural Tanzania, reducing facilities without stock from 78% to 26%."

One thread that weaves its way through the Global Pulse initiative, as well as van Rijmenam's observations, is ubiquity of mobile devices. Van Rijmenam writes:

"For the vast amount of the poor, a simple or basic mobile phone is the only interactive interaction with the World Wide Web. Although in the developed world smartphones may seem to be the common device, they still only account for 10.44% of the global mobile website traffic. On the other hand, traditional mobiles take up 78.98% of mobile worldwide website traffic (with tablets taking 10.58% of the traffic). Luckily there are vast opportunities for the developing world to use data created by basic mobile devices to identify needs, provide services, and predict and prevent crises for the benefit of the poor."

Gillian Tett adds:

"These days, there are about 2.5 billion people in emerging markets countries who own a mobile phone. In places such as the Philippines, Mexico and South Africa, mobile phone coverage is nearly 100 per cent of the population, while in Uganda it is 85 per cent. That has not only left people better connected than before – which has big political and commercial implications – it has also made their movements, habits and ideas far more transparent. And that is significant, given that it has often been extremely hard to monitor poor societies in the past, particularly when they are scattered over large regions." ["Big data is watching you," Financial Times, 10 August 2012]

Mike Wheatley asserts that one source of global data stands out above the rest when it comes to capturing real-time information – Twitter. "Few are as superior as Twitter," he writes, "and not just because of its widespread user-base that's spread across the globe. More important, tools such as TwitterHose facilitate this data calection, allowing anyone to download 1% of tweets made during a specified hour at random, giving researchers a nice cross-section of the Twitterverse."

Although mobile data provides a wealth of useful information, van Rijmenam understands that mobile data alone is insufficient to address all potential problem areas. He concludes:

"Big data can be as a catalyst for long lasting improvements, but we will have to look further ahead to see that. Mobile data alone is not sufficient to really create opportunities that could impact developing countries on the long term. Therefore, more data sources are required, ranging from data from NGO's, to public data and social data. ... Big data offers many opportunities for the developing world to beat poverty, but it will require different organisations to work together in order to achieve lasting results. In addition, the joining organisations should ensure transparency and availability of the data. Transparency will stimulate awareness of the possibilities, ensure data accountability and reduce bureaucracy and corruption. Availability of the data will ensure that multiple data sources can be fused, such as CDRs, open data, social data, government data, NGO data and corporate data, to create valuable and relevant new insights that will truly have a long term impact."

Van Rijmenam's anecdotal use cases touch on many of the areas that have been identified as areas in which Big Data can be used to address seemingly intractable problems: agriculture, finance, healthcare, poverty reduction, and disaster response. In the final part of this discussion, I'll look a little closer at how Big Data can help solve problems in each of these areas.

Social Intelligence and Business

When you hear the term "social intelligence," what comes to mind? The term has been applied to two very different subjects. The term is most widely used in a new scientific field that is exploring complex social relationships and environments. Daniel Goleman, author of the book Wired to Connect, writes, "The most fundamental discovery of this new science: We are wired to connect." ["Social Intelligence"] He continues:

"Neuroscience has discovered that our brain's very design makes it sociable, inexorably drawn into an intimate brain-to-brain linkup whenever we engage with another person. That neural bridge lets us impact the brain — and so the body — of everyone we interact with, just as they do us. Even our most routine encounters act as regulators in the brain, priming emotions in us, some desirable, others not. The more strongly connected we are with someone emotionally, the greater the mutual force. The most potent exchanges occur with those people with whom we spend the greatest amount of time day in and day out, year after year — particularly those we care about the most. During these neural linkups, our brains engage in an emotional tango, a dance of feelings. Our social interactions operate as modulators, something like interpersonal thermostats that continually reset key aspects of our brain function as they orchestrate our emotions. The resulting feelings have far-reaching consequences, in turn rippling throughout our body, sending out cascades of hormones that regulate biological systems from our heart to immune cells. Perhaps most astonishing, science now tracks connections between the most stressful relationships and the very operation of specific genes that regulate the immune system. To a surprising extent, then, our relationships mold not just our experience, but our biology. The brain-to-brain link allows our strongest relationships to shape us in ways as benign as whether we laugh at the same jokes or as profound as which genes are (or are not) activated in t-cells, the immune system’s foot soldiers in the constant battle against invading bacteria and viruses."

That subject is fascinating to be sure; but, it is very different than what ListenLogic Chief Marketing Officer Mark Harrington thinks about when he discusses the term. Harrington thinks of the term "intelligence" in the information gathering sense of that word (i.e., think of the CIA). In his world, social intelligence is gleaning insights from the "billions upon billions of brand and product messages distributed across open channels and social networks everyday." ["Five Reasons Marketers Need to Embrace Social Intelligence," CMS Wire, 3 July 2013] Monitoring online chatter, he reports, is important because, "according to Dimensional Research, over 90 percent of consumers rely on independent online reviews of products and services as an integral aspect of their purchasing process." As a result, Harrington asserts that marketers "need to get sophisticated and vigilant in their analysis and understanding of these messages and how they can harm or help their business."

Kevin Glacken, Executive Vice President at ListenLogic, agrees with his colleague that the best marketing teams use social intelligence to protect and promote their business. "Many Marketing, Insight, Brand and Product teams are leading the charge within their organizations to extract true, actionable intelligence from the hype of 'big data'," he writes. "They are using advanced social intelligence, which is filled with unprecedented consumer insight, to set their strategies, guide their decision-making and drive innovation. While other teams wait and wonder if the promise of 'big data' will ever come to fruition, the ability to conduct digital consumer ethnography via real-time streaming 'big data' processing of billions of daily social discussions is revolutionizing how these groups understand, engage and win their markets." ["Marketers Leading the Charge to Unlock Value from Big Data," SmartData Collective, 13 July 2013] Glacken concludes:

"As the 'Age of the Consumer' progresses and consumers become more empowered with search engines, product comparisons and pricing tools, it is critical for Marketers to arm themselves with deep understanding of consumers and the decision points and factors they undertake en route to their purchases."

As the headline of Harrington's article states, he offers "five major reasons that marketers need to get serious about incorporating advanced social intelligence (ASI) into their strategies." The first reason involves the simple notion that the world is now connected. He calls it "a streaming world." He explains:

"It's a streaming world that sees billions upon billions of real-time comments and conversations everyday from customers, employees, management, shareholders, suppliers, prospects, competitors, unions, activists, advocates, influencers, politicians, regulators and journalists, among many others, from all corners of the open social universe. Ignoring the relevant messages and corresponding influence within this intelligence is no longer a luxury for businesses given the wealth of insight it delivers to helping understand the current and coming forces, positive and negative, impacting on your business. ... Incorporating advanced social intelligence into your marketing's strategic planning framework opens up real-time, dynamic, multidimensional insight to build and optimize your corporate and product strategies as markets and landscapes shift in a streaming world, thus allowing your actions and strategy to mirror the real-time movements within the market."

Most analysts agree that the clock speed at which business operates is getting faster each year. That is why real-time monitoring is also becoming more important. But real-time monitoring is only valuable if the collection and analysis system is capable of alerting decision-makers to potential problems or opportunities in near-real time as well. This is where cognitive reasoning computer systems can play an increasingly important role. Harrington's second reason that marketers need to get serious about social intelligence involves the insights that can be drawn from real-team or near-real-time analysis. He writes:

"For years companies have relied on generating intelligence through surveys and focus groups, often building the strategies for millions on the opinions of dozens. Given the typical slow-moving approach, inherent bias and common methodology flaws with these approaches, many businesses have made bad decisions and developed poor strategies because of a lack of genuine market insight available to them. This is no longer the case for marketers today. Marketing and brand teams now have a wealth of deep insight about their industries, markets, products and competitors if they are merely willing to listen to the millions of open social discussions. No longer is there a need for flawed approaches of building a strategy based on the opinions of a few customers."

Glacken agrees. "By analyzing millions upon millions of daily social conversations Marketers can understand their consumers like never before," he writes. "Advanced social intelligence provides clarity on the detailed personas within consumer segments as well as the likes, dislikes, interests, actions, attitudes, and beliefs of each of these consumer personas." That comment is a good segue to Harrington's third reason to utilize social intelligence — unprecedented understanding. He explains:

"For years, a major focal point across the marketing organization has been to 'know the customer.' By actually listening via digital ethnography, marketing teams can understand shoppers, prospects, customers and consumers on multidimensional levels. This intelligence elevates segmentation to a new level with the ability to personify consumers based on their attitudes, opinions, actions and experiences shared across the open social universe."

Harrington goes on to explain that social intelligence can also be used to construct "a specific customer journey ... leading up to a purchase. With this understanding, marketers can influence these critical moments with promotions, messaging, education, channels, packaging or features." His fourth reason for marketers to utilize social intelligence involves monitoring consumer sentiment. He writes:

"For years, sentiment or 'buzz' has been a primary driver for social monitoring. Unfortunately, this sentiment has been largely one-dimensional, simply telling if the market liked or disliked a brand with no actionable detail. The large issue has been that first generation tools have delivered questionable accuracy when it comes to sentiment. ... Today by using 'big data' processing and sophisticated concept modeling to understand the infinite ways consumers speak, incredibly detailed sentiment analysis can be mapped."

Because social intelligence is basically unstructured, natural language data, systems that can analyze natural language for its nuances and relationships is important for gaining critical insights. The final reason Harrington insists that marketers should use social intelligence is that it can help "crush" the competition. He explains:

"Aside from a deeper understanding of your markets, consumers and products, ASI can drive your competitive strategy both on deep inspection and real-time basis. Your enterprise can know your competitors better than they know themselves by understanding the issues, concerns, successes, flaws, strengths, weaknesses and decisions impacting and driving their business. Companies receiving this intelligence are using it to shape their strategic decision-making and tactical execution on a daily basis. If your competition is not going to listen to what the market is saying about them, then your business should seize that opportunity. This not only helps your organization gain a strategic upper hand with greater, more widespread market understanding, but also helps to shape your own decisions in beating the competition."

Harrington concludes that the world will become more socially connected which will make social intelligence even more valuable. Glacken concludes:

"To effectively take full advantage of the wealth of intelligence within the billions of daily discussions across the open social universe Marketers are unlocking the value of 'big social data' with advanced 'big data' processing at over one billion streaming operations per second. These organizations have realized that simple, first-generation keyword tools provide 'noisy' results focused on questionable 'buzz' that often cannot be strategically acted on. Given the sheer volume of social commentary (billions of daily comments across millions of open source channels) achieving advanced social intelligence now requires a streaming 'big data' solution to keep pace with the volume, velocity, and variety of the social data. In many leading brands, Marketers are the ones leading the charge to transform 'big data' hype into reality. Doing so is not only possible and affordable, but critical to succeeding into today's 'Age of the Consumer'."

Harrington and Glacken put forward a pretty compelling case for why social intelligence is growing in importance and why businesses would be foolish to avoid taking advantage of advanced systems that can properly analyze all of the data being generated on social channels.

Guest Post: Effective Supply Chain Management through Social Networking

Today's post was written by Nicholas Moores, a professional technology writer for Waer Systems.

The irrepressible force of social media has in recent years collided with the corporate world. These days, the image of a company on social media is crucial to its success, and businesses around the globe are having to learn quickly how to use this tool to their advantage.

One key benefit of today's immediate and ubiquitous media industry is the way in which a company can reinforce its brand and raise its profile. Twitter promotes itself as the "largest real time conversation ever created," and shows you how in 140 characters you can grow your business profile, monitor trends in your industry, and attract customers. Similarly, Facebook allows for a dialogue between company and customer. The customer is able to speak directly with the company, and the company in turn is able to publicly exercise their great customer service for all to see, and to advertise promotions to attract new business.

For the individual, platforms such as LinkedIn, the professional networking site, allows you to interact with colleagues and business links remotely, whilst promoting yourself to customers or potential employers. And what a way to network – with more than 225 million members, it's certainly the place to pinpoint exactly the skills you're looking for.

However, social media in industry is not just outward facing, but is being used increasingly within business to help manage supply chains, creating what Adrian Gonzalez, founder and president of Adelante SCM refers to as "social business networks." These networks are more about facilitating "people-to-people communication and collaboration," allowing real-time transparent conversation between peers groups. This function, in a widespread, even global supply chain, is proving to be a golden ticket.

Firstly, one is able to monitor the here and now of their supply chain production and needs. Logistical updates can be tracked, data can be shared, and progress can be monitored. All this means that should any problems arise they can be dealt with quickly and effectively -- pooling resource and ideas from across the entire network of suppliers.

Take for example the social media platform Socialtext, a company which improves business performance by "making it easier for employees to find the colleagues and information they need to solve challenges new and old." When the manufacturing company Industrial Mold and Machine (IMM) used this platform to revolutionize their processes, it resulted in the consistent achievement of "unprecedented production." On the shop floor, employees used iPads to mobilize their communication. This meant that employees could contact others and be contacted at all times. The result of this interaction was increased productivity; a 20% increase in "cut time" with a 40% decrease in labor hours. Coordinating via Socialtext from across the warehouse was a quick and easy innovation that allowed IMM to use its workforce effectively.

Across these platforms, this ongoing conversation enables companies to share best practices with one another, build and improve relationships, and share knowledge and data effortlessly. LinkedIn means that colleagues across the world can introduce themselves to one another, advertise their skill set and background, and network without having to meet in person. Document and process building can now avoid the black hole of email systems, as shared platforms allow for collaboration throughout a business, such as cloud, where multiple users can input into a document and feed back to one another simultaneously.

The high profile leaders of the social media industry are not the only competitors in this environment, as more and more industry specific social media emerges. Take, for example, Sourcemap, a company that allows both clients and colleagues access to supply chain information. This "social business network" consists of maps on which you can pinpoint the origins of a specific part of a product. This tool shows a transparent supply chain, and allows people to contact any supplier involved in the production.

These social networks can provide a wider view of the supply chain – a large kinetic entity, rather than static and separated cogs. Seeing how the chain functions as a whole can help businesses make decisions to increase efficiency, and cultivate innovation.

The interaction from the customer is also vital – it allows the consumer to speak directly with the business about its product, what it needs, what could be improved, and where they are falling down. If approached in the right way, social media can turn on the tap for all the information you could need, without stepping away from your desk.

But here is the one pitfall. We've all seen the damage social media can do to a company if it is used incorrectly, therefore it's essential to have a dedicated manager or team that handles this interaction and adheres to agreed process.

What's evident in today's climate is that social media won't be going away. It is now an integral and powerful tool available to all, and businesses must get on board, or get left behind.

The Benefits of Urbanization on Innovation

People continue to flock to cities. Although some migration is forced (see, for example "China’s Great Uprooting: Moving 250 Million Into Cities," by Ian Johnson, New York Times, 15 June 2013), most of it is voluntary. Many analysts believe there are good reasons that people prefer urban life (like better use of resources and better access to services), but Jim Russell isn't sure. He writes, "Greater population density drives innovation and productivity. Albeit a theory, urbanists rally around the idea. I'm skeptical of the claim. The conclusion supports the preferred geography, raising a red flag." ["The Magic of Cities," Sustainable Cities Collective, 9 June 2013] Russell states, however, that a new study by MIT researcher Wei Pan is tempering his skepticism. He cites an article by Emily Badger in which she interviews Pan. ["The Real Reason Cities Are Centers of Innovation," The Atlantic Cities, y June 2013] She begins her article by writing:

"It's obvious from human history that people have long found unique value in living and working in cities, even if for reasons they couldn't quite articulate. Put people together, and opportunities and ideas and wealth seem to grow at a more powerful rate than a simple sum of all our numbers. This has been intuitively true for centuries of city-dwellers."

In the information age, however, intuition is seldom satisfactory. Badger continues:

"There have been plenty of theories. Adam Smith famously figured that people become more productive when we're able to specialize, each of us honing a separate area of expertise. And when lots of us elbow into cities, we're able to specialize in ways that we can't when a rural farmer must also double as his own butcher, accountant and milkmaid. Other economists have suggested that cities become great agglomerators of industry when factories cluster together around economies of scale and communal access to transportation."

Pan and his associates (Gourab Ghoshal, Coco Krumme, Manuel Cebrian, and Alex Pentland), attempt to move beyond theory in a study entitled Urban characteristics attributable to density-driven tie formation. That's a title only an academician (or an academician's mother) could love. The study's abstract is just about as dry:

"Motivated by empirical evidence on the interplay between geography, population density and societal interaction, we propose a generative process for the evolution of social structure in cities. Our analytical and simulation results predict both super-linear scaling of social tie density and information flow as a function of the population. We demonstrate that our model provides a robust and accurate fit for the dependency of city characteristics with city size, ranging from individual-level dyadic interactions (number of acquaintances, volume of communication) to population-level variables (contagious disease rates, patenting activity, economic productivity and crime) without the need to appeal to modularity, specialization, or hierarchy."

In the paper, the authors write, "One of the enduring paradoxes of urban economics concerns why people continue to move to cities, despite elevated levels of crime, pollution, and wage premiums that have steadily lost ground to premiums on rent." Why indeed? Pan told Badger, "We think there's an underlying completely different way of thinking here, which is very different from the economist's way of thinking." The study builds on previous work by researchers at the Santa Fe Institute that proved "the math behind the power of cities: As they grow in population, all kinds of positive outcomes like patents and GDP and innovation (and negative ones like STDs and crime) grow at an exponential factor of 1.1 to 1.3. This means that all the benefits (and downsides) that come from cities don't just grow linearly; they grow super-linearly. Badger writes:

"As for why this happens, ... Pan pushes aside theories about the location of manufacturing or the specialty of trade. 'It's more fundamental than that,' he says. 'Cities are about people. It's just that simple.'"

Manufacturers and retailers certainly see cities in that fundamental way. As they view the future, they want to know how to reach the billions of people that live (and will live) in urban environments. Only big data analytics can help sort out the diversity, preferences, and locations of future urban consumers. Pan and his colleagues "argue that the underlying force that drives super-linear productivity in cities is the density with which we're able to form social ties. The larger your city, in other words, the more people (using this same super-linear scale) you’re likely to come into contact with." Pan told Badger, "If you think about productivity, it's all about ideas, information flows, how easily you can access ideas and opportunities. We believe that the interaction mechanism is what drives the productivity of the city." Badger continues:

"It's not possible for scientists to measure your social ties in the same way they can measure GPD or crime incidents or STD infections (despite their best wishes, they can't put sensors on all of us). But this study examined a proxy for the same idea: The researchers looked at phone logs between anonymized telephone numbers all over the country, in search of the number of people who we communicate with inside our own metropolitan statistical area. 'If you look at the interaction patterns of cities,' Pan says, 'You will see that they grow super-linearly with population with the same growth rate as productivity, as innovation, as crime, as HIV, as STDs.' All of those facets of urban life have appeared until now to share a somewhat mysterious mathematical relationship. But this research suggests that this particular super-linear growth rate is directly tied to how dense cities enable us to connect to each other. As cities grow, our connections to each other grow by an exponential factor. And those connections are the root of productivity. 'What really happens when you move to a big city is you get to know a lot of different people, although they are not necessarily your "friends,"' Pan says. 'These are the people who bring different ideas, bring different opportunities, and meetings with other great people that may help you.'"

Russell, however, isn't convinced that density is the most important factor when it comes to spurring innovation in urban environments, he believes it is migration. He writes:

"The magic of big cities is migration. A sprawling residential pattern doesn't dampen the effect. Talent can live in the core or super-commute into downtown. Concerning innovation, better to be an immigrant than work in [the] Big City. Migration matters more, much more, than density."

Other recent work, however, seems to support the density theory proposed by Pan and his colleagues. Economists Neil Lee of Lancaster University Management School and Andres Rodriguez-Pose of the London School of Economics conducted a survey "of roughly 1,600 small and medium enterprises across the United Kingdom." Their results indicated, "U.K. firms located in the city were indeed more likely than those in rural areas to report both new products (52 to 46 percent, respectively) and new processes (43 to 34 percent)." ["Cities Are Innovative Because They Contain More Ideas to Steal," by Eric Jaffe, The Atlantic Cities, 12 June 2013] Jaffe concludes:

"The city environment, ripe with chance exchanges and interactions, might only explain a sliver of new product development. Some complex combination of other forces (e.g., creative inspiration or specific demands or more approaches to problem-solving) is also involved. When it came to new business processes, however, the urban advantage seemed to rely almost entirely on ideas learned from neighboring firms (as opposed to original ideas). Here the city itself would appear to play its greatest role in innovation. Greater proximity to other firms, and perhaps also greater employee movement from company to company, no doubt increases the flow of outside information and leads to new ways of working."

In other words, density (i.e., greater proximity to other businesses) plays and important role in urban innovation. That doesn't mean that migration, as proposed by Russell, doesn't also matter. The bottom line is that magic does occur in cities and it is the result of increased opportunities for interactions between people.