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The Datafication of Business and Society

The Datafication of Business and Society

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I first encountered the term datafication in The Rise of Big Data: How It’s Changing the Way We Think About the World, by Kenneth Cukier and Viktor Mayer-Schönberger.  Their 2013 Foreign Affairs article does a very good job of articulating why “big data marks the moment when the information society finally fulfills the promise implied by its name.”  Datafication is the term they give to our newfound ability to capture as data many aspects of the world and our lives that have never been quantified before.

Datafication is a fairly new concept associated with our recent data revolution.  But digitalization, - its companion concept which captures the impact of the digital revolution on the economy and society, - has been in use since computers were widely adopted around 60 years ago.  Its ensuing digital products have been generating all that data and its drastically lower technology costs have made it possible to store and analyze those oceans of information.

The Impact of Datafication on Strategic Landscapes, - a report published in April, 2014 by Ericsson in collaboration with the Imperial College Business School and the UK’s Sustainable Society Network, - nicely explains the differences and interrelationship between datafication and digitalization. 

“In contrast to digitalisation, which enabled productivity improvements and efficiency gains on already existing processes, datafication promises to completely redefine nearly every aspect of our existence as humans on this planet.  Significantly beyond digitalisation, this trend challenges the very foundations of our established methods of measurement and provides the opportunity to recreate societal frameworks, many of which have dictated human existence for over 250 years.”

Around 50 years ago, the nascent digital revolution gave rise to an exciting new discipline and profession, - computer science.  Similarly, our data revolution has now led to the emergence of data science as a hot new profession and academic discipline.  Data science research and educational programs are being organized in universities around the world.  In a 2012 Harvard Business Review article, Data Scientist: The Sexiest Job of the 21st Century, Tom Davenport and D. J. Patil, defined data scientist as “a high-ranking professional with the training and curiosity to make discoveries in the world of big data…  Their sudden appearance on the business scene reflects the fact that companies are now wrestling with information that comes in varieties and volumes never encountered before.” 

Data science and datafication may be new, but the relationship between computing and data goes back to the early days of the IT industry.  Data processing was the term then used to describe the applications of computers to automate highly structured business processes like financial transactions, inventory management and airline reservations.  Over time, sophisticated applications were developed to better manage the operations and associated data of the organization, including enterprise resource planning, customer relationship management and human resources. 

Beyond their use in operations, the data generated by these various applications was also used to improve the efficiency, financial performance, and overall management of the organization.  The information was generally collected in data warehouses, and a variety of business intelligence tools were developed to analyze the data and generate the appropriate management reports. 

These commercial applications dealt mostly with structured information in those early decades of computing.  But at the same time, the scientific research community was developing methodologies for managing, analyzing and visualizing the high volumes of much more unstructured data generated by their  experiments and observations.  Physicists, astronomers, biologists, geophysicists and other scientists and engineers were developing methodologies and architectures for dealing with very large volumes of unstructured data, as well as analytical techniques, like data mining, for discovering patterns and extracting insights from all that data. 

The explosive growth of the Internet since the mid-1990s has taken digitalization to a whole new level.  Digital technologies are now permeating just about every nook and cranny of the economy, society and our personal lives.  Data is now being generated by just about everything and everybody around us, including not only the growing volume of online and offline transactions, but also web searches, social media interactions, billions of smart mobile devices and 10s of billions of IoT smart sensors. 

All these data is enabling us to better understand the world’s physical, economic and social infrastructures, as well as to infuse information-based intelligence into every aspect of their operations.  It’s making it possible to not just better understand what’s happening in the present, but to also make more accurate predictions about the future.  Beyond its use in improving the operational efficiency and financial performance of companies, the data can now be applied to significantly improve customer relationships, as well as to create whole new classes of smart products and services. 

“This is the first time in human history that we have the ability to see enough about ourselves that we can hope to actually build social systems that work qualitatively better than the systems we’ve always had,” said MIT Professor Alex “Sandy” Pentland in an online conversation, Reinventing Society in the Wake of Big Data. 

“I believe that the power of Big Data is that it’s information about people’s behavior - it’s about customers, employees, and prospects for your new business,…  This Big Data comes from location data from your cell phone and transaction data about the things you buy with your credit card.  It’s the little data breadcrumbs that you leave behind you as you move around in the world… and by analyzing this sort of data, scientists can tell an enormous amount about you. They can tell whether you are the sort of person who will pay back loans.  They can tell you if you’re likely to get diabetes.” 

Our data revolution is inexorably linked not only to the digital revolution of the past several decades, but to the scientific revolution of the past few centuries.  Throughout history, scientific revolutions have been launched when new tools make possible new measurements and observations, e.g., the telescope, the microscope, spectrometers, DNA sequencers.   They’ve enabled us to significantly increase our understanding of the natural world around us by collecting and analyzing large amounts of data. 

One of the most exciting aspects of data science is that it can be applied to just about any domain of knowledge given our ability to now gather valuable data in almost any area of interest.  Our new data science tools are ushering an information-based scientific revolution, helping us extract insights from all the data we’re now collecting by applying tried-and-true scientific methods, that is, empirical and measurable evidence subject to testable explanations and predictions.

The Impact of Datafication report takes a close look at four areas where data science is already having an impact: 

Datafying personal behaviors:  “Traditionally, personality is measured by interviews and self-assessment questionnaires through sampling techniques…  The ability to accurately predict personality types using mobile phone data holds the potential for ‘cost-effective, questionnaire-free investigation of personality-related questions at a scale never seen before’…  Datafication of personality has the potential to disrupt several industries and also the manner in which social science research itself is conducted.” 

Datafying business processes:  “The lowering costs of sensors, increased processing capacity and availability of low-cost bandwidth mean that datafication is starting to enter into business processes that have until recently been un-monitored…  Financial services, for example, is starting to be changed by the use of micropayments, which are payments that cover small, incidental costs for example the purchase of a coffee or chocolate bar at a train station or as an individual moves through town for different meetings.” 

Datafying cities: “ICT will be used in a multitude of different manners that will allow the creation of new data streams in a city space, assisting with issues such as transport, energy and water management at a city level, urban planning, etc...  Customers are also able to take greater control over their transport choices - for example, by subscribing to information about their usual routes, an end-user may be able to know in advance that there is no point in going to the train station, but may instead take a bus or an alternative train line route.” 

Datafying private lives:  “A multitude of different technologies are now available from small and large companies that help individuals monitor and measure things that were previously difficult or impossible to quantify.  Everything - from how much energy and water I use, what my food purchasing habits are, how I use social networks, the air quality of my local neighborhood, when I am awake and asleep, knowing when I am stressed or when a certain temperature level has been reached in the air around me, what road I select to drive to work as a result, how many cups of coffee I drink in the morning, how I brush my teeth and what TV programs I decide to let my kids watch in the back seat of the car on long journeys - can now be measured, quantified and compared to other people.” 

“Digital technologies are recognised as creating disruption of established industries,… but we are currently in the throes of something much more profound - datafication does not change just how we do business with one another, or how we manage our companies, lives and cities - it begins to challenge some of the fundamental mechanisms upon which society has always depended upon – from the basis of the techniques used in the scientific method to how the economy is measured and structured.”

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