Terra-forming the digital landscape: Can Apple and IBM teach you how your digital contrails shape the big dataset, thus leading to a societal shift in metacognition?
Bec Johnson – University of Calgary, 14th August, 2014
Copyright: Canadian Intellectual Property Office, No. 1115561
Table of contents
- Executive Summary
- Apple and IBM a birth of fire.
- Big data
- Cloud computing
- Social context and analysis
- The feedback loop: interact, create, define.
- Exhaust Data
- Human volition terra-forms the digital landscape
- How the Apple / IBM partnership could affect this.
- Bridging the gap using Small Business as the Catalyst
- Broader implications for society
- Barons, Architects and ethics.
- The individual and the State
- Cause and effect cognition Vs. Correlative inference mindset
- The master canvas
- Futurist postulations
- What should YOU do now?
Big data has been in existence for sometime, however, it is now being used on such a vast scale that it is changing the very fabric of society. The purpose of this article is not to explore all of the possible benefits and pitfalls of big data, there are many excellent treatments on that already, but to discuss how this may affect society on a broader and far reaching scale.
The 15th July 2014 announcement of the Apple and IBM partnership acted as a catalyst to these thoughts. If this highly dichotomous partnership is successful and is able to achieve its full potential, Apple and IBM may directly impact and affect the evolutionary path of the global society in respect to the technologies of big data and cloud computing.
I posit that should the Apple / IBM partnership (or some other, equally suitable, computer and technology company) act upon the current situation and seize the opportunity to be market leaders in educating the general populous on big data; then not only would they develop an unprecedented level of trust with the superstructure of the dataset (the mass public), but that they may be instrumental in helping society avoid a hegemonic society ruled by data barons and algorithmist architects.
Educating people on the power of their volition, their expression of consciousness, to terra-form the digital landscape would lead to the development of a big data mindset amongst the majority of the people. This mindset would lead to a greater understanding of the correlation and inference paradigm employed by big data analytics as opposed to relying on traditional cause and effect paradigms of cognition. Ultimately the metacognition of society itself may change when the populous begins to understand how the very thoughts of its members shape the datascape and thereby shape the global village we call home.
Only July 15th 2014, a business partnership was announced that astounded many veteran technology users, and conversely, made the barest blip on the radar of the “app” generation; indeed it is likely that the majority of the world’s population did not even noticed this news as they busily went about their day checking emails, sending texts and surfing the web. However, I give to you that this may have been a day that marks a significant path correction for the evolution of society as it becomes increasingly dependent upon, and intertwined with, the progeny of technology, the ‘Big Dataset’. The new partnership was that of Apple and IBM, long rivals with entirely different perspectives on how to promote and employ technology within society. What change this may herald is one of both complexity and simplicity as the technology stanchions of liberal individuality and corporate homogeneity stand at a crossroads of technological determinism and human volition.
Notwithstanding the apparent ‘routine-ness’ of this announcement, a partnership outlining the benefits it will bring the customers of both parties, the potential for a prodigious change for society should this partnership achieve its maximum potential, is one of sweeping global change. Much of what I will discuss centers around the technology of big data and how its two main potential futures could lead to either an increasingly hegemonic state or alternatively facilitate greater control from within the superstructure of society which would in turn lend further weight to the argument of collaborative consumption. The most likely way that an Apple/IBM initiative (or some other large technology company that has the foresight to seize this opportunity) could facilitate a more egalitarian society as it moves into this new digital age, is by delivering education about, and access to, a big data mindset. I posit that the most effective delivery channel would be small business. For this reason it is important to look at both the potential gains as well as identify hidden pitfalls that may lie along this path.
Concepts that will be touched in in this article include: the impact that a ‘data mindset’ small business culture can have on the superstructure of society; the potential for individualistic data terra-forming versus government and big corporate dominance of data landscaping; a shift from cause and effect cognition to one of correlative inference marks another milestone for human control of their environment; and lastly where the diametric tensions of diversity and homogeneity can lead society in the way it metacognates and evolves.
Essentially this article aims to zoom out to a high macroscopic level to find correlations, draw inferences and cast predictions on the interplay of some of the largest concepts humankind has ever had to deal with. A thought experiment modeled on the nature of big data itself. Using the insight and research of some very intelligent women and men we can create broad brush strokes and develop a canvas that may help us, just a little, in deciphering what the heck is happening to us in today’s world of dizzying technological advances and daily information overload.
As our map we will be using the writings of technology theorists such as Nye, Schonberger & Cuiker and Golbeck. As our compass we will be using the knowledge and wisdom of two of the most powerful companies in the world, Apple and IBM.
Countless books and articles have been written on Apple but perhaps the best summary of Apple’s philosophy, an approach that laid the foundation for its phenomenal success, was spoken by Steve Jobs himself at an internal meeting at Cupertino on September 3rd 1997. The meeting was held to mark the launch of the Think Different campaign. During this meeting he re-iterated the need for simplicity, for “doing the basics really well”. At this meeting Jobs said, “this is a very complicated world, it’s a very noisy world…we have to be clear” (Jobs, 1997). The success of this approach shows that Apple is uniquely adept at taking something highly complex and making it easily available to the general populous. Since its inception, Apple has aimed to make computers accessible to the common people. Examples include their 1981 advertisement with a “typical American housewife”, and their 1977 campaign headlining “ready to use” which was a novel concept at the time, (Pop History Dig, retrieved August 2014).
Apple has shown their propensity for creating partnerships since the 1980’s. Partners have included Microsoft, AT&T, Verizon, Intel, Motorola, Hewlett Packard and many more, however, the business world had become skeptical of the long term viability of a partnership with Apple after many of the deals ended up with Apple cannibalising sales of their partnering company (Burrows, 2007). This is another factor that has economists and technologists eyeing this new partnership with reservation.
“The Simple Stick symbolizes a core value at Apple”, notes Ken Segall in Insanely Simple: The Obsession that Drives Apple’s Success, “it’s a reminder of what sets Apple apart from other technology companies and what makes Apple stand out in a complicated world.” (2012). Segall gives a great explanation of the success arising from the simplicity approach, wisdom gained during his time as a creative director working alongside Steve Jobs.
When we delve even deeper into the soul of Apple we see that it sits in something more fundamental than simplicity itself, it rests in the most prized of human possessions, individuality. Jobs eloquently summarises this in a 1995 interview with Robert Cringley (now a documentary called Steve Jobs: The Lost Interview, Jobs philosophises), “What causes people to be poets instead of bankers? When you put that into products people can sense that. And they love it.” (1995). Jobs’ belief that the computer is a medium for human expression is what defines Apple and has developed a worldwide followship amongst the mass society that sometimes borders on the realms of religious zeal.
Where Apple may be seen as the quintessential user-friendly computer experience designed to enable you to express your individuality with hipster street cred, IBM could be placed as its antithesis. IBM’s history section of its website traces its origins back to 1888; of its guiding principals throughout its history it says, “Nearly all of the company’s products were designed and developed to record, process, communicate, store and retrieve information” (retrieved 5th August 2014). Theirs is the history of big data. The over 100 years story of one of the world’s most prestigious companies has had an incalculable influence on modern society. Often nicknamed “Big Blue”, the company’s archives show that five Nobel prizes have been awarded to their employees. IBM has been instrumental in developing major advances in main-frame hardware and high level programming languages, and in funding research laboratories at many of the world’s most respected universities. Their strength comes from their immense infra-structure, conversely they are the very definition of corporate path dependency. IBM is often cited as the current world leader in big data; Wikibon reported an annual income at IBM in big data processing in 2012 of $1.352 billion.
“Figure 3 includes the largest contributor, the ODM’s. These are companies that design and manufacture products specified by other organizations. All the major Internet companies use ODM’s, system integrators (SIs) and other integrators. The three-letter federal departments also utilize these organizations. “ Wikibon (2013)
Whilst IBM is an excellent player in this highly complex, big corporate field it struggles in simplifying its language to small business and the average “person on the street”. Their marketing and communications style is paradigmatic of a corporate vernacular bordering on Orwellian “Newspeak”, as demonstrated in an article by Paul Zikopoulos, Vice President of Information Management Technical Sales and Big Data at IBM, Big data for small and medium-sized business (2013). Whilst this article is very well written, it certainly misses the cachet of simplicity and opiate “must have” quality that are hallmarks of Apple advertising. By its very nature, big data is highly complex and difficult to communicate to the mass public, but then so were computers half a century ago, it is not to say that it cannot be done.
From 2012 to 2014, IBM slipped from 19th place to 23rd place on the Fortune 500 list, whilst Apple climbed from 17th place to 5th place. Ginni Rometty was installed as CEO at IBM in January 2012; a partnership with Apple is seen by many as a way to arrest IBM’s plummeting shares, (Fortune 500 ranking, retrieved August 2014).
If the advertising and marketing of a company can give you insight into their beliefs and opinions, certainly it was clear for decades that Apple held IBM to represent all that it opposed. There have been numerous campaigns in which Apple has sought to mark their difference from IBM. Apple cast this difference as one of individuality and diversity as opposed to homogeneity and repression. The most famous example of this is the 1984 Apple campaign that aired for its second and final time during the Superbowl in January 1984. Whilst the ad does not mention IBM per se, Steve Jobs made it clear in his December 1983 keynote address that IBM was the subject of his upcoming campaign. “It appears IBM wants it all. Apple is perceived to be the only hope to offer IBM a run for its money. Dealers…now fear an IBM controlled and dominated future” (Jobs, 1983).
Apple 1984 Superbowl Ad (image retrieved from Imperial Management Review 5th August 2014)
IBM was represented by George Orwell’s’ Big Brother, the bespectacled image intoning, “My friends, each of you is a single cell in the great body of the State” (Orwell, 1961). This advertisement clearly shows Apple positioning itself as the savior of individuality from a world of corporate drones, hegemonic control and homogeneity.
Despite this intense rivalry the companies did try to partner during Steve Job’s hiatus from Apple in the 1990’s, “The companies had three failed attempts at working together during the ’90s with various software projects; each attempt failing due to the inability of the companies to reconcile differing objectives” (Australian Financial Review 21 July 2014)
Thus the 15th July announcement this year came as something of a surprise and is even now being met with skepticism as we are yet to see any fruits of this partnership.
Apple and IBM (NYSE: IBM) today announced an exclusive partnership that teams the market-leading strengths of each company to transform enterprise mobility through a new class of business apps—bringing IBM’s big data and analytics capabilities to iPhone and iPad. The landmark partnership aims to redefine the way work will get done, address key industry mobility challenges and spark true mobile-led business change—grounded in four core capabilities:
CUPERTINO, California and ARMONK, New York—July 15, 2014
The initial flurry of speculation from technologists and economists centered around whether the deal was better for Apple or for IBM, using examples and forecasts of sales of phones and tablets. Kosner, writing for Forbes magazine, succinctly sums up the partnership “the deal will offer Apple access to IBM’s customers and data analytics capabilities to power enterprise apps. IBM will have something sexy to sell.” (2014).
There was little written about farther reaching societal impacts and in subsequent weeks the news item lost fervor. That I can find little published speculation about the potential for long term implications may indicate that the partnership is struggling to find lift after years of rivalry; or perhaps there is more significant work going on behind the scenes and Apple is waiting to dazzle our socks off. Regardless, the following thought exploration could be useful should someone be successful in bringing big data to the fingertips of the people. The following observations and inferences could just as easily be picked up by another large technology company, which leads one to wonder what Bill Gates is thinking about all this right now.
The array of technologies that could be covered in this section is vast and it is well beyond the scope of this paper to even attempt to be exhaustive. Thus I have focused on the two aspects that I feel have the most import under this thesis.
Humans have been generating data since the dawn of civilization. From the Library of Alexandria, to the Doomsday book (1086 A.D.), the Encyclopaedia Britannica, and the New York Stock Exchange, humans have been constantly developing new and improved ways to store and process data, to extract deeper meaning of the world around them. Big data is an old concept, it’s just that now it is so enormously behemoth we can barely discern its shape anymore and instead most of us merely sense its presence by the shadows it casts. Today’s computing power has enabled us to move far beyond the two dimensions of rows and columns to analysing data in an infinitely more complex manner; this in turn creates more data by developing inferences and predictions by perpetuating a positive feedback loop.
Big data is used in a wide array of fields including: health science, meteorology, finance and security, to name just a few. The applications have had many positive and far-reaching impacts on society. Specific examples are numerous and not the subject of this paper. I have found the treatment of this by Mayer-Schonberger and Cuiker in their 2013 book Big data to be an excellent source of information on how big data works and how exactly it is being used today. Some examples they explore include: Google’s’ tracking of flu outbreaks; the ability of the chain store Target to not only assess which shoppers were pregnant but also predict their due date; the ability to predict premature infant births long before traditional symptoms present; and the ability to predict which residences in New York city were most at risk of fire hazard. This book clearly outlines the supervening social necessity that the technology of big data satiates; the ways in which big data can be employed to solve seemingly unsolvable human problems are endless.
To try to conceive of the computing power we now have, I have chosen this example: in the final decade of the 20th century it took thirteen years to unravel the entire human genome, in 2012 big data was capable of performing this task in two days.
An interesting using of big data to gain insight into the social landscape of the world was published in Nature by Preis et al on 5th April 2012. Using the Google Future Trends Index, they found a direct correlation between a country’s G.D.P. and its citizens’ predisposition to look forward. On the use of big data for social science research they state, “Analysis of such ‘big data’ opens up new opportunities for a more precise and extensive quantification of real world social phenomena that was difficult to attain using complicated and expensive surveys and laboratory experiments alone.” (Preis et.al., 2012)
The most ubiquitous use of big data is in targeted advertising guided by the actions of the ‘targets’ themselves on the Internet, most notably on social media platforms. By monitoring consumers’ browsing choices and other manifestations of their online presence, companies have been able to very precisely market their products to their desired audience. This aspect will be discussed in depth in the Social Context section of this paper when the concept of “exhaust data” is explored.
Doug Laney is VP Research, Business Analytics and Performance Management at Gartner Inc. and has been credited with developing the 3’vs of big data in February 2001; these are Volume, Velocity and Variety, referring to three aspects or dimensions of big data. Since then other V’s have been proposed such as Variability and Value (Wigmore, 2013). IBM specialist Zikopoulos, in the article previously discussed, proposed the 4th V to be Veracity (2013). This banter of terminology demonstrates the early stages we are at when discussing the big data phenomenon of today and hence the timely nature of this paper.
Technical jargon aside, a key point to note with big data is that its primary structure is one of correlations. When vast amounts of data (Volume) are fed into supercomputers, even in their raw and “messy” state (Variety), algorithms are applied to look for trends, propensities and relationships. When correlations are found, inferences are made which often lead to predictions. Thus we see a shift from a traditional cause-effect paradigm to one of correlation-inference-prediction. A caveat is placed on this by Schonberger & Cuiker when they state, “Correlation never actually proves causation. This is because it is always possible for the connection between the variables to be entirely coincidental” (2013).
The importance of the algorithm applied to the data cannot be understated. It is the algorithms that direct how and where correlations will be found. Algorithms are the tools of architecture of the dataset. So who then are the Architects? The short answer is either computers or people. Either answer has significant connotations for the future of our society as will be discussed in the Social Context section of this paper.
What is clear is that to grasp this level of complexity certain skills are required. One is best served by developing a “data mindset”, enhanced by a high analytics IQ. Naturally there is a bevy of articles and advice bouncing around the Internet right now on ‘what this is’ and ‘how to get it’. One oft cited article is that by Zettelmeyer of the Kellog School of management, in this he defines the big data mindset as “essentially, the pursuit of a deeper understanding of customer behavior through data analytics” (2013).
- DESIGN MARKETING PROCESSES WITH DATA IN MIND.
- ENGAGE IN RESEARCH AND DEVELOPMENT EVERYWHERE.
- USE PREDICTIVE ANALYTICS.
- CHALLENGE CONVENTIONAL WISDOM.
Zettlemeyers four elements of a Big data Mindset, Kellogg School Management (2013)
Whatever the exact parameters of a big data mindset are, there is no doubt that those who possess it, or can cultivate it, will be well placed to achieve financial success in the coming years.
Open source software such as Google’s 2004 Map Reduce, Apache Hadoop have facilitated many start ups in big data enabling easier access to the datascape to companies with a range of budgets.
Another key technology to consider in the Apple, IBM partnership is Cloud computing, a vital component of mobile computing in this post PC era – essentially moving primary services from hardware and in-the-box to a broader infrastructure of shared services. These could include software-as-a-service (Saas), storage facilities, applications and other utilities. Computing in this way enables small companies to have access to larger computing power; “Building data center infrastructure is extremely expensive, to the point that it is cost prohibitive for smaller companies…large companies may not need all of that capacity and computing power at all times…as a result, they began to rent out this capacity to smaller companies”, (Phillips & Niu, 2014). The same article goes on to state, “the global cloud computing market is expected to grow to over US$120 billion by 2015”.
This represents another watershed moment in the way society develops along with its technology. That it comes at a time when we are launching into the big data digital landscape is serendipitous as it enables access to large computing power to a wide socio-economic sweep of society, thus giving the superstructure another weapon against a hegemonic state. Mobile computing also enables rapid adaptability and can help businesses divert from path dependency.
Understanding how these two metatrends are shaping society and how a partnership between IBM and Apple can impact this digital terra-forming is the primary basis for this paper. “Established firms are usually too committed to a particular conception of what their product is.” explains Nye (2007), who then continues to demonstrate this point by showcasing IBM’s slow move to the personal computer as an example of path dependency.
Both of these technologies are worthy of extensive discussion on their impacts on society, but it is the combination of the two that brings vast computing power, available to low budget startups and the prosumer of the collaborative consumption era that hold the greatest potential for large societal impacts. What is really lacking right now is a simple and effective delivery of (high quality) versions of these technologies to the general public along with simple design, easy to understand mechanics and…a sleek little package in a range of colour choices to reflect your individuality.
As previously stated, I believe that the current climate provides a prime opportunity for such a business model; it is really just a race now amongst computer hardware and service suppliers to see who will stake their claim first and draw in the early up-takers of the general populous.
The feedback loop caused by our interactions with the digital landscape, the subsequent mapping of our volitions used to build structures in the virtual world, and from the effect on us by the new realities of our self created building blocks are fundamental to understanding the individual’s and society’s interplay with the Dataset. We leave cognitive artifacts in the Dataset every moment we interact; the echoes of our consciousness rippling into the datascape, creating repeating loops and blooming patterns like the arms of a Mandelbrot set. This idea is a macroscopic echo of morphisms and strange-loops as explored by Hofstadter in Godel, Escher and Bach back in 1979. Understanding the basic mechanics of the global dataset feedback loop is absolutely imperative if the superstructure of society is to maintain any semblance of control of where the future is heading.
Exhaust data is “the trail of digital information you are leaving behind every time you go online”, warns Deloitte principal Reagan (2012). Understanding this concept of exhaust data is paramount to understanding how each one of us shapes the big dataset. The real value to big data companies is not in our direct interaction with the digital world such as creating a website or crafting a Linkedin account, rather, it is the shadow impressions we leave behind every moment we are in contact with the digital world. Just a very few examples would be when you: “like” a cat video on Facebook, share a funny picture on Instagram, browse Amazon for a bicycle lock, Google search the Kardashians, check the weather, map a route, watch something on Netflix, use an ATM, use a credit card, make a call, send a text, pass a toll booth, when other people tag you on social media, and every single networked app you have ever used.
What is more unsettling to consider is that this digital exhaust is not created just when you are typing at a keyboard, it happens any time you have any electronic interaction. Every human with a smart phone is a node in this developing nexus. With many personal health apps you are even sharing how many times your heart beats every minute to the global dataset. Did that make your ticker take an extra beat?
Wired Magazine initiated a movement in 2007 called the “Quantified Self”. It is a way to embrace the collection of personal information and takes image crafting to a whole new level. Founders, Wolf and Kelly, advocate to “share an interest in self knowledge through tracking and recording every nuance of their lives, from the number of bites they take while eating to the type of brain wave patterns that occur while sleeping, and everything else in between.” (Annalect, 2014). Nomenclature associated with this movement is self-hacking, life-logging and auto-analytics; it demonstrates a unique digital narcissism.
The technology behind this is fairly complex but essentially it can be viewed as someone collecting all of your digital vapour trails, electronic footprints and cyber traces, then combining and collating all of this and selling it to advertisers. Standard laws are eons behind legislating against this, not only because the vast majority of people don’t really understand this, but because most of them are unaware that it even exists.
The value of exhaust data is enormous. The access to it via cloud based big data software is essentially fairly easy for a startup with a bit of brainpower behind them; and with few governing laws in place your digital exhaust is fair game. Questions to ask are: will it be traded on the stock exchange, how many people are making money off your digital exhaust right now and how is your personal information being used by strangers?
Just as an ancient hunter gave thought to how his scent might be left on the shrubs he passes and how his footprints may lead a predator to him; in this age of the Dataset Wild West, we need to become more cognisant of how our digital expression of our volition creates an ethereal substance that not only speaks volumes of who we are, but by its very essence shapes the cyber world around us.
Essentially if you live in society you are “on the grid”, thus you are part of the big dataset. If you don’t like it your only real option is to go and live in a cave, giving up everything that the modern world affords you. However, it is not all doom and gloom and I am in no way advocating a Luddite reaction; by embracing this change and really using cloud computing nodes, subgroups of society can gather into virtual groups and effect power in real time more effectively than via a political rally. Should everyone in your group simultaneously decide to exercise their expressions of volition in a particular way you would be creating a breeze in the system, a sea change, the flutter of a butterfly’s wings rippling through the chaos of the datascape.
Nye discusses this when he speaks of the early Internet, “through the Internet every citizen does have the potential to communicate with a large number of other voters at almost no cost” (2007). Taking this a step further consider an educated public understanding how they impact and interplay with the Dataset. The implication for the rise of digital “political groups” to affect the datascape by unification of numbers and harmonizing of intended exhaust data is significant.
As a user’s volition percusses the datascape, his or her digital exhaust is collected, collated, and then re-packaged into new advertising and marketing campaigns. In real world time this can affect the economic success or failure of companies, what products are being produced and, thus, manufacturing/production schedules and lines, employment numbers, and a whole outreach of cause and effects in the physical world. Human will, on a very grand scale, is what is terra-forming the digital landscape and in turn the digital landscape shapes and forms us. Technological determinism becomes an archaic term as you recognize the symbiosis of the situation.
Developing a self-awareness of how your digital interactions form your exhaust data may ultimately alter your volition patterns. It’s a bit like opening the box to see if the cat is alive or dead, once you develop a data mindset, your actions will be permanently altered as you become aware that your actions are the box and without the box the cat is just an idea, disconnected and without contextual correlation. It is by the metacognitive power of one’s self that true control can be wrested from the state.
Following logically from an understanding of big data and digital exhaust one can now see the crucial need for developing big data mindsets in the general public. Here is where I see the Apple / IBM partnership as holding the potential for being a key player in determining where this next stage of society will go should they seize upon this opportunity. IBM, one of the strongest forces in big data, has thus far been unsuccessful in communicating the importance of the dataset to the mass public; they are simply not renowned for their ability to speak clearly to the individual. Conversely, Apple is the master of delivering new technology into the hands of the people, the computer illiterate and children. If Apple can facilitate a true understanding of big data to the common people (the mode of production of big data) then a safer future would seem to ensue. Of every corporation on Earth right now, Apple has the best chance of getting this message across.
Simply posting articles, blogs and newsfeeds on what big data is, and how it impacts individuals and society has been met with limited success. To truly enable a big data mindset in the general public it needs to be brought right to them, in a hipster chic package with an easy to use and attractive application.
Dr. Jennifer Golbeck, from the University of Maryland, a specialist in human-computer interaction, explores other educational options in her illuminating TED talk The Curly Fry Conundrum (2013). In this talk she abandons the idea that we will be able to pass effective exhaust data laws in a timely manner and posits a solution that involves the education of the public on how to more responsibly interact with social media. As a scientist who works in the field of using exhaust data to make inferences and predictions of people, she proposes the solution is to “develop mechanisms that can say to a user, ‘Here’s the risk of that action you just took’.” (2013). Golbecks proposal places the control and responsibility in the hands of the educated user.
The uptake on this new paradigm by small business and the mobile workforce will be a decisive factor. Whilst it is important to give access to big data to individuals, on a large scale they have shown a propensity to trivial games about mal-content birds and crushing candy; they use the marvel of the Internet to post selfies on dating websites; and they share a seemingly infinite stream of pictures of cats (usually alive and sometimes in a box). If the superstructure of society is to have any real power in the big dataset landscape then they must also practice and exercise some formalized patterns. Small business is the most suited facet of society to bridge this gap as it is grass roots enough to access a wide cross section of the general populous and also relies on some formalized systems for fiscal success.
Current texts and marketing of big data to small business is like watching two people with no idea of each other’s language trying to build a rocket ship together. Here is an opportunity for Apple to bridge this gap and not just act as a translator but also a facilitator of a two-way flow of communication and intent.
There are numerous articles written about the phenomenon of business-driven social change. An excellent report recently released by the Canadian Network for Business Sustainability explores this topic in depth using hard statistical research. In this report the first key finding is listed as, “Business-driven change is an emerging and fragmented field” (2013). Whilst this report does not mention big data, it does list the three key components of social change to be: motivation, capability and opportunity. These exact same principles can be applied in bringing a big data mindset to society through the members of the small business community. If an Apple / IBM partnership were to harness these principles when developing a method of delivery of big data capabilities to the mass public the benefits would be symbiotic.
By educating small business on how to effectively use big data capabilities to enhance their opportunities you are developing amongst the members of that business a big data mindset. In this focused environment their new skills can develop quickly and be transferred to their personal lives, which are otherwise constantly bombarded by information (good, bad and useless). The opportunity for education of the public by an Apple/IBM partnership is enormous and cannot be understated. Assisting people into the dataset age will garner deep public support. By projecting an image of benevolent interpreter and facilitator and providing the means with which people can begin to understand the dataset, how they can data farm it, and most importantly how their actions are terra-forming it, will establish a strong level of trust from the consumer. Additionally this new way of thinking about the dataset could lead to a lasting cognitive shift.
As mentioned under the technical description of big data, the algorithms that guide how correlations are made and thus what inferences will be drawn and which predictions will be cast, are the framework upon which dataset is built. The people who design these algorithms are experts in statistics, computers and mathematics with highly developed big data mindsets. Schonberger & Cuiker dubbed these people “algorithmists” (2013); they posit that this group of people will be under an obligation to act in an ethically professional manner. I am afraid I do not share their faith in humanity, just as in the same way I cannot express a high degree of trust for the owners of the datasets, the “Data Barons”. I do not feel that the rate of human ethical practice has evolved at the same pace as technology. This paradigm of Data Barons and Algorithmists can be seen as an interesting parallel to Ancient Egypt with the Pharaohs and the High Priests and their dominance over literacy; just another repeating pattern in human societal structure. Certainly it is easy to see the propensity toward a fascist state when knowledge is contained to an elite level.
Which leads us to another vitally important question; who owns the Dataset? Who are the Data Barons? There appears to be no clear answer on this. Social media platforms have intricate legal pages with multiple links that usually essentially say that the user retains ownership of their primary data, but one has to look deeper to find how your information may be used.
We may provide advertisers with information when we have removed your name and other personally identifying information from it, or combined it with other information so that it no longer personally identifies you
Data Use Policy – Advertising and Facebook Content
Updated Nov 15th 2013
There are few, if any, rules governing the use of exhaust data. Our ownership laws are inadequate and archaic when we start to view the world in this way. Those that place themselves at the cross currents of the movements of data, collecting the plumes and contrails left by users, will be the most well positioned to acquire real world wealth.
As Nye explores Marxist theory in Technology Matters (2007), we can see many parallels with the current structure of society and big data. The workers have become the nodes and contributors of the dataset and the machinery is big data itself. Marx predicted the fall of capitalism due to “mechanization producing greater surpluses [than demand]”. In part he may have been right. As more people understand the dataset, another level of equality could arise, potentially resulting in a new economic paradigm of collaborative consumerism. However, in this digital revolution we will have data barons and prosumers battling it out in cyberspace.
Returning to our problem of the creation of the algorithms by a subset of the population creating an elite class in this digital war, we see that the alternative would appear to be to let the machines create the algorithms themselves. However, not only would these algorithms be opaque to the majority of humans it does raise the question of artificial intelligence. The nature of artificial intelligence is beyond the scope of this paper and has received significant treatment since the inception of computers, however, key themes that arise when discussing A.I. are the ability to receive information from one’s environment, process and analyse it, draw inferences and posit conclusions and thereby come to understand the environment, and (most importantly) learn from these processes. Certainly leaving the creation of big data algorithms to immensely powerful supercomputers with access to the entire world’s data, would be a strong step in that direction!
Since Sophocles crafted Antigone, expounding his thoughts on the importance of the individual to society in the face of the seemingly omnipotent power of the State, this theme has been played out in a myriad of permutations with a plethora of backdrops. It appears an essential theme to humankind to retain a degree of individualistic power as some type of assurance (whether this be real or imagined) against a dictatorship of power.
In the Apple / IBM partnership we see the dichotomy of cultural conformity and individual diversity. Big data or IBM, can be see as conformity and homogeneity resulting in a technology that is unrivalled in its power to help solve some of the world’s biggest problems; conjoined with the liberal expression of individuality, represented by Apple, which allows us tremendous outlets for diversity. If these two leading companies can construct a balance of diametric tensions they could become one of the most significantly powerful influences on society in history.
Consider the enormity of change coming for society in the form of big data and the two main paths this change may take a) it remains the domain of government, big corporation and thus perpetuates a hegemonic society or b) it is delivered into the hands of the public, educating them to develop big data mindsets, thereby ensuring a large measure of control remains across the Marxist superstructure. If Apple can sell big data and the power of IBM to small business and the individual, that will bring big data out of a hegemonic state and into people’s hands via their iPads, iPhones and other post pc devices.
Assuming for now that the partnership will be successful, how may this impact our very thought processes? For millennia human thought has been based in a cause and effect paradigm, this permeates and underlies everything that we do. The big data mindset is a complete paradigm shift; it is based in correlations, inferences and predictions. What this new mindset could mean for humans could be a next seminal step in evolution.
Major human milestones (in the authors view)
- 125,000 years ago fire, controlling the environment
- 50,000 years ago mouth, tongue and larynx changed enough to enable speech
- 12,000 years ago, changing the landscape with agriculture
- 8,000 years ago use symbols to represent words and concepts, written comm.
- 1760 birth of the industrial revolution, heralding changes in the way we communicate, control our landscape, and the way we think.
- 1970’s Digital revolution and information age. The Internet.
- 21st Century Information revolution Big data, Cloud computing
- NEXT?? The nature of big data moves from a thinking process of cause and effect to one of correlation. This is a paradigm shift of thinking. The scope of this cognitive change depends on implementation of big data mindset to mass society. Collaborative consumption or invisible hegemony?
All of these milestones are about us controlling or changing our environment in some way. Each time we have enhanced our ability to change our environment our thought processes themselves have undergone a change. This is the result of feedback loops on a master scale. Before now, we have stumbled from one milestone to another, feeling our way in the dark as we try to understand the impact of the new self-imposed change to our environment. We are at such a stage right now, the Hillary step of our societal Mt Everest. With the help of the many people referenced in this paper and the many more who are cogitating this problem, perhaps we can be, just ever so slightly, more graceful as we move into this new age.
This has been an over-view of several large concepts and technologies with some rather sci-fi sounding postulations and conjectures. However, the science is real and the technology not only exists but is being interacted with every day by virtually all 7 billion of us. The implications are enormous both on an individual level and as we continue to grow as a global society. To draw together all of the threads I have explored in this paper I will attempt to paint a master canvas using the “Simplicity stick”.
- Each person is a node in the big dataset, forming part of the digital landscape.
- Their exhaust data is more valuable than their actual intended data. It is the by-product of their volition that creates the exhaust data.
- Rather than acting as just sensors at the peripheries of a nexus it is their very actions and expressions of their volition that power and shape the dataset. Beyond developing agricultural techniques for the physical landscape around us we are now data farming and terra-forming the digital landscape.
- The more that big data mindsets can be created in the mass populous the more self-aware each node becomes and therefore the expressions of their volitions changes at a core level as they better understand the consequences of their input into the system.
- Development of a big data mindset may lead to a change in metacognition from cause and effect to correlative inference.
- Education to aid in developing big data mindsets will be the most effective way to maintain some control of the dataset by the superstructure and avoid a hegemonic state of data barons and algorithmist architects.
- A partnership between Apple and IBM is potentially an excellent way to facilitate the required education to the general populous by combining their strengths of big data, cloud and large computing power with those of simplicity, marketing and the cachet of expression of individuality through diversity. Should Apple / IBM pass up this opportunity, it is likely another computer company may seize upon it.
- Due to the complex nature of the cognitive shift required, an initial development channel through small business would likely be the most successful approach.
The pitfalls are numerous as they always are with any large changes to society. The pitfalls themselves should be the topic of another paper(s). I have listed here the three primary pitfalls that arise from the above discussion. There are many more.
- Perpetuating a hegemonic state by leaving power of the dataset with the elite classes of data barons and algorithmists (the architects of our own matrix) as currently people with big data skills and mindsets are not mainstream. Regulating these industries is ultimately futile as the legal system works at a snail’s pace compared to advances in big data. This can easily lead to market control by a select few resulting in a monopoly and reducing further innovation.
- Moving to a correlative, inference cognitive state implies a risk of removing context. “Big Judgement” by humans should remain in place to protect ourselves from our own creation – although this is disputed by McAfee of the Harvard Business Review.
- Correlation and inference is future based. There is a concern that too much focus on the future can lead us to forget to learn from the mistakes of the past.
Just as life was once single cells in a pond of sludge and the coalescence of these cells into organisms such as stromatolites enabled mats of connected data, which in turn eventually led to higher cognitive processing of data and eons later to humans, we may be witnessing the birth of a new shift. As nodes in a nexus of big data landscape are we poised to herald the emergence of a new type of consciousness? As Orwell foretold, “My friends, each of you is a single cell in the great body of the State” – does not the uniformity of huge datasets only re-iterate Newspeak into the new language of “Dataspeak”? Perhaps we have evolved to a level of metacognition where we can begin to recognize the complexity of a dichotomous state of homogeneity and individuality as being the next step toward us understanding large scale structures such as the cognitive artifacts we create as a society. Certainly the transparency afforded by a stripping of privacy would lead each individual to consider his or her actions more carefully and, ideally, act more truthfully and responsibly.
Predicting the future is a tricky business, our global societal system is immensely complex. What is important is to explore the ideas of where things can go so that when the future does unfold we can say “oh yes I saw something like this in one of my thought experiments, I think we should try reacting this way.” There will always be a butterfly flapping its wings somewhere, messing up our carefully blueprinted futures. It could be something Google throws at us, it could be a new social media from the next 25-year-old mega-billionaire, or it could be as simple as a sunquake EMP pulse reminding us that, ultimately, we are still at the mercy of our environment.
What I do see is that we are at a crossroads. Never before has it been so important to bring such big ideas to the common people. Placing the power of big data in the hands of the people and educating small business how to effectively use and understand this power will be crucial to avoiding a hegemonic regime of algorithmists and data barons.
It is easy to dismiss all of this as something that is beyond your control. Or something that will never happen as it just sounds too far fetched and the product of a Timothy Leary vision quest. It is the most easy to believe in technological determinism, to blame the technology bogey man, to believe that this is happening to us not because of us. By now we should be more mature in our reasoning and take the responsibility of our actions and our lives within ourselves. This is not the time to wait for some benevolent super hero government to come in and save the day. It is time to take the initiative to educate ourselves, and those around us with what is happening. I am not talking about fear-mongering, crying doomsday or watching for a falling sky. I am advocating considered and thoughtful use of the amazing tool that you have called the Internet and the even more useful tool, your own brain, to monitor your own actions with the digital landscape.
Before you download the next app, share a link or ‘like’ curly fries, simply be aware that as you extend your musculature to press that button you are instigating an act of volition that will leave a digital contrail; and this vapour trail will become forever part of the BIG DATASET. The dataset that you interact with every single day, the dataset that shapes the world around you.
As the hunter is aware of his movements through a forest, be aware of your movements as you terra-form your way through the digital landscape.
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