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What is “big data”?  What are the most important drivers of big data? What could happen if companies focus solely on the quantitative (i.e., “numbers”) aspects of big data?  Explain.


Big Data is no longer just a buzzword; it is a proven phenomenon and not likely to die away soon. A recent IDC report predicts that the digital universe will be 44 times bigger in 2020 than it was in 2009, totaling a staggering 35 zettabytes.

Two factors have combined to make Big Data especially appealing
now. One is that so many potentially valuable data resources have come into existence. These sources include the telemetry generated by today’s smart devices, the digital footprints left by people who are increasingly living their lives online, and the rich sources of information commercially available from specialized data vendors. Add to this the tremendous wealth of data — structured and unstructured, historical and real-time — that have come to reside in diverse systems across the enterprise, and it is clear that Big Data offers hugely appealing opportunities to those who can unlock its secrets.

The other factor contributing to Big Data’s appeal is the emergence of powerful technologies for effectively exploiting it. IT organizations can now take advantage of tools such as Hadoop, NoSQL, and Gephi to rationalize, analyze and visualize Big Data in ways that enable them to quickly separate the actionable insight from the massive chaff of raw input. As an added bonus, many of these tools are available free under open source licensing. This promises to help keep the cost of Big Data implementation under control.

Marwa Khudhair

Big data means large volume, wide variety, increased variability, extensive velocity, and increased veracity of information. Big data refers to complex and sophisticated data within the context of volume, rate, variability, integrity, and variability, which can only be processed using advanced analytical data processing tools. Big data is challenging to store, manage, and process using traditional tools and systems. The term big data refers to “a data that is so large, fast or complex that it is difficult or impossible to process using traditional methods. Big data is larger, more complex data sets, especially from new data sources” (Crispo, 2019). The meaning of data revolves around complexity in the volume, veracity, variability, velocity, and variety of either or both structured semi, or unstructured data in an organization. Most companies, notably those relying on large chunks of information, rely on data to analyze and successfully operate their businesses. As such, they rely on big data and modern systems to help them manage and process data. However, there are drivers of big data. The drivers of big data influence a business to use big data, whether voluntarily or involuntarily. Some of the drivers of big data may be summarized as “The digitization of society; The plummeting of technology costs; Connectivity through cloud computing; Increased knowledge about data science; Social media applications; and The upcoming Internet-of-Things (IoT)” (Daniel, 2019). Businesses and organizations quickly shift to big data influenced by needs, innovations, societal lifestyle, and dependency on technology. There is a general desire to successfully operate businesses using big data witnessed in companies like Netflix, Google, Apple, and Ali Baba Company. Big data takes the form of quantitative data. Within this context, quantitative data mean “any set of information that can be numerically recognized and analyzed. Quantitative data is the most relevant form of data for use in mathematics and statistics, as it is the primary type of data that can be measured objectively” (Shelby, 2021). Quantitative data is useful in organizations. Focusing on quantitative data benefits a business in terms of data quantification, predictive analysis, data collection, eliminating biases in data, and enhanced statistical analysis.


Crispo, G. [GdataAnalyst]. (2019, September). Big data: What it is and why it matters [Infographic]. SAS. Retrieved September 7, 2021, from…

Daniel, C. (2019, February 28). Business drivers for big data. Enterprise Big Data Framework.…

Shelby, S. H. (2021, April 7). What is quantitative data? Datamation.…

by Ngoc:

What is “big data”? What are the most important drivers of big data? What could happen if companies focus solely on the quantitative (i.e., “numbers”) aspects of big data? Explain.

Big Data is the result of the exponential growth of data in both public and private organizations. Big Data is data so voluminous that conventional computing methods are not able to efficiently process and manage it.

Companies such as Visa and MasterCard have been involved in big data for years, processing billions of transactions on a typical holiday weekend. But there has been an explosion in data growth throughout the business world in recent years. In fact, it has been estimated that the volume of business data worldwide doubles every 1.2 years (Bidgoli, 2017).

The bulk of big data generated comes from three primary sources: social data, machine data, and transactional data. Three characteristics define Big Data: volume, variety, and velocity. Together, these characteristics define “Big Data”.

Volume—This refers to the sheer quantity of transactions, measured in petabytes (1,024 terabytes) or exabytes (1,024 petabytes). For example, the number of tweets sent or received around the world per day.

Variety—This refers to the combination of structured data (e.g., customers’ product ratings between 1 and 5) and unstructured data (e.g., call center conversations or customer complaints about a service or product).

Velocity—This relates to how quickly the data must be collected and processed. For example, consider a billboard that can display a specific advertisement as soon as a specific customer passes by. The billboard would recognize the driver’s face by comparing it to a large database, combine that information with the driver’s social media data, determine the driver’s preferences based on the number of likes and dislikes on his or her Facebook profile, and then display the appropriate advertisement. This has to happen within a nanosecond, otherwise, the window of opportunity is lost (Bidgoli, 2017).

If you focus your efforts entirely on quantitative data, you’ll overlook a huge amount of valuable information, and your insights and decision-making could become distorted as a result. This information is critical for understanding how to reach people most effectively and achieve success with your company. Quantitative data is any set of information that can be numerically recognized and analyzed. Quantitative data is the most relevant form of data for use in both mathematics and statistics, as it is the primary type of data that can be measured objectively.

On the other hand, when used correctly for a specified task, quantitative research focuses on gathering numerical data and generalizing it across groups of people or explaining a particular phenomenon. It provides a business with detailed information that cannot be expressed in a graph or chart. While qualitative data gives detailed information, it can be time-consuming and costly to gather and analyze.

The purpose of quantitative research is to attain greater knowledge and understanding of the social world. Researchers use quantitative methods to observe situations or events that affect people. Quantitative research produces objective data that can be clearly communicated through statistics and numbers (Williams, 2021).

Business owners can now use quantitative methods to predict trends, determine the allocation of resources, and manage projects. Quantitative techniques are also used to evaluate investments. In such a way, organizations can determine the best assets to invest in and the best time to do so.


Bidgoli, Hossein. MIS, 8th ed. Cengage Learning. 2017. ISBN: 978-1337406925

Williams, T. (2021). Why Is Quantitative Research Important? Doctoral Journey. Grand Canyon University. Retrieved from… 

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