Big Data And The Bigness Involved


Organizations everywhere, of all genres, hold vast amounts of data about all aspects of their operations. Companies have been utilizing the big data within their systems to step-up operations, provide better customer service, create customized marketing campaigns based on customer preferences, and of course, increase profitability. Businesses that make use of big data at their best have the potential to outperform others. Let us look at some of the basics of Big Data and the technology involved.

Big Data

Big data encompasses the large and diverse sets of information that grow at an exponential rate. However, big data is extremely large such that none of the traditional data management tools can store or process it efficiently. Apart from the volume of data, the method in which organizations utilize data matters. Big data analysis can create insights that lead to better decisions and strategic business moves. Humans are said to produce 2 quintillions of data every day. Social media platforms and airlines are also big contributors to data accumulation. In the prime of the 2000s, Doug Laney, an industry analyst defined three V’s to characterize big data:

Volume: The data inflow is exponentially high in business organizations. Data from numerous sources like business transactions, IoT devices, social media, industrial equipment, videos, etc, contribute to the cause. As it cannot be stored in a physical space, the storage issue was predominant earlier. Today, thanks to emerging technologies like data lakes and Hadoop, the burden is eased.
Velocity: The data speed also matters besides the exponential amount of data inflow. RFID tags, sensors, and smart meters deal with these torrents of data in real-time.
Variety: Data comes in formats like numeric data, text documents, images, videos, emails, audios, financial transactions, etc. Hence, there is no assurance that the data gathered are bound to be the same or fall under a similar category.

Big Data – Types

  1. Structured data
    Data that can be stored, accessed, and processed in the form of a fixed-format is called ‘structured data.’ Various advanced technologies are being invented to extract data-driven decisions from structured data. However, the world is going towards an extent wherein the creation of structured data is ballooning and it has already reached the zettabytes mark.
  2. Unstructured data
    Any data that comes in an unknown form or structure is classified as unstructured data. Processing unstructured data and analyzing them to get data-driven answers is a challenge as they belong to different categories and clubbing them together will only make things worse.
  3. Semi-structured data
    Semi-structured data has a combination of both structured and unstructured data. Web application data is an example of semi-structured data; it has unstructured data such as log files, transaction history files, etc. OLTP systems are built to work with structured data where data is stored in relations.

Ever since technology took over big data analysis, business decisions are majorly based on predictive outcomes. Also, big data is contributing to personalized customer experiences at high-ends. Some of the important business applications within the big data sector are:
Product development– Companies avail big data to anticipate customer demands by building predictive models to understand customer preferences and for providing relevant materials.
Log analytics– Commercial and open-source log analytics collect, process, and analyze massive log data without dumping the data into relational databases
Security compliance– Big data helps in identifying patterns in data that indicate fraud and compiles large volumes of information to quicken regulatory reporting.
Recommendation engines– Big data enables companies to recommend the best option for customers based on their history, thereby customizing uniquely, each time.

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