What Is Datafication? Exploring Its Evolution And Unforeseen Consequences

The role of data in our daily lives has expanded significantly. Many routine tasks like email, social media, online shopping, and banking now take place digitally. This flood of information powering modern technology has led to the emergence of new trends.

Although data has enhanced our world in many positive ways, its influence has brought some unforeseen changes, too. One development people are becoming more aware of is the idea of “datafication.”

Details on daily activity, shopping preferences, and interests are generated from fitness trackers, online orders, and social media use. More people’s routines are gradually becoming measurable electronic information through ongoing digital interactions.

This constant collection and transformation of human actions and behaviors into records for analysis online is known as datafication. It provides insights but also changes how parts of our lives are documented. This article will delve into the evolution of datafication, unforeseen challenges, and addressing the societal impact of datafication.

1. Evolution of Datafication

• The Dawn of Data Collection

In the early 1990s, when the commercial internet anddatafication were new, companies were starting to collect basic customer information. They gathered things like names, addresses, purchase histories, and phone numbers. This helped them begin to understand buying behaviors.

While those early data-gathering efforts allowed companies to learn more about customers, much less information was collected compared to the present day. The limitations in technology at that time also meant any analysis of the data had to remain relatively straightforward.

Things have advanced tremendously in the decades since, as technology has evolved to support much greater levels of information collection and skilled examination of that data.

• Rise of Big Data and Analytics

Come the new millennium, computing power expanded exponentially, according to Moore’s Law. Combined with falling data storage costs, this enabled a robust collection of digital footprints and interactions online. By the mid-2000s, companies amassed petabytes of user data by observing web and app usage, online searches, social media posts, and more.

Sophisticated analytics also emerged, utilizing machine learning algorithms to identify meaningful patterns and trends within these vast troves of information. For example, retailers could analyze shopping cart contents to see frequently bundled items. Tech giants recognized voice patterns to improve assistants.

Banks assessed financial transactions and credit applications to minimize risks. These insights powered individually tailored experiences, ads, and recommendations.

• Ubiquitous Connected Devices

As smartphones and powerful mobile apps became mainstream around 2010, billions of new internet-capable devices emerged beyond traditional computers. Wearables tracked steps, workouts, and vitals. Location data revealed spending habits at stores and venues people visited.

Smart home devices monitor utility usage, temperatures, and more. By the mid-2010s, the booming Internet of Things brought connectivity even to objects like appliances, city infrastructure, and industrial systems. This proliferation of online sensors expanded the scope of continuous, passively collected data into new domains and subtle aspects of modern life.

• Pervasive Social Media Tracking

The rising dominance of centralized social networks from the late 2000s had a huge transformation. It transformed both how people shared information publicly and how their digital behaviors were monitored privately. Uploading personal photos, constantly updating statuses, and streaming videos generated enormous volumes of seemingly innocuous metadata.

Meanwhile, platforms’ intricate algorithms learned from these public disclosures and linked them with other personal details, contacts, locations, and app engagement. These enabled the building of multidimensional profiles of traits, opinions, and attributes. Advertisers also leveraged these treasure troves of personal data to deliver hyper-targeted messages.

2. Unforeseen Challenges of Extensive Data Collection

• Hidden Surveillance

As data collection and analytics grew increasingly sophisticated, many did not foresee its potential use. The use such as for covert monitoring, profiling, and social influence either by malign groups or even state intelligence agencies.

The constant recording of sensor data or granting invasive app permissions under the guise of “personalizing services,” in fact, opened doors for undetectable tracking, breaches of privacy, and exploitation. Ordinary devices in people’s homes became potential listening posts without oversight or consent.

• Privacy and Identity Theft Risks

With data being consistently pooled and resold among corporations, vulnerabilities in any single system’s security may expose copied records held by others in cascading breaches. Centralizing personal digital dossiers also made them an attractive target. Simultaneously, the lack of legislative safeguards left individuals with little control over secondary uses of sensitive data.

• Algorithmic Bias and Filtered Feedback Loops

As data-driven technologies learn from existing data sources, there is a risk they could perpetuate biases or leave some groups out. Personalizing what people see based only on past interactions runs the risk of dividing people into separate belief bubbles. More so, over time, without exposure to different perspectives.

Data-based tools have real potential to do good. Still, people must proactively ensure they respect and benefit all members of society.

• Manipulation Through Targeted Influence Campaigns

Population-scale profiling empowers certain groups to covertly micro-target at-risk demographics. This risk enables manipulative messaging that sows societal divisions for political or commercial gain without transparency or oversight.

3. Addressing Societal Impacts of Datafication

• Strengthening Privacy Regulations

Laws need updates to:

• Curb invisible tracking.
• Strengthen security requirements for sensitive data.
• Mandate risk assessments for automated decision systems.
• Establish oversight for algorithmic auditing.
• Ensure due process for disputing profiling.
• Limit function creep for data originally collected in other contexts.

Rules must put individuals back in control through strengthened consent and purpose limitation tied to the severity of intended data uses.

• Algorithmic Transparency Requirements

Technical explanations of complex analytics must be translated into plain language understandable to laypersons. Individuals deserve insights into factors determining their experiences without compromising trade secrets. Public interest audits by neutral third parties could also evaluate models for fairness in representing various communities.

• Education For Data Literacy

School curricula must integrate basic concepts around digital citizenship, data science, ethics in AI, and managing an online identity early on. Public awareness campaigns bring powerful knowledge to civilians unaccustomed to weighing privacy trade-offs of newer technologies. Media literacy around online discourse aids critical thinking amid influence campaigns.

• Decentralization of Data Holdings

Sharing information across platforms and services gives people more control over their data. When people can store their data themselves, using something like a personal data store, they get to choose what information is shared, where it goes, and who can see it. Right now, most of the data ends up in big centralized systems that users need to understand and control fully.

Conclusion

In just a few decades, the rise of digital data collection has drastically changed society by recording many daily activities on a huge scale.

While this “datafication” aims to enhance lives through useful applications, the vast access it provides to people’s private information could also cause harm if not managed carefully.

Whether data collection builds a fairer, more cooperative future or increases problems in the community depends on continuous work to oversee its impacts, ensure transparency, and make algorithms answerable.

Source of Article: https://indiaai.gov.in/

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