There is no standard definition for big data or data mining

Q: There is no standard definition for big data or data mining. For this paper, follow the general definitions used in our textbook. “Big data” refers to a dataset that is too complex and big to apply traditional data analysis methods. “Data mining” is discovery-oriented in comparison to traditional databases when users know what they are looking for in the database. Provide an example of a company that is collecting big data for competitive advantage. Explain how each of the three Vs, (i.e., volume, variety, velocity, and veracity) outside the volume, is helping the company achieve a competitive advantage.

Explain the values of data mining in a business and at least three challenges in managing a data mining project.

A:, Inc. employs big data for competitive advantage. The company relies on its customer data: names, addresses, payment details, search histories, and online activity, to recommend and market personalized products and services.

  • Variety: The Company gets data from various sources, including structured and unstructured data. It includes weblogs, social interactions, e-commerce information, and online and financial transactions. Access to this information from various sources helps the company understand customers’ and market needs and devise effective marketing and sales strategies for different segments.
  • Velocity: There is always a limit on the time a company collects data up to when it becomes available for use. Inc. collects, stores, processes, and analyzes data fast enough through its ‘quick delivery timeframe’ to ensure customers obtain products and services faster hence a competitive advantage. For instance, once the customers purchase on the website, their data is stored to ease the subsequent order process and allow faster delivery times.
  • Veracity: Data quality can get influenced by its source, the collection method, and the analysis method. Inc. uses various technologies to provide better visualization, improving data accuracy. It enables the company to provide services more efficiently, gaining a competitive advantage in the sector.

Data mining is crucial in a business in various ways. It enables a company to increase customer retention through more accurate targeting and personalized marketing. It offers a competitive advantage with better customer insights. Data mining is also crucial in managing risks in insurance and business from competitive threats, poor product quality, and customer attrition (Hormozi & Giles, 2004). It is also essential in fraud detection and customer acquisition and retention.

Despite the benefits, there are several challenges associated with data mining. There is the problem of heterogeneity and incompleteness because data in bulk is often unreliable or inaccurate because of human mistakes or errors in equipment that measure data (Beakta, 2015). There is also the challenge of data complexity, whereby the data comes in various forms, necessitating interpretation while concentrating on the essential information. Finally, there is the challenge of timeliness because as the size of the data sets to be processed increases, it takes more time to analyze.


Beakta, R. (2015). Big data and hadoop: A review paper. International Journal of Computer Science & Information Technology2(2), 13-15.Chicago

Hormozi, A. M., & Giles, S. (2004). Data Mining: A Competitive Weapon for Banking and Retail Industries. Information Systems Management, 21(2), 62–71.

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