Datafication – The idea of data
Let’s first spend a moment discussing the notion of data before moving on to the definition of datafication. Data is a term in computers to refer to information converted into a format that can be moved or processed quickly. It exists as a binary digital form right now.
The process of converting everything in our life into machines or software that is driven by data is referred to as datafication.
Any quantifiable activity made by anyone using practically any technology can result in information generation.
Thus, you create data if you access your email, purchase with a credit card, or unlock a personal device. Your kids produce data while they play another level of their favorite game, check their social media feeds, or visit a store while carrying a smartphone.
As your boss travels through the sensor-filled smart office or when his car’s license plate triggers the garage doors to open automatically, a significant amount of data generated. Additionally, your phone is continuously updating your location, adding it to pictures, and informing other devices of where you are (learn more about beacons) if you grant it permission.
Data also generated by the sun and rain (collected by the sensors). Your smart home’s appliances are. A tram came to a stop at the signal. the basement water heater.
Even your dog can produce data if a shop or dog park installs a scanner that can read its chip number (and connect it to your customer profile). You both pass past a security camera at an ATM nearby, and it’s surely recording the data and sights.
Results in two significant findings
First, data is a very abstract concept that doesn’t exist in the natural world. We produce it by gathering and analyzing data from various actions as they take place; you may compare its gathering to “taking a picture.” It glimpses the truth of a straightforward parameter and then permanently freezes it.
Therefore, to see a change, you must take a good number of images, compare them, and then zero down on a particular aspect. However, some cameras have excellent lenses that capture genuine colors, while others only capture black-and-white, sharply contrasted frames.
The quantity of information gathered by sensors vs sophisticated equipment differs in this way (such a s smartphone).
Second, the data processing options constrained by the capability of the tools used for the job and our imagination.
Some devices generate much more data than anticipated, leading to complex big data clusters and just a tiny amount of “conventional” sorted datasets. Consider the “backgrounds” of your photo collection, which may compare based on several criteria.
We have not yet used sophisticated machine learning algorithms spread across a network of computers to examine (and then classify) those enormous clusters (measured in tera-, peta-, and exa-bytes). And all the while, real-time acquisition of new records is ongoing.
Let’s hope that data science, which integrates math, programming, domain expertise, scientific methods, algorithms, procedures, and systems to make dealing with data easier, would help manage such enormous amounts of data.
Science of datafication
Researchers increasingly use the term “datafication” to describe how digital interactions transform into records that can be gathered, analysed, and sold. The term was first popularized by Mayer-Schoenberger and Cukier’s (2013) early definitions of “big data.”
To enable real-time tracking and predictive analysis, it is important to remember that data collecting is a continual process requiring digitizing as many parts of our lives as possible. Since continuity is a problem, information is routinely acquired, processed, and stored in specialized data infrastructure businesses or governments typically hold.
- Datafication is already helping society by keeping track of the weather and seismic activity, enhancing healthcare, spotting fraud, and monitoring student development.
- the number of recordings rises, an increasing number of enterprises are seeking fresh approaches to turn even more facets of human existence into a constant stream of data, focusing on social interactions in particular.
- The main driver of this transition is the ability to track, analyze, enhance, and profit from routines and habits once they are transform to data.
- Businesses now have the chance to turn human behavior into information that can use to influence customer behavior and alter the main business plan. or to enable social services to find individuals in need as soon as possible. In general, data is more valuable the more sorts of value it may produce.
- What matters is that firms may still get a lot of data, store it, and then determine how to utilize it later, even if they don’t use it immediately.
- Consequently, organizations can now start gathering information on previously untraceable procedures. After processing, they might also become data-driven (being able to, i.e., reduce the risk of introducing new products or services to the market).
Comparing datafication and digitization
- Datafication is distinct from digitization, which transforms analog content, such as books, movies, and images, into digital information, or a series of ones and zeros that computers can read.
- The process of datafication, which involves converting all facets of life into data formats, is much more comprehensive. When we datafy something, we can change its function and transform the data into new types of value.
- In contrast to digitization, which involves transforming selected media into computer-ready format, datafication focuses more on the process of gathering, processing, and storing customer data from actual actions.
- To return to the picture analogy, digitization entails uploading the image to a server. In contrast, datafication entails providing a set of analytical tools to track the evolution of the image over a selected time frame.
controversy surrounding datafication
The heated discussions regarding how organizations or geographic areas discriminate against people, particularly from lower-income or minority groups, using datafication in certain contexts.
A list of the most common datafication problems is provided below in addition to that:
Data is accessible to everyone. The more information we gather, the more accurate information we can uncover about a person.
The legal system, the media and some businesses already use this to conduct background investigations on specific people by associating them with particular events, activities, and ideologies. Regrettably, hackers and spammers can study the same data to commit identity theft and other types of cybercrime.
Every activity within its scope is monitoring using data.
- Tech giants like Facebook, Apple, Microsoft, Google, Amazon, Baidu, Alibaba, Xiaomi, and others force datafication on its consumers by storing enormous datasets on multi-store server rooms that are updated daily.
- The level of interference is often governed by law and used for paid ad personalization within the company’s apps and platforms. Regrettably, in certain places the government has also adopted similar monitoring techniques.
- In other instances, the legal system is attempting to protect individual autonomy from the risks associated with ongoing data collecting (by implementing i.e., GRPR).
- The GDPR and other anti-datafication policies, such as platform opt-outs, might impact future data collection. Some claim that this technological trend is among the most important social challenges of our day because of the close connection between children and social media.
PROS
- The data, knowledge, and mathematically confirmed steps they provide aid humans in making more informed decisions.
- They help us stay connected and can advise on everything from what to watch to what to eat for dinner.
- As financial institutions shift away from utilizing criteria like race, socioeconomic status, postal code, and the like to determine loan eligibility, more people will be able to secure financing. And with additional information, banks may lower their risk and increase the number of loans they make.
- People will have more control over their health conditions and be less of a drain on medical resources.
- Government may reduce bureaucracy and spend less time monitoring and regulating things like traffic.
- Algorithms have the potential to reduce pollution, traffic, and economic waste PROS
CONS
- We can’t fully understand the algorithms that guide our daily actions and decisions.
- Most algorithms today focus on profit maximization and efficiency rather than societal effects.
- People may find it too easy to accept an algorithm’s suggestions, which gives those in power an unfair edge.
- Differential pricing will increase as companies use customer information to set personalized prices.
- We fear political and economic manipulation, lack of privacy, and self-determination due of the internet’s algorithms.
- Global rivals will grab most available resources, thus algorithms can eradicate local intellect, talent, minority languages, and entrepreneurship.
Today, humans are still the primary authors of algorithms. Intelligent, learning machines expectes to play a significant role in their evolution in the future…. There is a danger that humans will lose control over their own lives.
There is inherent bias in any data set. All past, present, and projected data are tainted with bias. The machine decided, thus we have to accept it, which could lead to the institutionalization of harmful, biased conclusions.
Also Read – The New Wave of Technology: AI, Machine Learning, and More (informationtechnologypros.com)
