{"id":3802,"date":"2020-06-29T12:00:00","date_gmt":"2020-06-29T10:00:00","guid":{"rendered":"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/uncategorized\/what-is-big-data\/"},"modified":"2023-07-18T15:40:14","modified_gmt":"2023-07-18T13:40:14","slug":"what-is-big-data","status":"publish","type":"post","link":"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/what-is-big-data\/","title":{"rendered":"What Is Big Data?"},"content":{"rendered":"

Big data is everywhere\u2014but what exactly is it, and how is it different from \u201cordinary\u201d data?<\/strong><\/p>\n

Every action you take leaves a trail of data\u2014both on and offline. In fact, humans have been leaving their informational footprint since the beginning of time. And, with the rapid digitalisation of the last thirty years, it is now easier than ever to effectively capture all kinds of data.<\/p>\n

So where does big data come into it? Well, if you consider the online world alone, there are over five billion active internet users worldwide<\/a>\u2014each leaving their own complex data trail. That\u2019s exactly what the term \u201cbig data\u201d refers to: the huge volumes of unstructured data that are generated every minute of every day. Unlike \u201csmall\u201d data, big data comes with some major challenges\u2014but, when leveraged correctly, it provides untold value.<\/p>\n

If the big data buzzword has got your head in a spin, you\u2019ve come to the right place. In this guide, we\u2019ll explain everything you need to know about big data, including where it comes from and why it\u2019s so important.<\/p>\n

Interested in picking up some data analytics skills? Try this free data short course<\/strong><\/a> out to see if you like it.<\/p>\n

We\u2019ll answer the following questions:<\/p>\n

    \n
  1. What is big data?<\/a><\/li>\n
  2. Where does big data come from?<\/a><\/li>\n
  3. What are the different types of big data?<\/a><\/li>\n
  4. What is the value of big data?<\/a><\/li>\n
  5. What are some examples of big data?<\/a><\/li>\n<\/ol>\n

    At the end of this post, you\u2019ll find a summary of all the key takeaways and links to further reading. So, what is big data? Let\u2019s take a look.<\/p>\n

    1. What is big data?<\/h2>\n

    John Mashey, a computer scientist, is thought to have coined the term \u201cbig data\u201d in the early 90s, but the concept really started to gain traction in the early 2000s.<\/p>\n

    There are several reasons for this, such as the advent of the Internet of Things<\/a>, our penchant for smart, connected devices, and the ubiquity of social media. As it stands, we\u2019re generating huge volumes of data all the time\u2014and fast! This is essentially what big data is: large volumes of data that are exceptionally fast and, usually, too complex to be processed using traditional methods.<\/p>\n

    But just how big is \u201cbig data\u201d? And what exactly do we mean when we say \u201cfast\u201d? In 2001, Douglas Laney, a data expert at leading research firm Gartner, came up with the three Vs of big data: Volume<\/strong>, Velocity<\/strong>, and Variety<\/strong>. These characteristics are used to define big data and distinguish it from \u201cordinary\u201d data.<\/p>\n

    The three Vs of big data<\/h3>\n

    Let’s go through each one briefly:<\/p>\n

    Volume<\/strong><\/p>\n

    The first thing you should know about big data is that it is huge\u2014and growing constantly.<\/p>\n

    At the beginning of 2020, the digital universe was estimated to consist of a whopping 44 zettabytes of data<\/a>. To put that into perspective, one zettabyte is roughly equal to a trillion gigabytes. And, by 2025, it\u2019s thought that approximately 463 exabytes of data will be created every 24 hours worldwide. An exabyte is equivalent to one billion gigabytes.<\/p>\n

    So, when we talk about big data, we\u2019re dealing with massive, almost incomprehensible volumes of the stuff.<\/p>\n

    Velocity<\/strong><\/p>\n

    Our ever-connected world means that companies are literally inundated with data.<\/p>\n

    Every single person who has a connected device, uses the internet, including social media is generating their own stream of data. From a business perspective, it\u2019s in companies\u2019 best interest to capture and utilize this data.<\/p>\n

    So, the velocity of big data simply refers to the sheer speed at which data is generated and gathered.<\/p>\n

    Variety<\/strong><\/p>\n

    Not only is big data huge in volume and mind-bogglingly fast, it also comes from myriad different sources.<\/p>\n

    This is, in part, what makes big data so complex. It comes in many different forms\u2014from video, text, and image data to audio data, real-time data, and beyond\u2014and therefore requires different types of processing and analysis.<\/p>\n

    We\u2019ll explore where big data comes from and the different types of big data in sections two and three.<\/p>\n

    So what\u2019s the difference between big data and \u201csmall\u201d or \u201cnormal\u201d data?<\/h3>\n

    The best way to distinguish between data and big data is to consider the challenges that big data presents.<\/p>\n

    \u201cOrdinary\u201d data is essentially structured data which fits neatly in a database, and can be gathered and analyzed using traditional tools and software (such as Excel). By contrast, big data is so huge in volume, so varied and unstructured in format, and so fast in its accumulation that traditional tools are simply not sufficient when it comes to processing and understanding the data.<\/p>\n

    In that respect, the term \u201cbig data\u201d refers not only to the three Vs; it also encompasses the complex tools and techniques that are needed to draw meaning from the data.<\/p>\n

    2. Where does big data come from?<\/h2>\n

    Now we have a working definition of big data, let\u2019s explore where this voluminous and complex data comes from. There are three main sources of big data:<\/p>\n