User data sales account for a significant share of the modern digital economy. User data are used for anything related to sales, marketing, product development, user experience (UX) and more. Any online activity leaves behind a lot of data that provide valuable information about who we are, what we like, how we live and more. All kinds of businesses seek to access as much data as possible, either to use or sell. The aggregation of user data helps predict what demographics might represent you, what other products and services you might be interested in and more.
Businesses collecting user data can use them for a variety of purposes. For example, they can use user data to better tailor products or services to target consumers. They can also use them for making advertising more accurate. In addition, user data can be included in a statistical or any other commercially valuable analysis. User data also generate value by helping businesses better understand customers and prospects, predict trends which might affect business and make more money.
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The progressing commercialization of user data has raised concerns over data privacy. As more data privacy regulations have been passed, businesses have become worried how to comply with them, and how much they would cost. A 2019 Gartner survey of senior executives found that the acceleration of data privacy regulation and related regulatory burden is the top emerging risk faced by companies globally. 64% of senior executives consider it as a key risk, which could potentially bring large fines and brand damage.
In part to meet more stringent privacy laws, businesses have been collaborating with computer scientists to develop solutions that would strengthen data protection and would not hamper innovation and operation efficiency at the same time. Tech companies such as Google, Facebook, Amazon and Apple have introduced changes that give users more control over how they are being tracked, and how their data are used. Even though businesses seek to self-regulate, data privacy laws are needed “because companies have to be prodded to adopt them”, observes Michael Kearns, Professor of Computer and Information Science at Penn Engineering and the Director of the Warren Center for Network and Data Sciences.
Businesses collect user data in a variety of ways from many sources. There are collection methods which are highly technical, while others are more deductive. These collection methods and sources aim to capture data ranging from demographic data to behavioural data. “Customer data can be collected in three ways: by directly asking customers, by indirectly tracking customers, and by appending other sources of customer data to your own. A robust business strategy needs all three,” explains Liam Hanham, Data Science Manager at Workday, a system software provider.
Capturing a large amount of data causes the problem of how to sort through and analyze them. People cannot process all data by themselves. They need to rely on computers which can process data more quickly and efficiently and work 24/7/365 without taking a break. As machine learning algorithms and other forms of artificial intelligence (AI) continue to improve, data analytics is getting better at turning data into actionable insights. Some AI programmes can flag anomalies or offer recommendations to decision-makers based on contextualized data.
The value of user data could be estimated in different ways. One of them is “to calculate the amount in cash equivalent after an acquisition”. For example, Microsoft acquired LinkedIn for $26.2 billion. At that time, LinkedIn had over 400 million users. Based on this information, “a single user data point” could be estimated at $65 per user. The monetary value of user information depends on the type of industry and, consequently, the type of data companies gather. The wider and richer the scope of big data that companies possess, the more revenue they can generate.
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