Datafication
What is “datafication”?
Datafication refers to the notion of translating human activities into data, which is subsequently repackaged into units that offer new forms of value.
Datafication is the process of transforming data into usable and actionable information. It involves collecting, organizing, and analyzing data to facilitate decision making. Datafication can be used to gain insights into large data sets, improve customer experience, and create efficiencies in the workplace.
Datafication, according to MayerSchoenberger and Cukier is the transformation of social action into online quantified data, thus allowing for real-time tracking and predictiveanalysis. Simply said, it is about taking previously invisible process/activity and turning it into data, that can be monitored, tracked, analysed and optimised.
Summarizing, datafication is a technological trend turning many aspects of our lives into computerized data using processes to transformorganizations into data-driven enterprises by converting this information into new forms of value. Datafication refers to the fact that daily interactions of living things can be rendered into a data format and put to social use.
Datafication, according to MayerSchoenberger and Cukier is the transformation of social action into online quantified data, thus allowing for real-time tracking and predictive analysis. Simply said, it is about taking previously invisible process/activity and turning it into data, that can be monitored, tracked, analysed and optimised. Latest technologies we use have enabled lots of new ways of ‘datify’ our daily and basic activities.
Margarita Shilova. Datasciencecentral The-concept-of-datafication
Datafication is a technological trend swifting many aspects of our life’s actions, moments, and works into data with the help of technologies like Artificial Intelligence, Machine Learning, Robotics, and many other emerging stacks.
The crucial context of datafication is to enable better decision-making, effective understanding of analytics, efficient utilization of resources and achieving maximum growth.
Its application is vital for many institutions, firms, businesses, and industries.
Kenneth Cukier and Viktor Mayer-Schönberger introduced the term datafication to the broader lexicon in 2013 Up until this time, datafication had been associated with the analysis of representations of our lives captured through data, but not on the present scale. This change was primarily due to the impact of big data and the computational opportunities afforded to predictive analytics.
Datafication is not the same as digitization, which takes analog content—books, films, photographs—and converts it into digital information, a sequence of ones and zeros that computers can read. Datafication is a far broader activity: taking all aspects of life and turning them into data [...] Once we datafy things, we can transform their purpose and turn the information into new forms of value
There is an ideological aspect of datafication, called dataism: "the drive towards datafication is rooted in a belief in the capacity of data to represent social life, sometimes better or more objectively than pre-digital (human) interpretations.”
Datafication has become increasingly popular in recent years as businesses have adopted digital technologies and become more data-driven. Companies are taking advantage of big data to gain insights into their operations, customers, and markets. This allows them to make better decisions, which can result in increased revenues, improved customer satisfaction, and greater competitive advantage.
Datafication involves a variety of techniques, including data mining, machine learning, and predictive analytics. By applying these methods to large data sets, businesses can identify patterns and relationships that may not be immediately apparent. This can help them uncover opportunities, improve their products and services, and gain a better understanding of their customers. Datafication can also be used to automate tasks and processes. By using automation, businesses can reduce labor costs, improve productivity, and increase efficiency. Automating mundane tasks can also free up employees to focus on more important tasks. Datafication is being used in a variety of industries, from healthcare and finance to retail and manufacturing.
By harnessing the power of data, organizations can gain insights into their operations, customers, and markets to make better decisions. This can lead to improved customer satisfaction, increased revenues, and a competitive edge. In the future, datafication will continue to become more important. Data-driven decision making will become even more prevalent as businesses look for ways to gain an edge over their competitors. In order to stay ahead, businesses will need to continue to invest in datafication and make sure they have the right tools, techniques, and strategies in place.
Source : Techpinas Datafication Wikipedia Datafication
Summarizing the definition, datafication is a technological trend turning many aspects of our lives into computerized data using processes to transform organizations into data-driven enterprises by converting this information into new forms of value.
Datafication refers to the fact that daily interactions of living things can be rendered into a data format and put to social use.
In this article...
The Verge Of Datafication In Business
The Future Of Datafication In Various Realms
The controversion over datafication
The concept of data
.In computing, data is defined as information that has been translated into a form that is efficient for movement or processing. As of today, it is a binary digital form.
A piece of information can be generated by any measurable action taken by anyone using almost anything tech-related. So, you generate data when you use your email, pay with a credit card, or unlock a personal device. Your children generate data as well while they complete another level of their favorite game, check their social media feeds, or go to a brand store with a smartphone in their pocket. Your boss generates a solid portion of data as he moves around the smart office, which is jam-packed with sensors, or when his car registration activates automatic garage doors. Your phone is also streaming data and constantly updating your location, adding it to photos, and, if you give it permission, letting other devices know where you are (learn more about beacons).
The sun or rain generates the data as well (collected by the sensors). Your smart home devices do. A tram stopped at a red light. A water heater in your basement. Even your dog can generate data if a store or a dog park installs a scanner that can read its chip number (and connect it to your customer profile). And the data is undoubtedly recorded as you both pass by a security camera at a nearby ATM – and we're not talking about the visuals only.
This leads to two important observations.
- To begin with, data is an extremely abstract concept that does not exist in nature. We create it by collecting and processing data from various actions as they occur – you can think of its collection as “snapping a photo”. It catches a glimpse of reality of a simple parameter and freezes it forever. So, to observe any change you have to take a solid handful of photos and compare them, focusing on a chosen factor. But some cameras have great lenses and true colors and others shoot only black-and-white, highly contrasted frames. This is the difference between the number of details collected by sensors vs. complex devices (such a s smartphone).
- Secondly, the data processing possibilities are only limited by the capabilities of the devices assigned to the task and our own creativity. As a matter of fact, some devices collect far more data than expected, resulting in complex big data clusters aside to a small portion to "traditional" sorted datasets. Think of them as the “backgrounds” of your picture collection, which can also be compared, based on a variety of factors.
Those massive clusters (measured in tera-, peta-, and exa-bytes) have yet to be analyzed (and then classified) using advanced machine learning algorithms distributed across a network of computers. All while continuously acquiring new records in real time.
Data science, which combines math, programming, domain knowledge, scientific methods, algorithms, processes, and systems to make working with data easier, promises to aid in managing such massive amounts of data.
Datafication in data science
The concept of datafication was popularized by Mayer-Schoenberger and Cukier's (2013) early descriptions of "big data", and is now used by researchers to describe how digital interactions are being turned into records that can be collected, processed and finally - sold.
Keep in mind that data collection is an ongoing process that involves converting as many aspects of our lives as possible into computerized data in order to enable real-time tracking and predictive analysis. Due to the issue of continuity, gathered information is automatically collected, processed, and stored in dedicated data infrastructure, which is mostly owned by corporations or governments.
Datafication is already assisting society by monitoring weather and seismic activity, improving health care, detecting fraud schemes, and tracking students' progress. And, as the number of records grows, more and more businesses are looking for new ways to turn even more aspects of human life into a continuous source of data, with a particular emphasis on social interactions.
The primary reason for this shift is that once habits and routines are converted to data, they can be monitored, analyzed, improved, and monetized. This provides businesses with the opportunity to translate human behavior into practical knowledge that has the potential to influence customer actions and adjust core business strategy. Or, for social organizations to quickly identify those in need. In general, the more types of value that data can generate, the more valuable it is.
What matters most is that businesses can still collect a large amount of data, store it, and decide how to use it later, even if they don't use it right now. As a result, businesses can now begin collecting data on previously untraceable processes. And, once processed, they can become data-driven (being able to, i.e., reduce the risk of introducing new products or services to the market).
Datafication vs. Digitization
According to the original Big Data article (2013) by Mayer-Schoenberger and Cukier,
“datafication is not the same as digitization, which takes analog content—books, films, photographs—and converts it into digital information, a sequence of ones and zeros that computers can read. Datafication is a far broader activity: taking all aspects of life and turning them into data format [...] Once we datafy things, we can transform their purpose and turn the information into new forms of value.”
So, in fact, datafication is more about the process of collecting, storing, and managing customer data from real-world actions, while digitization is the process of converting chosen media into computer-ready format.
Going back to the picture metaphor, digitization uploads it to the server, whereas datafication provides a set of analytical tools to measure its changes over a chosen period of time.
Datafication examples
Examples of datafication as they relate to social and communications media include:
- How Twitter ‘datafies’ stray thoughts or the datafication of HR by LinkedIn
- Facebook’s misuse of data in the Cambridge Analytica scandal
- Netflix’s transformation from a mail order-based disc rental service to an online entertainment powerhouse
Examples of datafication as applied to social and communication media are how Twitter datafies stray thoughts or datafication of HR by LinkedIn and others. Alternative examples are diverse and include aspects of the built environment, and design via engineering and or other tools that tie data to formal, functional or other physical media outcomes. Data collection and -processing for optimal control (e.g. shape optimization) is an example.
Social platforms, Facebook or Instagram, for example, collect and monitor data information of our friendships to market products and services to us and surveillance services to agencies which in turn changes our behaviour; promotions that we daily see on the socials are also the result of the monitored data. In this model, data is used to redefine how content is created by datafication being used to inform content rather than recommendation systems.
However, there are other industries where datafication process is actively used: ‹
Insurance: Data
used to update risk profile
development
and business
models.
Banking: Data used to establish trustworthiness and likelihood of a
person
paying back a loan.
Human resources: Data used to identify e.g. employees risk-taking
profiles.
Hiring and recruitment: Data used to replace personality tests.
Social science
research: Datafication
replaces sampling techniques and restructures the manner in which social science
research is performed.
Netflix Case
Netflix, an internet streaming media provider, is a bright example of datafication process. It provides services in more than 40 countries and 33 million streaming members. Originally, operations were more physical in nature with its core business in mail order-based disc rental (DVD and Blu-ray). Simply said, the operating model was that the subscriber creates and maintains the queue (an ordered list) of media content that they want to rent (for example, a movie). If you limit the total number of disks, the contents can be stored for a long time, as the subscriber wishes. However, to rent a new disk, the subscriber sends the previous one back to Netflix, which then forwards the next available disk to the subscribers queue. Thus, the business goal of the disk rental model is to help people fill their turn. The model has changed and now Netflix is actively transforming their service into a smart one, actively using datafication processes.
It’s noticeable that in all aspects of the streamlined implementation of the Netflix business, a gradual change occurs where the IT infrastructure and artifacts completely free media content from its physical manifestation; for example, a disk and its mail delivery. While streaming, subscribers can select videos before making a reservation, they can consume multiple videos in one session and observe viewing statistics to a much finer degree; and in real time, to a greater extent. Therefore, much more data is dematerialized in the streaming model. In addition, data sources have become diverse and diverse – including catalog data (more than 1000 facets are now associated with the title), search terms, streaming queues and games, interactions and external sources such as movie reviews and social data. Removing time and distance from the business model has increased the potential for interaction between the provider and the subscriber through dynamic personalization: by household, genre, etc. Explaining the content to promote trust, ranking, ranking and review and social influence resulting from the fact that associated friends watched or evaluated.
On a daily basis, Netflix’s dematerialization has about 30 million daily games and 3 million odd queries to inform about the dynamics of recommendations. What offers through dematerialization and a combination of liquidity has allowed an interesting manifestation of density due to the recent transition of Netflix from streaming content to its creation. Statistical analysis of user behaviour over the years has been used to inform content, not recommendations, introducing Netflix with an interesting intersection of the genre, actors and director. The result of this data crossing was their recent remake of the television series House of Cards, a political thriller.
Data can be gathered practically at every point of contact between technology and our everyday life. For example, you can store: numbers, text, images, routes, audio and mobile data, IP addresses; but also clicks, scrolls, interaction times, logins and passwords, acquisition paths and device activity logs.
The most well-known sectors who use datafication are:
- social platforms (i.e., Facebook, Instagram, LinkedIn, and TikTok) which invite users to move their environmental relationships online, stay active, and share as much social data as possible—especially when it comes to profile updates, reactions, and preferences. The data is mainly used for paid ads profiling.
- internet streaming platforms (i.e., YouTube, Netflix, HBO, and Disney) which supplement traditional television combined with blockbusters. The main goal here is to make binge watching addictive again. The data is used to plan customized, influential media content and recommendations.
- banking which provides a secure network to use money online. The data is used to assess clients' credit scores (“trustworthiness") and suggest the best ratio between risk and profit from lending the money. This way, banks can identify risk taking profiles and conduct statistical analysis. In other words, datafication replaces sampling techniques, constantly updating the outcome with the use of monitored data.
- Human Resources/journalism – the publicly available data can be used to verify the person's background. Also, the data collected within the company can assess the employee productivity and-based on a chosen set of factors-the chance for a raise. It can be also used as a substitute to extend or even replace personality tests.
As a matter of fact, each company that either uses e-mail, owns a website, has a marketing/logistic department, or monitors its production chain, is already collecting a number of data points that can be used and should be (for the best possible results) expanded and updated. What’s interesting, the number of business-related data is so huge, that in 2018, the amount of data generated by commerce has already out numbered that generated by the datafication of human life.
Datafication: Impact
- Human resources. In some cases, data obtained via mobile phones, apps or social media is used to identify potential employees, their specific characteristics and personality traits. It’s possible that this type of data extraction and analysis will replace personality tests or certain behavioral interviewing questions.
- Insurance and banking. In these industries, datafication allows for a keen understanding of an individual’s risk profile and their trustworthiness as a borrower.
- Customer relationship management. Businesses are using datafication to delve into consumer needs and wants. This data can be obtained via the language and tone that a person uses in emails, phone calls, or social media.
- Smart cities. The data collected via smart city systems can be used in areas ranging from transportation, to waste management, to logistics, to the energy sector. In addition, real-time datafication could enable groups to gain more detailed insights into pollution levels, water quality, and necessary environmental regulations.
Data obtained from mobile phones, apps or social media usage is used to identify potential employees and their specific characteristics such as risk taking profile and personality. This data will replace personality tests. Rather using the traditional personality tests or the exams that measure the analytical thinking, using the data obtained through datafication will change existing exam providers. Also, with this data new personality measures will be developed.
Insurance and Banking
Data is used to understand an individual's risk profile and likelihood to pay a loan.
Customer relationship management
Various industries are using datafication to understand their customers better and create appropriate triggers based on each customer's personality and behaviour. This data is obtained from the language and tone a person uses in emails, phone calls or social medias
Street lamps in Amsterdam have been upgraded to allow municipal councils to dim the lights based on pedestrian usage.
Through the data obtained from the sensors that are implemented into the smart city, issues that can arise might be noticed and tackled in areas such as transportation, waste management, logistics, and energy. On the basis of real-time data, commuters could change their routes when there is a traffic jam. With the sensors that can measure air and water quality, cities can not only gain a more detailed understanding of the pollution levels, but may also enact new environmental regulations based on real-time data.
In short, new ways to explore, process, store and visualize information have led to and will continue to result in new business and societal benefits.
Datafication: Acceleration
As an increasing number of dimensions of our lives play out in digital spaces, some argue that datafication will intensify. Data science is an in-demand skill set in the work place, as exemplified within Mastercard’s latest business development programs.
Datafication may represent the next mass-enterprise migration. “We’re entering a new world in which data may be more important than software,” stated Tim O’Reilley, Founder of O’Reilly Media.
The Verge Of Datafication In Business
Business is a big entity. It incorporates many actions in the direction to achieve objectives and goals. Therefore, a multitude of workforce, plans, strategies, and expenses are short to accomplish the paradigm.
In the middle of all of this, data plays an important role. It helps in planning, directing, staffing, and coordinating according to the principle as needed.
Datafication is in trend now and practicing it makes it more illustrious. This emerging technology trend helps businesses improve their services, products, and management.
Take data-driven marketing strategies as an example. Their datafication helps understand targeted customers’ preferences, actions, likes and dislikes, and upcoming perceptions towards new products and launches.
The Future Of Datafication In Various Realms
The success of a business highly depends on a careful, attentive, and fail-proof decision derived from the sources of primary and secondary practices.
And now the decisions are made by data-driven things, algorithms, and machines like RPA and others. Many entrepreneurs believe that AI-accredited machines can make better decisions than humans as they learn, adapt, and never-stop exploring emotions of the human based on the campaign.
Their processed data can be presented at their fingertips is becoming more important than ever. With the support of other emerging technologies such as Data analytics, Artificial Intelligence, and Machine Learning have made this reality.
Here are some area of applications where datafication plays big role for us:
Blockchain
We all know about it and many of us are groomed with its implications already. It’s a technology based on a distributed ledger that records transactions conducted using cryptocurrencies.
Datafication, here, contributes in a big way. Help engineers know better about its working, develop secured data protection, and many things for blockchain owners.
AIOps
AIOps is another extended technology that uses datafication in its sphere. As it is cloud-based it uses various AI tools to provide real-time data, insights, and metrics on almost everything. It can be accessed in the web browser or mobile.
Cognitive Computing
It is a type of computing that uses intelligence to learn more about the subject or input. It uses artificial intelligence, machine learning and human-computer interaction. As it interprets learning of the subject, it uses data mining to extract knowledge from large amounts of information.
You may have heard of NLP (Natural Language Processing) used by Google to better understand search queries of the intent. This is an example of datafication in superior form.
Micro weather
Micro weather or microclimate is a term used in meteorology that includes learning, extracting, and understanding the data related to weather conditions. This field is characterized by differences in temperature and humidity.
The predictions are derived from the data collected using the meteorology method which further helps consumers, companies and, especially, farmers to measure air quality, past, today, and tomorrow’s weather condition, wind speed and direction, rainfall intensity, and much more.
Amelia Scott The-next-tech What-is-datafication-does-business-future-depend-on-it
The controversion over datafication
There have been big debates about how corporations or regions use datafication in certain areas to discriminate against people, especially those from lower-income or minority groups.
Aside from the that, here’s the list of the most frequently mentioned datafication issues:
- Data can be accessed by anyone. The more data we collect, the more precise information on an individual we can dig up. This is already in use by the law, journalists, and some companies to run a background check on a specific person, connecting them with a specific place (and time), actions, and even ideology. Sadly, the same data can be analyzed by a hacker or spammer to perform identity theft or other forms of cybercrime.
- Data is used to monitor every activity within its reach. Massive datasets are stored (and daily updated) on multi-store server rooms owned by tech giants (Facebook, Apple, Microsoft, Google, Amazon, Baidu, Alibaba, Xiaomi, and so on), forcing datafication on their users. Collected data is then used for paid ad personalisation within the giant’s apps/platforms, and the level of interference is usually regulated by the law. Sadly, in some regions, the government has adapted similar monitoring methods too. In others, the law is trying to shield individual autonomy from the dangers of continuous data collection (by implementing i.e., GRPR).
- Data is a commodity. Platforms are a new kind of multi-sided datafication market. The currency is data. To produce it, tech giants bring together platform users who create data, data buyers (like advertisers and data brokers) who are willing to exchange it for real money, and service providers who profit from the release, sale, and internal use of data. Contrary to the typical goods, datasets can be not only stolen, re-sold, but also used to commit cybercrimes on users, whose records are gathered in a compromised set.
- Data is collected globally. Data surveillance is not limited to a region or a language. In other words, platform owners are now able to store information regarding every person on the planet who has access to the Internet. This is especially important in times of rising cybercrime, which is usually more efficient in attacks on smaller platforms.
GDPR and other anti-datafication measures, such as platform opt-outs, may have an effect on future data collection. Because of the strong association of children in social media, some argue that this technological trend is one of the most pressing social issues of our time.
Datafication for business
As the rapid advancements in data collection can be witnessed daily over the last decade-including much more powerful computational power, advances in AI, and the vast storage capacity of cloud computing-we must now learn to handle data responsibly. Keep in mind that the information you store represents a piece of current society's organization. And the way you use it may affect it in real time.
You can create a new trend, habit, or influence a whole generation with your tailored media content. But you can also make someone isolated, in debt, hostile towards other brands/ideas followers, or mold them into some kind of collective, labeled "segment"-so they become a perfect copy of each other (‘wine mom’ or ‘insta travel girl’ anyone?).
"Dark data" is a Gartner term for “information assets organizations collect, process, and store during regular business activities but generally fail to use for other purposes (for example, analytics, business relationships, and direct monetization). This "dark matter" of data is in fact a majority of records gathered by companies, using space and resources and posing the risk of consequences in the event of data theft.
Take this section with a grain of salt, but keep in mind that the majority of core business decisions are now supported by some pre-analyzed data, which is typically sorted into several clusters that do not favor diversity. This could have far-reaching consequences for some.
To avoid this scenario (and handle dark data as effectively as possible), it is best to combine datasets so that we have access to the most comprehensive and up-to-date data stream depicting our users. Remember to run the results through new algorithms on a regular basis to discover new, emergent categories.
Conclusion
As the industrial age ended, computers and easy Internet access revolutionized how we live today. Almost everyone has an Internet-connected computer and generates data. Also, the number of devices that create data is constantly growing.
Corporations are the main beneficiaries here, but in several regions, the government profits from constant surveillance as well. Assuming the problem isn't the data, we should always ask if datafication can be even fairer towards individual users. Who should control dataset access, and how can we spot breaches? How to transfer the "right to be forgotten" to multiple devices that collect dark data about us? Should we store our data online if it can't be deleted?
Although the concept of datafication may scare some of us, properly handled datasets (by law regulations, security measures, and work ethics) could bring more industries into a world of less aggressive ads and more customer-friendly services, as each experience could be improved due to thousands of records collected (as opposed to decades on the market). And in which brand size and name will no longer be a deciding factor when it comes to choosing a provider.
DawidPacholczyk, Piotr Wawryka Codete Datafication-concept-definition-and-examples
There are many fields where datafication contributes its charm. Digital marketing, Warehouse management, Quantum Computing, Edge computing, and FinOps are some more examples of datafication usage.
Additionally, US startup companies are also investing in this technology to get severe benefits for competitive survival. The attempts are severely shooting for data-driven strategies to offer the best solutions.
As datafication becomes more common and the impact of peoples’ lives more widespread, the development of new frameworks for understanding is becoming increasingly necessary. In addition, datafication requires a significant re-assessment of several areas of an industry’s operation.
Main sources :
Big Data: A Revolution That Will Transform How We Live, Work, and Think by
Viktor Mayer-Schönberge
The impact of datafication on strategic landscapes by Ericsson
Datafication, Dataism and Dataveillance: Big Data between scientific paradigm
and ideology by José van Dijck
Margarita Shilova Datafloq Datafication-concept-definition-examples
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