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What exactly is big data?

What exactly is big data?

Big data is a collection of organized, semi structure and unstructured data that may be mined for information and used in machine learning, predictive modeling, and other advanced analytics initiatives.

Big data processing and storage systems, as well as technologies that facilitate big data analytics, have become a standard component of data management architectures in enterprises. The three V's are frequently used to describe big data.

the high volume of data generated, collected, and processed in many environments; the great diversity of data types commonly stored in big data systems; and the speed with which most of the data is generated, gathered, and processed.

Doug Laney, then an analyst at consulting firm Meta Group Inc., identified these traits in 2001, and Gartner popularised them after acquiring Meta Group in 2005. Several other V's, such as veracity, value, and variability, have subsequently been added to various formulations of big data.

Big data deployments frequently contain terabytes, petabytes, and even exabytes of data created and collected over time, despite the fact that the term "big data" does not refer to a specific volume of data.

What is the significance of big data?

Companies use big data in their systems to enhance operations, provide better customer service, generate targeted marketing campaigns, and take other activities that can raise revenue and profitability in the long run. Businesses who properly use it have a potential competitive advantage over those that don't since they can make more informed and faster business decisions.

Big data, for example, provides firms with important customer insights that they can utilise to improve their marketing, advertising, and promotions in order to boost customer engagement and conversion rates. Consumer and corporate purchasers' developing preferences can be assessed using both historical and real-time data, allowing organisations to become more responsive to their wants and needs.

Medical researchers and doctors use big data to uncover disease indicators and risk factors, as well as to diagnose illnesses and medical problems in patients. Furthermore, data from electronic health records, social media sites, the internet, and other sources is combined to provide healthcare organisations and government agencies with up-to-date information on infectious disease threats and outbreaks.

Collection techniques and restrictions for big data

As the collecting and usage of big data has grown, so has the risk of data mishandling. The European Union approved the General Data Protection Regulation (GDPR), a data privacy law that went into effect in May 2018, in response to public outcry about data breaches and other personal privacy infractions. GDPR restricts the types of data that businesses can collect and needs consumers' opt-in consent or compliance with other specific reasons for acquiring personal data. It also includes a right-to-be-forgotten provision, which allows EU citizens to request that their data be deleted.

The keys to a successful big data strategy

Developing a big data strategy in an organisation necessitates a grasp of business goals and the data currently available to use, as well as an assessment of the need for additional data to assist fulfil the goals. The following are the next steps to take:

Prioritizing anticipated use cases and applications; identifying new systems and technologies that are required; developing a deployment roadmap; and assessing internal skills to determine whether retraining or hiring is necessary.

That's all I have for now. Let me know in the comments if you like it or not, and what else I should write about.

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