ScienceSoft Company
ScienceSoft Company is one of the companies using big data for a competitive advantage. The company is IT-related, and it offers both consulting, software development, and other IT services. The company offers services, for example, consulting, big data as a service, and implementation. To ensure success, the company has embraced the four V(s) in their service delivery. These V(s) are volume, velocity, variety, and veracity (Kapil et al., 2016). The company has big volumes of data to process; they have to use very high speeds, develop data from varying sources, and have high-quality data. These four V(s) help a company become outstanding, thus giving it a competitive advantage.
Velocity involves using high speeds to process data (Kapil et al., 2016). ScienceSoft Company offers high speed, and this makes it outstanding among other related IT companies. By Variety, we mean gathering data from different sources, which makes data big. To process a variety of data, ScienceSoft has distinct processing, and they use special algorithms. The other characteristic is veracity, which refers to data quality. The data quality makes the data big, and ScienceSoft has been using high-quality data for their services. Using data of high quality, the company can have a competitive advantage over companies that rely on low-quality data.
There is a need for a company to maintain their customers before they can find value in other companies. Additionally, customers are willing to work with companies offering ready services than those that do not. With data mining, a business can easily tell what its customers want right before asking for it, thus creating a good competitive ability (Ismail et al., 2015). This approach helps businesses not only maintain their customers but attract more customers too. Effective data mining helps a business identify the correct customer offer to make and gives an appropriate approach to make any business transactions and processes.
Despite the need to use data mining to improve customer value, there are some challenges, for example, data transformations. There is a challenge for the mining tools in transforming data. Another challenge is the scalability in mining algorithms (Ismail et al., 2015). The final challenge is making these mining models clear to the users. Some businesses find it difficult to design strategies that will present data mining into the business.