Big Data vs. Conventional Data

-

 



New, innovative, and more efficient technologies have enabled the management of big data for analytics to extract meaningful insights for decision-making (Minelli et al., 2013), emphasize the many differences between big data and conventional data. 

Edd Dumbill as cited in Minelli et al. (2013), defines big data as “data that becomes large enough that it cannot be processed using conventional methods,” and Gartner’s definition in 2001 reads “Big data is data that contains greater variety arriving in increasing volumes and with ever-higher velocity” (Treehouse, 2022), featuring big data’s three V’s: variety, volume, and velocity (Minelli et al., 2013). Kumar et al. (2021) explains that with big data, it is possible to store large volumes of data, but not in a conventional data setup. Conventional data reflects a centralized database architecture, and big data is found in distributed databases. The data sources are many when dealing with big data, but severely limited with conventional data. Data integration of the conventional type is an easy process, but it is more complex when dealing with big data as huge amounts are generated quickly and often (Kumar et al., 2021). 

Other differences between big data and conventional data include flexibility as it refers to schema. Big data uses a dynamic schema, whereas a fixed schema is associated with conventional data. Also, big data enables real-time analytics, which is not possible when dealing with conventional data as it relies mostly on historical records (Treehouse, 2022).

Big data’s ability to enhance data analytics’ approaches allows for data exploration and development of meaningful questions as data analysis is taking place. With conventional data, reports are structured to answer pre-established questions that offer little insight (Minelli at al., 2013; Treehouse, 2022). 


There are several public sites offering free access to large datasets. Kaggle, for example, requires users to register as it is more of a community rather than a search engine. Not only are there countless dataset topics available for download, but the site holds machine learning (ML) competitions and offers educational tools for learning data analysis and artificial intelligence (AI) (Hillier, 2022). Just like Kaggle, Data.gov offers free access to large datasets but does not require users to register. The federal government compiles data in diverse topics such as crime, climate change, and others, and offers a user-friendly interface that allows users to drill-down based on different attributes, including federal, state, county, and city categories or levels (Hillier, 2022).   





References 

Hillier, W. (2022, January 12). 10 great places to find free datasets for your next project. Career Foundry. https://careerfoundry.com/en/blog/data-analytics/where-to-find-free-datasets/ 

Kumar, K. S. A., Hababa, S. M., Worku, B., Tadele, G. Mengistu, Y. G., & Prasad, A. Y. (2021). Big data characteristics, classification and challenges – A review. Turkish Journal of Computer and Mathematics Education, 12(12), 4236-4243. ProQuest Central. 

Minelli, M., Chambers, M., & Dhiraj, A. (2013). Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses (1st ed.). John Wiley & Sons. 

Treehouse Technology Group. (2022). Big data vs. traditional data: What’s the difference? https://treehousetechgroup.com/big-data-vs-traditional-data-whats-the-difference/

Comments

Post a Comment

Popular posts from this blog

Think Tanks

-
Image
  Lewis (2019) describes think tanks, also known as think factories, idea factories, reflection pools, or policy institutes, as organizations where scholars representing different disciplines assemble to think without any restrictions, conduct research associated with specific issues, ideas, and policies (Aly, 2019; Whittenhauer, n.d.).   Think tanks conduct research, generate ideas, debate issues, and monitor public policy by means of implementing various strategies, which are associated with the business model utilized by the think tank, such as consulting, advocacy, research, and contract efforts (Lewis). There are four types of think tanks as follows: (1) Ideological or advocacy tanks are organizations where problem solving is rooted on an ideological philosophy, (2) Specialist tanks are institutes with a specific focus or theme, such as the environment or foreign policy, (3) Subnational tanks are government-related entities that typically work on specific state policies, ...
Read more

The Rise of Data Scientists

-
Image
  Data science has been used in physics and astronomy, but it is being applied to businesses and finance, manufacturing, and other fields. The demand for data science professionals exploded about two decades ago when businesses began collecting massive amounts of data,  and a shortage of talented individuals, or data scientists, with a specific set of skill sets able to extract the most benefit from the available data (World Data Science, 2022). Data science is a multi-disciplinary field that converges programming, mathematics, statistics, computer science domain expertise, and communication skills, yet seldom organizations are able to find a data scientist that excels at all of them (Bansal, 2021; World Data Science, 2022).  The World Data Science Initiative (2022) reports data science jobs were expected to grow by 364,000 in 2020, but the real number came in at 2,720,000. Data scientists became part of the solution to address the challenges of growing organizational da...
Read more

Big Data Analytics in the Banking Industry

-
Image
  The implementation of big data analytics (BDA) in organizations is a costly and complex undertaking, and given there is no recipe that guarantees a successful implementation and execution, a careful and honest assessment of different aspects of the business must be conducted, from the technology, data collection, the human resources involved in the process, and managerial support (Hajiheydari et al., 2021). Hajiheydari et al. state that approximately 60% to 85% of BDA projects fail not because the technology is bad, but because organizations do not have a clear understanding of how to utilize data analytics to construct achievable business goals,  or how to successfully integrate its practice with day-to-day business operations (Sun et al., 2020). BDA offers financial institutions countless benefits, and provides valuable insights to personalize customer service via the analysis of transactional records and other data, including social media information (Flynn, 2020; Sun et ...
Read more