bigdatascalability.com big data: Why It Is Important

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What is big data?

Big data is a collection of structured, semistructured, and unstructured data that is gathered by organizations and can be mined for information for use in advanced analytics applications like machine learning and predictive modeling.

Together with tools that support big data analytics uses, systems that process and store big data have become a common part of data management architectures in businesses. The three V’s are frequently used to describe big data:

the enormous amount of data being produced, collected, and processed across a wide range of environments; the diversity of data types frequently stored in big data systems; and the speed at which much of this data is being done all of these things.
Doug Laney, a consultant at Meta Group Inc. at the time, first identified these traits in 2001; Gartner further popularized them after acquiring Meta Group in 2005. Veracity, value, and variability are a few additional V’s that have been added to various definitions of big data more recently.

Big data deployments frequently involve terabytes, petabytes, and even exabytes of data that have been created and gathered over time, even though big data does not equal any specific amount of data.

Why is big data important?

Big data is used by businesses to enhance operations, deliver better customer service, develop individualized marketing campaigns, and carry out other tasks that can ultimately boost sales and profits. Because they can act more quickly and with greater knowledge, businesses that use it effectively may have a competitive advantage over those that don’t.

Big data, for instance, offers insightful information about customers that businesses can use to improve their marketing, advertising, and promotions and boost customer engagement and conversion rates. Businesses can become more responsive to customer wants and needs by analyzing historical and real-time data to assess the changing preferences of consumers or corporate buyers.

Big data is also used by doctors to assist in the diagnosis of illnesses and medical conditions in patients as well as by medical researchers to find disease signs and risk factors. Additionally, healthcare organizations and governmental organizations receive up-to-date information on infectious disease threats or outbreaks from a combination of data from electronic health records, social media platforms, the web, and other sources.

Here are some additional instances of how businesses use big data:

Big data is used by utilities to monitor electrical grids and by oil and gas companies to locate potential drilling sites and track pipeline activity in the energy sector.
Big data systems are used by financial services companies for risk management and in-the-moment market data analysis.
Big data is used by manufacturers and transportation firms to manage their supply chains and improve delivery routes.
Emergency response, crime prevention, and smart city initiatives are additional government uses.

Examples of big data

Big data is derived from a variety of sources, including customer databases, transaction processing systems, documents, emails, medical records, clickstream logs on the internet, mobile apps, and social networks. It also includes data that is produced by machines, like network and server log files, as well as data from sensors on industrial machinery, internet of things devices, and manufacturing machines.

Big data environments frequently include external data on consumers, financial markets, weather and traffic conditions, geographic information, scientific research, and more in addition to data from internal systems. Big data applications frequently use streaming data that is processed and gathered continuously, including images, videos, and audio files.

characteristics of big data

Following up on the original three Vs, here are some details on some additional ones that are currently frequently linked to big data:

Veracity is a term used to describe how accurate and reliable a set of data is. Data quality problems resulting from the use of raw data from various sources can be challenging to identify. Bad data causes analysis errors that can reduce the value of business analytics initiatives if they aren’t fixed through data cleansing procedures. Teams working on data management and analytics must also make sure they have access to enough reliable data to generate reliable results.
The list of qualities of big data also includes some data scientists and consultants. Not all of the data that is gathered has actual commercial value or advantages. Therefore, before incorporating data into big data analytics projects, organizations must ensure that it is related to pertinent business issues.
Big data sets can have multiple meanings or be formatted differently in different data sources, which adds another layer of complexity to big data management and analytics.