I am Data, therefore I am Dead

Facebook, Twitter, Instagram and other social media platforms enables us to create a digital profile and share it with other  users. The profile can be filled with photos, videos, biological, geographical, employment and educational histories, preferences, likes and dislikes.   Alongside one’s digital profile, these platforms allow us to connect, share information, chat as well as respond to other registered users.  We can find this clearly articulated in the mission statement of facebook, for instance which says that it seeks to people the power to share and make the world more open and connected. By letting the user become a co-developer of the content, facebook, twitter, instagram etc., joined online experience that is called web 2.0. It is drastically different from web.1.0 which was chiefly a single-serve website. Web.2.0 are basically number of websites that connect to each other and provide possibilities of communication between them.  While there are great benefits of this experience, what is done with data of the footprints left by the users of these social media platforms is an important issue that requires critical attention. Just like Google has the data of the query log of its users, facebook being a social network site has log of preferences, views, shopping, travelling, entertainment choices , health , education pursuits, sentiments  and potential readiness/ dispositions. This is why facebook and other social network sites require big data techniques for storage and analysis. Big data analysis is said to bring velocity, veracity and value to its users. While it is highly efficient and productive and provides several benefits to its users like the facebook, the question is about reduction of human person to an objectification of data and use of it for commercial, sports and political gains raises concerns that of importance for in this study.

We shall begin with an exposition of what has been called as data mining and travel the road of its evolutions. Next we broadly consider the word of data mining and finally close with a philosophical reflection on human condition in the digital world.

The Growing World of Data Mining

We are living in an accelerated society. We can trace rapid changes in the field of data analytics.  If one takes 2007 as the bench mark for Analytics 1.0, we have already seen analytics 2.0, 3.0, and 4.0. We can already find extensive use of data both for statistical and quantitative analysis which offers possibilities of fact based management with the help of predictive and explanatory models that powers decisions and action in fields of business, sports and politics.  The use of data analysis is radically transforming the performance and the reach of several institutions across the globe.

Analytics 1.0

Analytics 1.0 was heavy on descriptive data analytics.  Data companies at this level were busy tracing what happened in the past and in the light of it attempted to predict the future or to make recommendations (Prescriptive) to do the job better. This means predictive and prescriptive analytics grew on the descriptive one.  Descriptive analytics still has its use today.  There are self service analytics that assist in descriptive analytics today.  The 1.0 era brought in the challenge of storing large chunks of data.  Our data storing needs have increased and new data warehouses were constructed.  At this stage data analytics moved at a slow pace and mostly depended on the orders of the managers of the firm. This led to the thinking that data analytics was decision support.  Managers played data driven executives only for public perceptions and mostly depended on intuition and gut feelings to make their decision.    Despite this the companies involved with data analytics competed with each other and worked to make the best of a difficult situation.

 Analytics 2.0

Google, eBay, PayPal, LinkedIn, and Yahoo paved the way forward beyond analytics 2.0.  They were dealing with what we call today Big Data. Data was voluminous, fast-moving and fast-changing.  In order to store, analyse and act on this data, these firms required new technologies  to handle it.  Hadoop constructed Doung Cutting and Mike Cafarella, an open source program for storing solved the need of storage although it could do minimal processing of data.  Hence,  Pig, Hive, Python, Spark, R and varieties  of other tools emerged on the scene that could both store and do analysis.   These developments led to the coming of experts who were christened as data scientist. They soon bridges to the CEO or senior executives  helping to guide the ships of the big corporations.  This level brought the fruits to customers. LinkedIn for instance, data products like, people you may know, jobs you may be interested In, Groups you Might Like. One might be more familiar with the data products of facebook, like people you may know, trending topics, news feeds, time line, and search.

Analytics 3.0

Analytics 3.0 is in many ways the combination of analytics 1.0 and 2.0.  It deals with both big data as well as small data.  This means companies may be interested in the location of the customers which involves big data analysis  but same company may be also  interested in combining it what the customers have brought from them in the recent past.  This means it is not really big data or small data that  is important at analytics 3.0. It only means it embraces all data.  Analytics at this level penetrates production processes and becomes what is names as operational analytics.  Operational analytics  makes it possible for the marketing analytics to inform about new marketing campaign and integrates it into real time offers on the web as well as it allows supply chain optimization incorporated within it.  Thus, the customers know that right offers on the products and the management has the right products in their warehouse.  Companies at 3.0 shape their decisions, products and services through data analytics.  Thus, data at 3.0 has become mainstream business resource.

Analytics 4.0

The first three levels of analytics required human analyst or data scientist. The level 4.0 is the domain of artificial intelligence or cognitive technologies.   Machine learning, neural networks and deep learning rule the roost at this level.  In machine learning creates models of data and creates models and determines whether they fit the data or not and create more models. Neural networks are a version of statistical machine learning and sophisticated versions of the same are called deep learning. It offers multiple layers of features or variables to predict or make a decision about something and can easily compute with large amount of data. Both open souce  and proprietary software are rapidly growing  to assist machine cognition. Google, Microsoft, facebook and Yahoo have made available open source machine learning libraries while DataRobot and Loop AI Labs have proprietary  versions for machine learning. There are stand alone software as well as those that are embedded   or augmented with other links. Machine learning is yet to replace human analyst. They have become important productivity aids for them.  These and other developments have brought out what is called the internet of things.

The Process and Results of Data Mining

Data mining unearths patterns that are interesting and useful from a data-set/ data base and converts it into understandable form. Data analytics presents it’s as an operational necessity in a big data world and proclaims that it creates value for all. The businesses as well as their prospective

Process of Data Analytics

Data analysis brings structure, order and meaning to mass of collected data.  It is a process of sorting out of data.    The big data is described by three Vs: Volume, Velocity, and Variety.  We are talking about web data that may relate to customer behaviour on the net.  Next is text data that involves emails, facebook feeds, news etc. Time and location data provided by the internet obtained through the mobile phone, GPS and Wi-Fi networks. Smart Grind and Sensor Data provides information on the performance of the engine and machines.  There is also social network data that is collected from the footprints of the individual or groups in a social network sites.  It processes data through the following mechanisms: data selection, data interpretation, data cleansing, transforming of the raw data into some information, integrating and evaluating the pattern for the information received. Selection consists in the combination of data that comes from various resources into a single data called target data. The pre-processed data from the target data is then transformed into processed data. This step is called cleansing of data.  The next step is called data transformation.  It puts the information into a shape that is required for data mining operations.  The next step is a vital stage. It seeks to dig out useful patterns from the data that was transformed by using data mining algorithms. Finally, the information undergoes what is called pattern evaluation and it is converted into knowledge.  This means dark data which is meaningless is converted into use worthy data and is interpreted accordingly.  This Knowledge then can be further analysed and put to use for commercial or political purposes.

The products/services of Data Analysis

Data analysis provides several services.  These have been described as descriptive, predictive, prescriptive, and autonomous analytics Data once analysed and interpreted offer insight that is not just descriptive but is both predictive and prescriptive.  Descriptive analytics only looks into what happened in the past.   It may manifest a business house which of its products is making good business, where it is most sold and who buys it. Predictive analytics attempts to project the future. It tells us what might happen. From the data available, it extrapolates the future trend or patterns. Prescriptive analytics aims at providing solutions tries to answer ‘how do we deal with this?’  The sheer speed at which this analytics works as well as customised response abilities that it offers are indeed game changing.  This is why it provides high value for business and politics.  It accelerates decision making as well as use of automation on a large scale.  It can lead to automated decision making as well as autonomous analytics.  Autonomous analytics uses machine learning. It involves a big world of possibilities. Thus, for instance, we may have driverless cars with intelligent systems that are continuously learning from the data available and are able to make optimal decisions in advancing ways that are revolutionary.  Artificial intelligence working in synergy with big data is radically transforming the way we live our life.  Autonomous analytics takes forward prescriptive analytics to new levels that we may call optimizing analytics.  Such an analytics exhibits agent’s behaviour, adaptive behaviour and social behaviour among machine challenging what we so far considered as the unique province of humanity.

 

Consequences of Data Analytics

The consumers are dying and Prosumers    are born everywhere. Uber has drivers and passengers, flip-kart, Amazon,  swiggy and zomato have buyers and sellers on the same platforms.  Web is fast uniting everyone. But these platforms seem to becoming at best echo-chambers which put together islands of common interests. These groups may flourish in isolation oblivious of other experiences of human life. this is why the world enabled and produced through data analytics might be impoverishing our human experience on the whole while amplifying  some part of it.  Besides, it is posing dangers to human privacy, autonomy and identity. The real ‘I’ in us is dying and I is becoming my Data.  Machine learning will find patterns in my data and the self that has become data is in danger of being enslaved. The machine learning does not just study the patterns of data from the self that has become data but will also predict future behaviour responses and thus provide ways of manufacturing future behaviours of people not necessarily involving their intellect and free will. Maybe we have already reached there.  This brave new world promises heaven for the business and political elite by providing great power in their hands while providing several benefits to the public at large might pose grave dangers to the future of humanity. This why we may have to ask are we marching towards a collective hell?

Philosophical Reflection

The digital migration of humanity into several social network platforms as well as the growing use of data analysis is raising several philosophical questions. It is continuously challenging humanity to belong to the future by joining the digital world. Every person is promised a state of twice born/ dweja. There are several benefits to the born again in the social media but there also several vulnerabilities.

The Promises of the Bridgital World

Dweja or the born again of the big data world are not merely individuals but big corporations. While the digital world has become a brigital for both companies as well as individuals and has its great benefits, it has accelerated oppression of humans and exploitation of the environment.  No one can deny that it has redefined our relationship with the physical and the human world. This is why the bridigital world of Big data is also called phygital world.  Some thinkers seem to think that the digital which has become brigital in its true sense became it has built the bridge to the creator God.  Jaspreet Bindra for instance thinks that the digital revolution has opened us to the world of Hindu God of creation, Brahama. The new digital world is spawn with possibilities for creative ventures and indeed the world of brahma of Hindu mythology. These godlike possibilities open new modes of being in the world for humanity.   Once one migrates into the digital world one enjoys his or her digital after life. We open new bridges of possibilities but we die to our privacy, ownership and have no control over the data that we constantly create or consume on the internet. All digital life is indeed bridgital.  It is bridges us to an afterlife.  We are creating as new digital legacies which seem to say ‘I am data, therefore I Exist’.  But who will own our digital legacies is a real question that does not really have clear answers.  But it is crucial and vital as our digital assets are only going to increase in the days to come.

The Displacement of the Physical

The digital is fast replacing the physical from our life.  But the significance of this change is strangely going unnoticed. We are now creating and sharing a digital experience for us as well as passing it on to the future generations.   It has changes both our expression and experience of our world. Our world is hybridized into a phygital. We create, consume and share digital representations of the physical. We are living into a digital maya.  We have truly entered Plato’s world of copies today. We are fast replacing the physical objects with their digital representations.  This has removed us from reality and we have begun living a hyper-real life powered by digital technology.  This have become digital, therefore they began to exist for us. Our relation with the physical world is weakened and we are trapped into the stupendous world of digital maya.  It traps us into our echo-chambers and we continuously create digital data that reveal our preferences and aesthetical orientations.  But this can be our weakness as it renders us vulnerable to exploitation by those who have access to that data.  This is why we have the challenge not only live our life on earth with consideration to ethics and morality but we have an ethical imperative to live well our digital life.  The seismic shifts in the way we live our life demands ethics for our digital life.   Our growing dependence on digital institutions has rendered us vulnerable and humanity might tie itself with new chains of (un)freedom and oppression, if such an ethics does not come to our rescue.

Facing our Digital Death

Our digital life dies and lives at the same time. It is a hybrid life. The fact that digital life gets aggregated as mere data, it may be regarded as dead while it opens us possibilities of continuously digitizing our experience of life it remains alive.  Therefore, we may to reconsider what we have said when we say ,’I am data, therefore I am’. In fact we way have to consider our being in-between death and life  and may have to say; I am a data, therefore I am a Living dead. As a living dead, I do not fully own my past. It has multiple belonging in the social networking sites. It fundamentally becomes a data that becomes of value to the big data analytics. I am a data that is both dead and alive.  As data, my digital legacy may remain dark matter until data analytics subjects it for analytics to mine its use value.   But paradoxically, my data is not useful for me. It may be used even to harm me or manipulate me. This is why all of us need a careful and critical consideration of our individual and collective digital legacies. We may need new laws to protect these legacies.  Our digital legacies are unfortunately spread all over the place in the web. But they can be retrieved by data analytics and can be mined to distil our preferences, aesthetical tilts, worldviews etc., and may be put to use to undermine our own interest and wellbeing. This is why the data that we live behind is also a living dead. It is dead because it is a gold mine of information but is dark matter for all. But once data analytics brings light into it the dead data become living with great consequences for its producers.

 Conclusion

Our study strove to enter the digital world along with data analytics and tried to unravel the potentials of its promises to humanity. The digital world has created an afterlife for our data. It has life that is beyond the control of its producers as well as consumers and can be put to use for political or business purposes.  This is why it may be more correct to say: I am data, therefore I am dead.  Here we do not speak of our physical death. It is a death that I do not physically die. It a death that I die in the data I create or consume in the internet.

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- Fr Victor Ferrao