Big Data: A New Era Of Digital Communication

This article explains the relevance of Big data, well-known examples and their technologies, and the connection with Data mining. One hundred seventy-five zettabytes a number with 21 zeros. According to a forecast, this is how high the global data volume will be in 2025.

This number is unimaginable and difficult to comprehend. However, it impressively shows the importance of Big data and will continue to do so in the future: the mass collection, processing, and analysis of data is an integral part of digitization. It makes new inventions and business models possible. Big data includes collecting, using, evaluating, exploiting, and digital marketing data. Because the issue has multiple dimensions, Gartner analyst Doug Laney classified it in the 3V model in 2001 :

  • Data Volume: Size of the data volume
  • Data Velocity: Speed โ€‹โ€‹of data generation and processing
  • Data Variety: Variety of data types, e.g., B. photos, videos, sensor data, etc.

The newer 6V model replaced Doug Laney’s 3V model over the years. This consists of the additional dimensions:

  • Data Validity: Ensuring data quality
  • Data Veracity: the credibility of the data
  • Data (Business) Value: Value of the data (e.g., for business purposes)

There are also the 9V and 10V models, which still contain these dimensions:

  • Data Viability: Relevance and usefulness of the data collected
  • Data Visibility: Visibility, especially in terms of business value
  • Data volatility: transience, i.e., the deletion of data
  • Data vulnerability: vulnerability, which means the security of the data

Examples Of Practical Applications Of Big Data

Collecting data has never been easier than it is today. Various sources are often used:

  • Websites & online shops
  • Software & Apps
  • voice assistants
  • Social networks
  • search engines
  • Automobile & Traffic
  • wearables, e.g., B. Fitness Trackers & Smartwatches
  • Robots and IoT devices

The list goes on for pages; new sources and technologies are added every day, resulting in mass data.  An excellent example of this would be innovative home technologies, i.e., sensor data from smart thermostats, images from surveillance cameras, requests to language assistants, control data for the heating system, and much more. In an intelligent home alone, there is a lot of unstructured data that systems collect, process, and exchange between devices.

What Is Data Mining?

Data mining means “data mining.” Data mining is, therefore, an essential part of Big Data. Companies are collecting and aggregating gigantic volumes of data today, even more so in the future. For companies to derive value from data masses as described in the 3V, 6V, or 10V model, the data must be analyzed and processed so that it has value. This includes, for example, the “Knowledge Discovery in Databases (KDD)” process, which among other things, selects, cleans, reduces, and analyzes data sets. The analysis step is data mining. However, the entire KDD process is often referred to as data mining. The terms, therefore, flow into one another in linguistic usage.

Big Data Technologies

Many classic technologies, such as relational database systems, are not suitable for processing big data. Instead, companies that want to work with big data rely on server clusters and robust cloud solutions. Only such a network of hundreds or even thousands of server units can meet the high requirements. This includes, for example, the fast processing of gigantic amounts of data, preferably in real-time.

Well-known representatives of such big data technologies include:

  • Apache Hadoop, Spark, and Hive
  • Apache Impala

Without Mass Data, There Is No Digitization

The collection and processing of mass data is not a bad thing per se. Nothing works without data โ€“ especially with complex digital solutions. They serve z. For example, autonomously driving cars receive all the information they need to go themselves. In the area of e-commerce and sales, the data is used to make individual and suitable offers to customers along their customer journey. And customer service can use precise user data to respond to their specific inquiries and work out optimal solutions.

Also Read: Opportunities Through Big Data

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