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Introduction –

Datafication: Today, data is a vital corporate tool that has revolutionised industries like accounting and human resources. Datafication is still crucial even though it is a notion that was first used in 2013. Datafication, as opposed to digitization, aims to quantify social behaviour.

In this article, we argue that datafication encompasses more than merely collecting and analysing data; it also involves enhancing the quality of our daily lives by making them more effective, efficient, intelligent, and enjoyable. This article’s main goal is to demonstrate how datafication is a crucial component of digital strategy for businesses that want to stay competitive.

What is Datafication?

According to The Rise of Big Data: How It’s Changing the Way We Think About the World by Kenneth Cukier and Viktor Mayer-Schoenberger, “datafication” is a relatively new idea that defines how we “translate into data many aspects of the world that have never been quantified before.” In other words, this new phrase describes our capacity to gather information on previously unquantified areas of our existence and transform them into something valuable, such as knowledge.

The Ericsson research claims that private lives, cities, commercial operations, and personalities are all dataficated. For instance, the supply chain sector has been datafied, making it possible to track and trace things. LinkedIn has datafied our professional network and contacts. Our workouts are now recorded in our private life by means of devices and applications that monitor our heart rate, pulse, calories expended, distance travelled by running or walking, and other parameters.

Datafication, on the other hand, refers to the act of gathering data as well as the instruments and technologies that facilitate it. Organizations use data to create short- and long-term strategies, aid in decision-making, and keep track of business activities. The buzz surrounding big data has led to the establishment of numerous start-up businesses by gaining value from it. No company will be able to function in a few years without utilising the data at its disposal, and entire industries may require total re-engineering.

Datafication vs Digitalization – Digitalization and datafication, on the other hand, are distinct concepts. The latter term refers to the process of reshaping our society, economy, and personal lives through the use of digital technologies. It started with the development of computers and their use by businesses. Over the years, new technologies have gradually invaded our lives and revolutionised them, including the Internet of Things. The production and correct collection of data are already established, but the next stage of evolution is known as datafication, and it occurs when society starts to set up procedures for the extraction of important knowledge.

Importance of Datafication in a Business Organisation:

Using real-time data, datafication enables firms to enhance their goods and services. It is also a critical component of getting customer feedback on the calibre of the products and services any organisation offers.

Take data-driven marketing techniques as an example. The technique of gathering client intelligence through numerous channels, including social media, email, and other digital platforms, is one of the most crucial components of digital marketing. The data can be utilised to tailor campaigns to each customer while focusing on the appropriate audience profile.

Datafication – A New Business Model – In the digital age, creating an analytical culture that permeates all facets of business operations is what we’re talking about. The initial step in the datafication process is collecting data from various sources, but machine learning and artificial intelligence are equally important. In order to create information that can be used to make decisions, the gathered data will subsequently be analysed using AI/ML algorithms. The most crucial aspect is having a distinct vision and goal statement.

No matter how accurate the data is, nothing can be done without knowing what your company’s goals are. And the reason for this is that data can only be considered valuable within a reasonable context.

Data in the Cloud – The transition to cloud computing is a crucial issue that needs to be handled in relation to digital transformations, particularly this stage of datafication. A rising number of businesses have started moving their infrastructure into the cloud during the past several years.

What does this imply then? Well, first of all, it indicates that users of platform as a service (PaaS) or software as a service (SaaS) can do so (PaaS). Furthermore, since the supplier already offers servers, they are no longer obliged to buy them. All they do is pay to access the resources. Then there is infrastructure-as-a-service (IaaS), which entails that only the hardware is available for rent and not the operating system. You receive a virtual machine with your own operating system installed so you may execute your own programmes.

Data Protection – But why do so many IT departments still manage their own servers, when you give it some thought? They are, after all, expensive to purchase and maintain. Furthermore, if you don’t have any unique needs, why would you want to pay for something you could use for free in the cloud? The majority of businesses don’t truly understand what they need from IaaS, is the most common response. Although they could be considering it as a means to save money, there are other benefits as well.

For instance, some businesses might not want to cede control of their data or apps. Additionally, they could feel more secure with their own hardware (in this case, something more sophisticated). And given the sensitive data that many organisations gather, this is frequently a preventative action that makes room for investments in secret computing.

Great power entails enormous responsibility. Therefore, it is obvious that when datafication enters the world of digital transformation, data protection will need attention. Any action taken by a company to maintain the safety and security of personal data is known as data protection.

It Includes:

• Legal obligations – These include laws like the UK’s Data Protection Act 2018 and the EU’s General Data Privacy Regulation (EU GDPR). They also cover the collection, storage, handling, and disclosure of client data by businesses.
• Technical safeguards are procedures taken to ensure that data is secure both while it is in motion and while it is at rest. Technical security measures include things like access controls, encryption, and firewalls.
• Business practises refer to the interactions a company has with its customers and other stakeholders. Examples include marketing campaigns, sales processes, and customer service. Furthermore, unless there is a legitimate legal cause, it is illegal for businesses to process personal data without authorisation under a data protection scenario.

Your data cannot be handled if you do not give your consent, according to this. If you do grant your permission, it must be freely and knowingly provided.

Data is King in a Digital Economy – Until recently, all that existed in terms of data was paper documents or bits on floppy discs. In the modern world, we have access to a virtually limitless amount of data about people, places, things, services, and events. The market for big data and business analytics (BDA) now has more justifications for investment thanks to this abundance.

The Future of Business in Data Fluency – 

Making decisions based on data and having the skills to interpret the data that is readily available to us are more crucial than ever. This is now a reality because of the development of artificial intelligence, machine learning, big data analytics, and other technologies. According to McKinsey & Company, the biggest corporations in the world will generate $1 trillion in value from AI by 2025.

This figure illustrates how commonplace AI is across all business sectors. In this way, datafication is profoundly democratic and may be seen in a variety of fields, including human resources, accounting, marketing, and finance. AI has the potential to assist humans in making wiser judgments as long as there is data. In addition, by datafying new procedures and services, these emerging technologies have the potential to transform the way we conduct business.

Blockchain – More than ten years have passed since the invention of blockchain technology. It’s time to use this opportunity to revolutionise how companies connect with their customers. A distributed ledger called the blockchain records transactions between two parties without the aid of a third party. This implies that nobody is dependent on anyone else. Because every user has simultaneous access to the same information, the system is secure.

AIOps – AI-as-a-service The term “AIOps” is used to describe how AI tools are employed in organisations. AIOps are frequently available via a web browser or mobile app because they are cloud-based. They also give real-time insights into operations and processes. AIOps can therefore be applied for proactive maintenance, process optimization, and other operational upgrades.)

Machine learning is the type of AI that is used the most. Data that has been classified as good or negative by humans is used to train an algorithm for machine learning. The algorithm then makes predictions about fresh data using this knowledge.

For instance, you could train an algorithm to predict whether someone will buy something in the future if you have a dataset of people who have and have not purchased a product. Because it requires human input throughout the training phase, this sort of AI is known as supervised learning. There is no need for human supervision during unsupervised learning. It functions best when there is no obvious difference between instances that are positive and those that are negative.

FinOps – The activity of overseeing financial operations within an organisation is known as financial operations management (FinOps). FinOps encompasses all aspects of forecasting, risk management, and budgeting. Financial reporting is no longer the only concern. Financial reporting just makes up a small part of what FinOps covers. And in this situation, datafication is extremely important since it enables the integration and analysis of data that was before isolated in many systems.

Fintech is the name of this recent development in technology. It combines finance and technology. In fact, organisations like Google Finance and Intuit QuickBooks Online are only two examples of those who have effectively embraced FinOps.

Cognitive Computing – Cognitive computing is a catch-all phrase for the study of artificial intelligence, machine learning, and human-computer interaction. Data mining is used to extract insights from the vast amount of data. In order to tackle issues that we are unable to handle on our own, it is intended to make computers think like people. The development of tools like natural language processing (NLP) or pattern recognition methods, which are currently used to analyse text, images, and even speech, is an excellent example.

Edge Computing – Edge computing is the application of cloud-based services and technology to the outermost point of a network, such as in wireless sensors or mobile devices. Because it promises to speed up data processing and use less energy, a new technology known as edge computing has grown in favour.

Edge computing’s key benefit is the ability to process data locally without having to send it all back to the cloud. This reduces bandwidth usage, reduces latency, and improves user experience.

Microweather – In meteorology, the phrase “microclimate” (or “microclimate”) refers to the local meteorological conditions on a tiny scale, such as inside a specific building or on a street. Differences in temperature and humidity from those of the surrounding environment are frequently used to describe microclimates.

Consumers, businesses, and farmers in particular can benefit from the predictions made possible by the data collecting. In addition to producing thorough climate forecasts, the system uses sensors to measure the air quality, wind speed and direction, rainfall intensity and duration, soil moisture content, and other characteristics.

Warehouse Management Tech – Autonomous robotics, analysis, and prediction are a few of the buzzwords used to describe this developing business that improves warehouse management. The goal is to enable a robot to carry out activities automatically. It can achieve this by using sensors to recognise things or other entities in its environment. The robot then makes decisions based on these inputs about what to do next. Up till the assignment is finished, this process is repeated.

A picker robot taking products off shelves and packing them into boxes for transportation is a typical illustration. We can predict the robot’s future actions based on past behaviour by tracking its movements utilising data. Using this information, routes can then be planned through the warehouse to avoid wasting time wandering aimlessly.

Online Reputation Management – Online reputation management (ORM) is now a crucial tool in the toolbox of HR professionals. Monitoring online reviews is just one aspect of ORM; there are other aspects as well. It involves controlling your organisation’s online appearance.

Protecting your brand name or company name from being tainted by negative reviews posted on review websites like Google, Yelp, TripAdvisor, Facebook, and others is the goal of ORM. A new age has begun for the human resources industry, and it is a digital one. Hiring procedures tend to be data-driven, as do many HR tactics.

Examples of Datafication:

And there may be numerous instances of datification. Take as an example social media platforms like Facebook or Instagram. These platforms collect and track information about our friendships in order to promote products and services to us and provide businesses surveillance services, which in turn changes how we behave. Daily promotions on these channels are also made possible by the observed data. By using datafication to inform content rather than recommendation algorithms, this paradigm uses data to reinvent the creation of content.

There are other industries, though, where the 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:

An excellent illustration of the datafication process is Netflix, a distributor of online streaming media. More than 40 countries are served by its streaming service, which has 33 million subscribers. At first, commercial operations were mainly physically centred, with mail-order disc rental as its main activity (DVD and Blu-ray).To put it simply, the operational paradigm was that the subscriber established and managed their own queue (a ranked list) of the media items they wanted to rent (for example, a movie). 

If the overall number of CDs is restricted, the contents may be kept by the subscriber for as long as they see fit.To rent a new disc, the subscriber must return the old one to Netflix, who will then add the next available disc to the subscriber’s queue after receiving the old one back. Therefore, the disc rental model’s commercial objective is to assist customers in filling their turn. The business model has changed, and Netflix is now aggressively adopting datafication procedures to make their service into a smart one.

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