Big Data Analytics in Supply Chain Management
Read this paper for an overview and examples of how big data is used in specific areas, such as supply chain management, risk management, and logistics of business in industry. One of the biggest issues for analysts with big data is knowing how to separate the valuable data from that which does not help answer their requirements.
Sometimes people describe intelligence as "connecting the dots", but it is rarely simple like a "paint-by-numbers" art project. The dots are not just lying around waiting to be connected. More appropriately, it has been described as filtering out the right radio signals from the fray in a huge city. You have to be carefully tuned to your requirements, which will be discussed at length in Unit 2 and again in Unit 8, as these are the guide stars that keep you on track to finding the right data to answer the questions you need to focus on.
1. Introduction
1.2. Big data applications in a business environment
Because of recent technology developments, obtaining data is not a difficult task anymore, though the efficient use of data to achieve strategic and operational goals is still an area of concern. Traditionally, businesses used their own data to make decisions, but the development of new technologies gives businesses access to various brand-new types of datasets. The usage of social networking is booming at a quick pace, and a huge volume of consumer data is being provided to businesses. Big data has become a major keyword in the technology world and has shown its useful applications in other areas as well. For example, big data has been successfully used for fraud prevention and detection in financial transactions.
Data plays a vital role in developing today's operational systems. Big data can be used to increase business competitiveness, according to the recent development of data. Today's business environment provides a huge opportunity, since a large volume of data is generated every minute. Most companies use big data for continual improvement. Four steps are commonly used in data analytics: The first step is to ensure that the available data is clean, structured, and organized, which can then be used for further analysis. The second step is to ensure that the right data is accessible in the right form, the right time, and the right place. The third step is to do quantitative analyses, such as descriptive analytics. The fourth step is to apply advanced analytics such as predictive analytics, automated algorithms, and real-time data analysis. Using big data in the last step requires particular expertise in advanced data analytics.
Various techniques such as statistics, data mining, machine learning, neural networks, pattern recognition, visualization, etc. are used to extract any valuable information out of big data. For example, cloud computing is one of the practices used to store, develop, and deploy big data in business processes.
Decreasing data management costs can increase the desirability of companies to use big data. For example, in 2019, storing a terabyte of data using relational traditional databases could cost over $20000 for a company, but storing the same amount of data could cost just $1000-$2000 using cheap big data technology such as a Hadoop cluster. Hadoop gained popularity in the area of technology development because of its price and capacity for data storage.