BIG DATA: Advantage and Capability Gap (Article by Dian Martha Nurrul Amanah)

Along with the development of technology, many people are accessing many things digitally. The more people who use the internet, the more digital footprints formed. The numbers are very abundant and contain a lot of information. Based on the very large number, this digital record is part of big data.

Jabbar et al (2019) describe big data as an incredibly large data set, consisting of structured and unstructured data that can be processed and analyzed to reveal patterns and trends. In more detail, the notion of big data is expanded into several components known as 7V (Hallikainen et al, 2019), namely:

1. Volume: scale and amount of data
2. Velocity: the speed at which data is generated and the speed at which data is analyzed
3. Variety: various structured and unstructured data formats
4. Value: the value obtained from the data
5. Veracity: truth, certainty, and accuracy of data
6. Variability: the meaning of data is constantly changing
7. Visualization: presenting data in a readable way

There are many things that companies can explore in today's big data era. Such as psychological traits, including personality, IQ, and political orientation can be accurately predicted based on consumers' digital track records (Matz and Netzer, 2017). Understanding consumer conditions and psychology can be used to match the goods and services offered by the company with the desires and tendencies of consumers.

"Many industrial marketers are being challenged by a large ocean of data that is far beyond the company's capacity to understand and use."

Unfortunately, the explosion of big data information is not matched by the company's ability to take advantage of the large amount of information available. There is a fairly high gap in this regard. The growth of a person's information grows 50% per year, but consumption of that information only grows 2% per year (Day, 2011). Many industrial marketers are being challenged by a large ocean of data that is far beyond the company's capacity to understand and use. Like gaps in the big data infrastructure for machine-generated and unstructured data, big data visualization, artificial intelligence and machine learning, and advanced data analytics (Herhausen et al, 2020).

As Day (2011) points out, During periods of technological disruption, most organizations have difficulty keeping up. The tendency toward inertia and sclerotic decision making is triggered by the effects of lag and organizational rigidity. Although it can overcome the problem of rigidity, companies need time to absorb, interpret, and translate new information into a strategic action that is beneficial to the company.

Citing data from Sirait (2016) which was sourced from Capgemini Consulting's 2014 research on 224 global company leaders in Europe, North America, and Asia Pacific, only 13% of organizations make full use of big data. In Indonesia, the application of big data in the business sector is still in the early stages for business prediction purposes and just few for the stage of business decision making (Kementrian Komunikasi dan Informatika, 2018). There are still negative expectations from business executives to use big data applications.

By utilizing big data, companies can better understand consumer profiles and increase consumer involvement in improving the company's market performance (Dong and Yang, 2018: 3). This big data analysis will later form a pattern of information about the market and consumers that is useful for companies to maintain sustainable relationships with consumers. According to Boldosova (2019), big data analysis is the process of collecting, processing and visualizing large volumes of data to provide descriptive, predictive and prescriptive descriptions and information. This analysis also supports decision making within the organization.

"It is important for companies to have good data science and qualified human resources."

Therefore, it is important for companies to have good data science and qualified human resources. Data science is about finding patterns and predicting how one variable will affect other variables (Davidowitz, 2017). However, the larger and more complex the data, the more difficult it is to read it. It takes an advance understanding of how to manage data into the required information. Because of the large amount, big data will be difficult to understand without processing it first. That is why, big data processing cannot be separated from the help of technology such as computers to make it easier to read.

References:

Boldosova, V. 2019. Telling Stories That Sell: The Role of Storytelling and Big Data Analytics in Smart Service Sales. Journal of Industrial Marketing Management Volume 86.

Davidowitz, S. S. 2017. Everybody Lies. Big Data dan Apa yang Diungkapkan Internet Tentang Siapa Kita Sesungguhnya. Gramedia Pustaka Utama: Jakarta.

Day, G. 2011. Closing The Marketing Capabilities Gap. Journal of Marketing.

Dong, J. Q., and Yang, C. H. 2018. Bussiness Value of Big Data Analytics: A Systems-Theoretics Approach and Empirical. International Journal of Information and Management.

Hallikainen, H., Savimaki, E., and Laukkanen, T. 2019. Fostering B2B Sales with Customer Big Data Analytics. Journal of Industrial Marketing Management Volume 86.

Herhausen, D., Mio?evi?, D., Morgan, R. E., and Kleijnen M. H. P. 2020. The digital marketing capabilities gap. Industrial Marketing Management.

Jabbar, A., Akhtar, P., and Dani, S. 2019. Real-Time Big Data Processing for Instantaneous Marketing Decisions: A Problematization Approach. Journal of Industrial Marketing Management.

Kementrian Komunikasi dan Informatika. 2018. Big Data, Kecerdasan Buatan, Blockchain, dan Teknologi Finansial di Indonesia. Usulan desain, prinsip, dan rekomendasi kebijakan.

Matz, S. C., and Netzer, O. 2017. Using Big Data as a Window into Customers’ Psychology. Current Opinion in Behavioral Sciences. 7-12.

Sirait, E. R. E. 2016. Implementasi Teknologi Big Data di Lembaga Pemerintahan Indonesia. Jurnal Penelitian Pos dan Informatika.