In the area of improvement, it is often said that measurement is key. Without measurement, progress remains elusive. However, the challenge lies not in the scarcity of data but in effectively harnessing the power of data collection and analysis.
In this era of technology and information, datafication has become a vital pillar for organizational functioning and strategic decision making. Get ready to delve into the concept of datafication, highlighting its importance for future organizations and exploring the associated challenges and considerations within this context.
Datafication is the process of transforming various aspects of our lives, activities and environment into digital data. It involves converting analog processes into data-driven systems. In simpler terms, almost every action we take in our daily lives, whether buying a soft drink or using social media, generates data that can be collected, stored and analyzed.
The rapid advancement of technology, particularly the Internet of Things (IoT) and interconnected devices, has led to the widespread practice of datafication. This process involves the collection and generation of large amounts of data in real time. For example, sensors on our mobile devices track our movements, social networks collect our interactions, and purchase tracking systems record our transactions.
The datafication process involves collecting data from different sources such as sensors, social media platforms, transaction logs, and other relevant channels. This data is then stored, processed and analyzed using advanced algorithms and tools to extract valuable insights. By implementing the datafication process, organizations can gain a comprehensive understanding of their operations, customers, and the overall market environment.
According to the original article on Big Data (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 much broader activity: taking all aspects of life and converting them into data format […] Once we date things, we can transform their purpose and convert information into new forms of value.”
In reality, datafication is more about the process of collecting, storing and managing customer data from real-world actions, while digitization is the process of converting selected media into a computer-compatible format.
Impact of datafication on companies
Datafication has important implications in many areas, including a wide range of industries and contexts. Organizations that embrace datafication can use data as levers for innovation, process optimization, and personalization of user experiences.
Using evidence-based insights allows businesses to make informed decisions that lead to greater efficiencies, lower costs, and improved levels of customer satisfaction. For example, e-commerce platforms apply datafication to recommend products to a customer based on their recent purchases and preferences, resulting in increased sales and customer satisfaction.
This also facilitates the use of predictive analytics, allowing companies to predict future trends, customer needs and market demand in advance. As such, it can be predicted that this will allow companies to react and change their strategy to remain competitive in the market.
Furthermore, datafication facilitates the development of data-driven products and services that generate value, leading to differentiation in the market. For example, video streaming service providers use data to offer personalized content recommendations that keep viewers interested in watching their channel.
In today’s market and society, there can be no room to ignore data collection that has become an intrinsic element of the datafication phenomenon, regardless of a particular product or service.
We are talking about creating a culture of analytics that encompasses all aspects of business management in this digital age. Both artificial intelligence and machine learning are important for datafication, but the first step is to collect information from different places and then AI/ML formulas will analyze the data to obtain useful information for decision making.
If you don’t know the purpose of your business, you won’t be able to achieve anything, even with the best data. This is because data can only matter within a situation that makes sense.
Examples of datafication
Nowadays, there are all kinds of ways data is collected whenever we use technology in our daily lives. It can store virtually anything: numbers, text, photos, maps, audio files, your phone information, IP addresses, click paths, how long you spend on a site, logins and passwords, the path you took to get there.
Some of the large industries that use this datafication are:
- Social media platforms (like Facebook, LinkedIn, Instagram, TikTok). They want you to bring all your real-world relationships and interactions to their platforms, stay active, and share as much personal data as you can by constantly updating profiles, reacting to things, and showing your preferences. Of course, they use this data primarily to target ads.
- Video streaming sites (think HBO, YouTube, Netflix). Supplement old-school TV with on-demand movies and shows. They try to generate a certain addiction so that the user consumes content.
- Online banking offers a secure way to manage money on the Internet. Banks use your data to determine your creditworthiness and recommend the best risk-reward ratio when lending money. Essentially, the data helps banks constantly update their analytics rather than relying on occasional samples.
Public information and internal data can reveal a person’s background and work performance and companies can use this to verify someone’s history or evaluate productivity. It can even replace personality tests when considering promotions.
Any company that uses email, websites, marketing logistics, or production tracking is collecting data. This data must be continually expanded and updated to maximize benefits. Here’s the crazy part: in 2018, business data surpassed personal data! The amount of business data that exists is enormous
In theory, datafication allows small businesses to locate, manipulate and process trend data with great ease. This method allows a company to make sound decisions regarding pricing, products, and advertising.
Facilitation of Digital Transformation
It is true that, in this case, the technological movement of the current era is the “digital transformation”, as is evident today. However, this is not just a fad; It is an integral component of any company seeking to adapt its operations to current times, as well as remain competitive and functional in our times.
In fact, there is no way to use modern technology without relevant data.
In this case, datafication implies that companies will understand themselves, who they are or their activities. This leads companies to be more efficient in the use of resources, which translates into greater overall company efficiency.
Did it ever occur to you why you started noticing those same ads for products you bought at the same time? That’s datafication in action. We generate information every time we log into our Facebook account, withdraw cash from the ATM, swipe our credit cards, among others. Unlike digits, data representation gives marketers the opportunity to forecast sales and monitor market trends, among other things.
So what is the risk? And how can companies safeguard datafication? Whenever a new technology appears, security and privacy are always at the top of the agenda; this applies with the same force to datafication. Needless to say, inappropriate data would be used for frauds such as scams or the spread of misinformation.
“This is also a good thing as there are more data security measures in any company, regardless of its size,” Moorthy said.
Datafication makes it easier to manage your data and turns many tasks into automatic actions that save a lot of time. By implementing datafication techniques and tools, you will have more time available to focus on other critical business tasks. This is the data-driven world we live in now.
Achieve datafication and challenges that come along
Turning everything into data (total datafication) requires work in different areas. Companies need the right hardware and software to store and study all that information. Leaders should excite the team to use data to make decisions and, in addition to internal data, such as sales records and website statistics, also incorporate external data, such as social media conversations and industry reports.
The key is to bring all of these data sources together and then clean the data to remove errors and make it ready for analysis. Advanced techniques such as machine learning must then be used to interpret the content. Key findings and insights should be clearly shared with stakeholders, working to improve data quality over time. Data scientists and entrepreneurs must come together to solve data problems and make solutions a reality. Investing in training to improve data skills across the company is key.
Building a truly data-driven culture is difficult. It takes full acceptance everywhere, enough money and staff, and a willingness to question old ways of doing things. Datafication has many benefits, but also important challenges that organizations must face to use it well.
Some of these challenges are:
- One of the biggest problems is protecting the privacy and security of all the information that is collected. With so much data stored, there is a greater risk of it falling into the wrong hands or being hacked and stolen.
- Data privacy regulations like the GDPR in Europe have really strict requirements for managing and protecting personal information. It’s hard to keep up with these rules as they change. Having accurate and useful data is essential. Not having complete, current and accurate information can lead to poor decisions and operational problems.
- Bringing data together from different sources and systems can be complicated, and when data is siled and can’t communicate with each other, it’s difficult to gain valuable insights. Data management and analysis require specialized skills. Not having enough trained professionals in this area makes it difficult to properly implement data plans. Misusing data to manipulate or make biased decisions raises ethical questions. Organizations must address these issues and promote ethical data practices.
- Implementing all the sophisticated technology and data systems can cost a lot. And you have to keep everything up to date, which requires a lot of people and resources. Additionally, sometimes companies don’t want to change the way they work to use more data. Employees may need special training and help to really take advantage of all the information available.
- As data continues to increase, companies need to figure out how to manage and store these enormous amounts of stuff, which will likely mean spending more on infrastructure and technology. Companies need good plans to deal with these risks. Addressing these challenges is key to getting the most out of data while avoiding problems, and careful planning is critical to overcoming these obstacles and using data effectively for business decisions.
- Using more data provides organizations with new opportunities, but also creates major challenges that must be solved to truly benefit. The collection and use of more data raises privacy and security concerns, so companies must ensure they use data ethically, protect it appropriately, and follow relevant laws and regulations.
- The importance of having compatible and quality data for datafication to work well cannot be ignored. Organizations should make it a top priority to ensure that the data they obtain is accurate, stable and reliable. Furthermore, combining data from different places requires frameworks that connect all without problems. This helps avoid ending up with separate islands of data and allows to gain useful insights.
- Another problem is having enough trained people who can handle complex data analysis. To make the best decisions and drive innovation, companies must invest in improving the data skills of their staff and offer good training programs.
Despite the benefits of datafication, there have also been big debates about how corporations or regions use this practice in certain areas to discriminate against people, especially those from low-income or minority groups. This concern centers on public access to data and constant monitoring of people’s activities.
- Global data collection: Data surveillance is not limited to one region or one language. In other words, platform owners can now store information about everyone on the planet who has access to the Internet. This is especially important in times of increasing cybercrime, which is often more efficient at attacking smaller platforms.
- Public access to data: The more data we collect, the more precise information about an individual we can discover. This is already used in law, journalism, and some businesses to perform a background check on a specific person, linking them to a specific place (and time), actions, and even ideology. Unfortunately, a hacker or spammer can analyze the same data to carry out identity theft or other forms of cybercrime.
- Data as a commodity: Platforms are a new type of multilateral datafication market. The currency is data. To produce it, tech giants bring together users of platforms that create data, buyers of data (such as advertisers and data brokers) willing to exchange it for real money, and service providers who profit from the publication, sale and internal use of the data. Unlike typical assets, data sets can not only be stolen and resold, but can also be used to commit cybercrimes against users whose records are collected in a compromised set.
- Constant monitoring: Massive data sets are stored (and updated daily) in multi-platform server rooms owned by tech giants, imposing datafication on their users. The collected data is then used for paid advertising personalization within the giants’ apps/platforms, and the level of interference is usually regulated by law. Unfortunately, in some regions the government has adopted similar monitoring methods. Elsewhere, the law attempts to protect individual autonomy from the dangers of continuous data collection.
The one thing that really sticks out in data science these days is how everything is becoming datafied. Technology has made some changes that have turned our normal actions and behavior into actual data.
Not only business processes are changing but also new ways of doing innovative stuff and how companies interact. This gives companies new dimensions that can make them more competitive compared to other brands, and also help make our lives better.
Datafication has transformed how companies operate, innovate and interact with customers today. Companies can now enter into new areas to grow their business using data. This puts them in a position to perform better than others by making valuable contributions to society. But companies also have to deal with different issues and implications of datafication to get the most benefits. To meet these challenges, they need to resolve privacy concerns, data quality problems and skill gaps.
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