Big Data describes a large volume of data (both structured and unstructured) and is so large and complex that it’s critical to process using traditional methods. This data inundates a business on a daily basis. Big Data is a collection of data that is huge in volume and is increasing exponentially over time.
Doug Laney, an analyst at Meta Group Inc in 2001, identified three different characteristics of Big Data, which is stated as 3Vs: the large volume of data, the wide variety of data types stored in big data systems, and the velocity at which the data is generated, collected and processed. In recent times, several Vs have also been incorporated into different descriptions of big data, including veracity, value, and variability.
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Example of Big Data:
- Facebook generates over 500 + terabytes of big data in the form of photo and video uploads, message exchanges, putting comments, etc.
- New York Stock Exchange (NYSE) generates about one terabyte of new trade data on a daily basis.
- Scientific research repositories
- Customer databases
- Medical records
- Internet clickstream logs
Big data also includes a large variety of data types such as structured data in databases and data warehouses, semistructured data in web server logos or streaming data from sensors, unstructured data such as text and document files, or startup funding data. Big data applications also include multiple data sources that may not be integrated. The big data market is also growing through diverse end-use industries. Businesses can manipulate a huge amount of data by getting such data.
Corporations with the help of these answers gather efficiency and become exceptional in coping with an outsized quantity of uncooked data, in the end, thus increasing the big data analytics market in retail becoming valued at $4.43 billion in 2019, and is calculable to achieve $17.85 billion through 2027, registering a CAGR of 20.4% from 2020.
The big data industry report encompasses huge market players such as IBM, HP Enterprise, Teradata, Amazon, Microsoft, Google, SAP, EMC, and other famous brands.
Utilizing big data enables companies to become customer-centric. Historical and real-time data can be used to assess the evolving preferences of consumers, consequently enabling businesses to update and improve their marketing strategies and become more responsive to customer desires and needs.
Importance of Processing Big Data:
- With the help of this data, you can analyze and find answers to questions such as cost reductions, time reductions, smart decision making, new product development.
- Recalculating risk portfolios, businesses can early calculate risk to the product/services if any
- Generating coupons on the basis of customer’s buying habits
- Analyzing the cause of certain issues, failures, and defects in real-time
- Detecting fraudulent behavior before it affects the business
Marketing Analysis: Big data can also be used to make the promotion of new products, services, campaigns, initiatives that can be more informed and innovative.
Competitor Analysis: Big data also helps in the examination of user behavior metrics and the observation of real-time customer engagement in order to compare one company’s products, services, and brand authority with those of its competition.
Social media targetting: This data can also be used to help identify target audiences for marketing campaigns by observing the activity surrounding specific topics across various platforms.
The amount of unstructured data must be accounted for before it is used in big data analytics applications in an organization. IT and analytics teams also need to ensure that they have enough accurate data available to produce valid results.
Companies can make complete use of external intelligence while formulating policies:
With the help of big data accessed from search engines and social networking sites like Facebook, Twitter, Instagram, companies can take decisions and manage their policies, etc.
By analyzing big data, you can get feedback about your product or service through sentiment analysis. If you want to monitor and improve the online presence of your business, then, big data tools can help in all this. This way you can control your online reputation.
The faster speed of Hadoop and other such tools and in-memory analytics can be very useful in identifying fresh sources of data that can provide utility to businesses analyzing data immediately and make quick decisions based on the learnings.
Big data analysis can also help change several business operations. This includes the ability to match customer expectations, changing the company’s product line, and ensuring that the marketing campaigns are strategized as per the requirement.
Big Data can be used for creating a landing zone or staging area for new data before identifying what data should be moved to the data warehouse. Also, this kind of integration of Big Data technologies and data warehouse helps an organization to offload less frequently accessed data. The use of big data allows businesses to observe various customer-related patterns and trends. Observing customer behavior is important to trigger loyalty.
It is important to note that organizations employ practices such as data cleansing and confirm that data relates to relevant business issues before they use it in a big data analytics project.