Datafication – Data has become a key tool for businesses today, revolutionising fields like accounting and human resources. Despite being a concept that was first used in 2013, datafication is still very important. Datafication, as opposed to digitization, aims to quantify social behaviour.
In this post, we contend that datafication entails more than just gathering and analysing data; it also entails improving the quality of our daily lives by making them more effective, efficient, intelligent, and pleasurable. 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. Through applications and devices that track our heart rate, pulse, calories burned, number of kilometres run or walked, and other metrics, our workouts are now datafied in our private lives.
Datafication, on the other hand, refers to the act of gathering data as well as the instruments and technologies that facilitate it. Data is used by organisations to develop short- and long-term plans, support decision-making, and monitor operations in the corporate world. 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 – Datafication, on the other hand, is not the same as digitalization. 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. New technologies, including the Internet of Things, have steadily entered our lives over the years and revolutionised them. 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. Additionally, it is a crucial part of gathering client feedback on the calibre of the goods and services any organisation provides.
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. Both machine learning and artificial intelligence are crucial to the datafication process, but the first stage is gathering data from multiple sources. The obtained data will then be analysed by AI/ML algorithms to produce information that can be used to make decisions. The most crucial aspect is having a distinct vision and goal statement.
No matter how accurate the data is, nothing can be accomplished without a clear understanding of your business’s objectives. And the reason for this is that only inside a sensible context can data be deemed valuable.
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). Additionally, they are no longer required to purchase servers because the provider already provides 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, when you give it some thought, do so many IT departments still run their own servers? 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 clear that data protection will require attention as datafication moves into the realm of digital transformation. Any action taken by a company to maintain the safety and security of personal data is known as data protection.
- Legal requirements – These include regulations like the EU General Data Privacy Regulation and the UK Data Protection Act 2018 (DPA) (EU GDPR). Additionally, they discuss how businesses gather, maintain, use, and disclose customer data.
- Technical measures – Technical safeguards are steps taken to keep data secure while it is in motion or at rest. Firewalls, encryption, and access controls are a few examples of technical measures.
- Business practices – The way a business engages with its clients and other stakeholders is referred to as its “business practises.” Marketing initiatives, sales procedures, and customer service are a few examples. Additionally, under a data protection scenario, it is against the law for companies to process personal data without authorization unless there is a valid legal justification.
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. The greatest companies in the world will produce $1 trillion in value from AI by 2025, according to McKinsey & Company.
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). Budgeting, forecasting, and risk management are all included in FinOps. Financial reporting is no longer the only concern. Only a portion of what FinOps entails is financial reporting. And in this situation, datafication is extremely important since it enables the integration and analysis of data that was before isolated in many systems.
This emerging technology trend is referred to as fintech. Finance and technology are combined in it. In fact, organisations like Google Finance and Intuit QuickBooks Online are only two examples of those who have effectively embraced FinOps.
Cognitive Computing – A general term for the study of artificial intelligence, machine learning, and human-computer interaction is cognitive computing. Here, data mining is performed to glean insights from voluminous 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. A new technology called edge computing has gained popularity because of its promise to speed up data processing and use less energy.
Edge computing’s key benefit is the ability to process data locally without having to send it all back to the cloud. By using less bandwidth, this lowers latency and enhances 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. The system employs sensors to detect the air quality, wind speed and direction, rainfall intensity and duration, soil moisture content, and other variables in addition to generating comprehensive climate forecasts.
Warehouse Management Tech – Some of the buzzwords around this emerging market that helps warehouse management more effectively include autonomous robots and analysis prediction. The goal is to enable a robot to carry out activities automatically. To do this, it can employ sensors to identify objects or other entities in its surroundings. 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. By tracking the robot’s movements using data, we may anticipate its future moves based on past behaviour. 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. Consider social media sites like Facebook or Instagram as an illustration. 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. The observed data also leads to daily promotions on these channels. This paradigm uses data to reimagine the process of content creation by employing datafication to inform content as opposed to recommendation algorithms.
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.
An excellent illustration of the datafication process is Netflix, a distributor of online streaming media. 33 million people subscribe to its streaming service, which offers services in more than 40 nations. Initially, business operations were primarily physically focused, with its primary activity being mail-order disc rental (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).
The contents can be kept for as long as the subscriber would want if the total number of discs is limited. 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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