Understanding big data and its importance

Complex or massive data sets which are quite impractical to be managed using the traditional database system and software tools are referred to as big data. Big data is utilized by organizations in one or another way. It is the technology which possibly realizes big data’s value. It is the voluminous amount of both multi-structured as well unstructured data. Unstructured data is the one that is not organized and thereby cannot be interpreted by using a software or traditional database (Sawant & Shah 2013). An example of the unstructured data is the data being used by Twitter, Facebook and other social media in the form of posts (Choi et al. 2006). It is very important for organizations to have knowledge about big data. There is the need to adopt new techniques and tools in order to process this massive amount of data. New programs must be developed so that this data could be processed efficiently. It is critical for organizations to analyze big data to forecast trends and analyse the behaviour of people who are generating it, i.e. users of the social media (Michael & Miller 2013).

Advantages of Big Data

Due to the loopholes present in traditional data systems (used by an organization), big data was developed. Following table1 marks the difference between traditional data and big data:

Factors Big Data Traditional Data
Data Architecture Distributed Architecture Centralized Architecture
Type of Data Semi-structured/Un-structured Data Structured Data
Volume of Data Consists of (250 – 260 ) bytes of data Consists of 240 bytes of data
Schema No schema Based on Fixed Schema
Data Relationship Complex Relationship between data Known Relationship between the data

Product re-development

Big data helps an organization or the company to understand the demands of the customer. Unstructured data can be analyzed to analyse customer’s emotions and differentiate them on the basis of demography and  location (Danah Boyd & Crawford 2012).

Data security

Tools and techniques help to analyze and eliminate internal threats  associated with the data. Through the use of Big Data techniques it is possible to detect the information vulnerable to threats (Labrinidis & Jagadish 2012)..

Reduce communication gap

Big data helps to carry out conversations with customers in real time. Big data attempts to understand their needs and utilises it well so that they do not face any difficulty while doing online purchases of goods and services. This will reduce the communication gap between the organization and customer (Chen & Zhang 2014).

Real-time website customization

With proper analysis of the data in real time it would be easy to personalise the website content for different customers such as in case of websites like LinkedIn, Flipkart, etc. (Raghupathi & Raghupathi 2014).

Use of Big Data across different sectors

Media & communications

In media and communications it is used to analyze the personal and behavioral data of the customers in order to create customer’s profile. It creates the content for different customers, recommend that content on the basis of demand and measure its performance (Tankard 2012). In media and communication industry big data is used by the companies like Spotify, Amazon prime, etc.  Spotify use big data analytics in analyzing the  data and  give recommendation regarding the music to its customers individually. Similarly, Amazon prime makes use of big data in music and videos based data in order to provide an everlasting experience to its customers (LaValle et al. 2011). According to (Gartner 2013), 44% of the media and communication organizations have invested in Big data.

Banking

In banking data is used to manage large financial data. SEC (Securities Exchange Commission) uses big data in order to monitor the market and finance related data  of the bank and Network analytics in order to track illegal activities in the finance. Big data is also used in the trading sector for trade analytics and decision support analytics. According to (Gartner 2013), 39% of banking sector have invested in Big data.

Healthcare

Big data is used in the healthcare sector in order to manage the large amount of data relate to the patients, doctors and the other staff members. It helps to eliminate the failures like errors, invalid or inappropriate data, any system fault etc. that comes while utilizing the system  and provides benefits like managing customer, staff and doctors information related to healthcare  (Bughin et al. 2010).

According to (Gartner 2013), 43% of the healthcare industries have invested in Big data. Statistics of Big Data used in different sectors are as follows:

Use of big data in different sectors

Use of big data in different sectors

In India, big data is mainly used by the IT sector, healthcare sector, Business Intelligence and Banks. In the IT sector it is implemented by different companies such as Wipro, Infosys, TCS, etc. In the Healthcare sector bit is used for managing patient’s and staff data, reports, medical data, etc. Big data is integrated with the Business Intelligence to gain full ROI (Return on Investment) and the Banking sector take most of the leverage for different purposes. It helps to know about the details of each and every customer through profiling, in deriving the revenues, reducing the risks by detecting the frauds through predictive analysis, winning loyalty of customers through retention offers and feedback analysis. (Davenport, Thomas H; Barth & Bean 2012).

References

  • Bughin, J., Chui, M. & Manyika, J., 2010. Clouds, data, and smart assets: Ten tech-enabled business trends to watch, Available at: http://www.itglobal-services.de/files/100810_McK_Clouds_big_data_and smart assets.pdf.
  • Davenport, Thomas H; Barth, P. & Bean, R., 2012. How Big Data Is Different. MIT Sloan Management Review, 54(1), pp.43–46.
  • Katal, A., Wazid, M. & Goudar, R.H., 2013. Issues, challenges, tools and Good practices,
  • Labrinidis, A. & Jagadish, H. V, 2012. Challenges and Opportunities with Big Data. Proc. VLDB Endow., 5(12), pp.2032–2033. Available at: http://dx.doi.org/10.14778/2367502.2367572.
  • LaValle, S. et al., 2011. Data, Analytics and the Path From Insights to Value. MIT Sloan Management, 52(2), pp.21–32.
  • Michael, K. & Miller, K.W., 2013. B. Data: New Opportunities and New Challenges [Guest editors’ introduction]. Computer, 46(6), pp.22–24.
  • Raghupathi, W. & Raghupathi, V., 2014. Data analytics in healthcare: promise and potential. Health Information Science and Systems, 2, p.3. Available at: http://www.hissjournal.com/content/2/1/3.
  • Sawant, N. & Shah, H., 2013. Big Data Introduction. In Data Application Architecture Q {&} A: A Problem-Solution Approach. Berkeley, CA: Apress, pp. 1–8. Available at: http://dx.doi.org/10.1007/978-1-4302-6293-0_1.
Deepali Aggarwal

Deepali Aggarwal

Research analyst at Project Guru
Deepali Aggarwal, holds Master’s degree in Computer Science and Engineering. As a student she has worked on technology projects based on Java and PHP. She started her academic career as a Teacher Assistant during her masters, with experience in software development, database administration and management. She exudes command over training and mentoring engineering students. Her areas of interest are data-management, networking and digital image processing. Apart from her interests in academics she also loves dancing, singing and cooking.
Deepali Aggarwal

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