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Concept of Big Data

 

In Data analysis, the concept of Big Data plays a crucial role. Before digging deep into Big Data, it is important to start at the very beginning. By defining data, itself. When information is collected for calculations or analysis, the information must be translated into a form, which makes the analysis process efficient. And this form of information that can be calculated or analyzed is called data. From the very definition of data, it is clear that structure plays a major role. Information that has been collected is converted into a structured set of data that can be used in the process of data analysis through traditional methods.

The concept of data as established in the previous paragraph is the basic concept of data. But that simplified definition of data is often just a theoretical concept regarding major organizations' data management processes. The volume of the data collected by major organizations is large and divided into several environments. The data collected by major organizations are also more often than not of a wide variety. And the velocity at which the data is generated and processed is also much higher than traditional methods for data collection and analysis can handle. Volume, variety and velocity, these 'three v's' form the characteristics of what can be referred to as Big Data. Thus, the combination of structured, semi-structured and unstructured data that are used by major organizations in data management processes and require advanced analytic measures are called Big Data (Cui et al., 2020).

Big Data Analytics

 

In the scope of the previous subtopic, Big Data was defined and explained. Although, a major aspect of Big Data was left sort of dangling; mentioned, but not explained. The fact that traditional methods of data analysis are insufficient for analyzing Big Data (Tabesh et al., 2019). The complex process of examining Big Data to uncover the required information is called Big Data analytics.

Data analytics can be broadly defined as technologies or techniques that provide organizations with the possibility of analyzing data sets and deducing new information from them. The techniques utilized by organizations to arm themselves with the ability to analyze Big Data are often complex and involve elements such as statistical algorithms, predictive models and even "what-if" analyses (Hamilton & Sodeman, 2020). These complex technologies required to examine big data to uncover information are collectively contained within the topic of Big Data analytics.

Big Data Analytics and Marketing

 

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In recent years, a drastic evolution has been observed in marketing. Initially, marketing used to be a process that used to be common for the mass. Everyone would be a target of the same products with companies and businesses spreading the awareness of their products across the masses with the hopes of strengthening their customer base. But in recent years, with the advancements in online advertisement techniques, marketing has become more personalized and customer-centric (Dzwigol, 2020). This is only possible through organizations keeping track of customers' online activities, tracking their preferences and gauging the purchase history of an individual customer to predict their future requirements. Through the use of these measures, organizations have been able to create a more personalized marketing model that caters to the preferences of every individual or a group of individuals.

This power of being able to deduce an individual's preferences and suggest to them what they might want to purchase next rests in the hands of certain complex algorithms- mainly statistical and predictive (Lies, 2019). Now, if these algorithms seem familiar, it is because they were discussed as the technologies used for Big Data analytics. Big Data analytics has played a major role in the development of modern personalized marketing models. By definition, Big Data is characterized by the three v's, namely, volume, variety and velocity. All three are prevalent in the process of deducing individual preferences. There are a vast number of individuals to analyze to generate a personalized marketing system. Thus, volume. Each individual is different from one another and has different tastes and preferences. Thus, variety. And finally, keeping track of the online purchase history of the global population definitely will involve the gathering and processing of rapidly accumulating data. Hence, velocity.

Customizing marketing strategies to cater to different individuals is a major application of Big Data in marketing, but there are other important applications of Big Data as well when it comes to its utility in marketing (Cao et al., 2021).

• • A significant role played by Big Data analytics in marketing is raising brand awareness. Being data-driven and thoroughly analyzing the market and the customer base helps companies raise their brand awareness.
  • A company that uses Big Data to successfully predict the needs of its customers often pulls ahead in the race against the competition by catering to the needs and preferences of its customer base (Ranjan & Foropon, 2021).

 

• • Big Data's characteristic third 'v', velocity implies the ability to rapidly and efficiently collect, process and analyze real-time data and to be able to utilize that to take effective and instantaneous action. This is an absolute necessity when it comes to the analysis of GPS data, clicks on a website page and other such real-time data.
  • Business Intelligence (BI) can be referred to as the collection of knowledge, technologies and skills required to make data-driven business decisions. Big Data analytics provides BI, which optimizes market performance, hence, resulting in time and costs being saved.

Data Protection challenges faced when working with Big Data

There are certain security issues prevalent in the scenario of Big Data being used for Data Management. These issues regarding protection of the data are:

• One data protection challenge faced by companies and individuals working with Big Data is a risk of generation of fake data. Fake data or synthetic data is artificial information created via algorithms to test out or validate hypotheses or mathematical models. Thus, the generation of fake data has the capacity to have a negative impact on Big Data Analysis.
• Granular access control is an issue impacting data protection in Big Data analysis. Granular access control refers to the system that defines who can have what amount of accessibility to each part of the system. This implies a difficult and time-consuming task, and time as mentioned before is a very important component when it comes to Big Data analysis. Thus, granular access control becomes an inconvenient endeavor, but does affect the vulnerability of the data.
• There is often the case where the ‘points of entry and exit’ for the data are secure but the data within the system might be vulnerable. With the points of entry and exit secured, in transit, the data is safe. But with the inability to protect the data in a static state when stored within a system makes the data vulnerable to being leaked or changed, in turn negatively impacting the analysis.
• Real-time data is most vulnerable when it comes to data protection due to the instant nature of the processing of such data. The velocity at which real time data is collected and processed makes it a difficult task to offer protection to such data while in transit. This vulnerability makes protection of real-time data a challenge, given that there is no assured way to defend such data from miscreants.

Concluding Remarks

 

As this introspective piece regarding the impact of Big Data Analysis on marketing suggests that Big Data is a necessary component to the modern marketing landscape, especially online. It was also deduced how the application of Big Data Analysis on marketing has a vast area of effect, as other than targeted marketing, Big Data helps businesses in several ways including raising product awareness and raising profit. However, there was one aspect that Big Data is lacking on, due to the complex and fast handling of data analysis, the data itself becomes vulnerable. But, with awareness on that front, the rest of the implications of Big Data on marketing are extremely positive.

 

 

References

Cao, G., Tian, N., & Blankson, C. (2021). Big Data, Marketing Analytics, and Firm Marketing Capabilities. Journal Of Computer Information Systems, 62(3), 442-451.

Cui, Y., Kara, S., & Chan, K. (2020). Manufacturing big data ecosystem: A systematic literature review. Robotics And Computer-Integrated Manufacturing, 62, 101861.

Dzwigol, H. (2020). Innovation in Marketing Research: Quantitative and Qualitative Analysis. Marketing And Management Of Innovations, (1), 128-135.

Hamilton, R., & Sodeman, W. (2020). The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources. Business Horizons, 63(1), 85-95.

Lies, J. (2019). Marketing Intelligence and Big Data: Digital Marketing Techniques on their Way to Becoming Social Engineering Techniques in Marketing. International Journal Of Interactive Multimedia And Artificial Intelligence, 5(5), 134.

Ranjan, J., & Foropon, C. (2021). Big Data Analytics in Building the Competitive Intelligence of Organizations. International Journal Of Information Management, 56, 102231.

Tabesh, P., Mousavidin, E., & Hasani, S. (2019). Implementing big data strategies: A managerial perspective. Business Horizons, 62(3), 347-358.