Successful BI Implementations
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. Consider the times, even in school, when you cannot find the right information. Sometimes, narrowing search terms can be difficult if you are unfamiliar with the topic. 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, as these guidelines keep you on track to finding the right data to answer the questions you need to focus on rather than following rabbit holes and finding yourself in the weeds, awash in signals. What are some examples where you have had to make decisions and were concerned about the quality of the data you used to make those decisions? What did you do to 'connect the dots'?
Introduction
Big data
"I have just bought a house! I have bought a big house!" When people talk about big objects, generally there is a common sense of the word "BIG". When people use the word "big house", they are usually talking about the house area or the number of bedrooms. But, what does it mean when we use the word "big data", and what differentiates "big data" from the usual usage of the term "data"?
Big data is a developing phenomenon in the field of Information Technology. Big data includes data sets that can't be analyzed by the common traditional data analysis tools. Big data refers to a high volume of data with a high velocity and a high variety; these properties require more efficient methods than the current ones used in conventional database systems for decision making. Big data enables systems to manage their processes using a large volume of real-world data.
Big data entered the field of practical research in the 21st century; there was no noteworthy research applying big data analytics in other fields before 2000. The main characteristic of big data is simply its huge volume of data, but some other characteristics have been added to this definition over the years. The first time that Big Data was defined by the 3V model (Volume, Velocity, and Variety) was in a study by Laney. Volume refers to the amount of available data; Velocity refers to the timeliness of the data; and Variety refers to the diversity of the data types, including unstructured, semi-structured, and structured data sets.
Two other important Vs have been added to the definition of big data in the most recent decade. The economic Value refers to the profit gained by analyzing a huge volume of data, and Veracity refers to the considerable amount of uncertainty and imprecision in the big data. Wamba et al., integrated all of the Vs in one place and introduced the 5V big data framework for the first time. Figure 1 represents the evolutionary timeline of the big data concept, as well as the most-cited articles using big data in manufacturing, logistics, and supply chain management.
Figure 1 Big data framework evolution during the time.
Big data analysis is a process that transforms terabytes of low-value data into a small amount of high-value data, which shows an overview of the company using just a small slice of the overall picture. A big data system can be separated into four consecutive phases: data generation, data acquisition, data storage, and data analytics.
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.
Methodology and research questions
There have been many developments in big data collection and analysis methods in recent years. The author of this paper used the Thomson Reuters Web of Science search tool to track the number of "big data" publications over the last decade. Since several studies were performed on the topic of big data, we narrowed our search to the articles that have "big data" in their title. We also tried different combinations of title and topic searches in the Web of Science and came up with the solution that the best combination was to keep "big data" in the title search and the other keywords in the topic. For example, a search for articles with both "big data" and "supply chain" as a topic for the year 2019 gave us 173 articles; several of them were not related to the focus of our study at all. On the other hand, a search for articles with both "big data" and "supply chain" in the title for the year 2019 gave us only 15 articles, with many valuable articles not included. However, a search for articles with "big data" in the title and "supply chain" as a topic for the same year resulted in 54 articles that covered topics related to our study. Our methodology here was the same as that in many other review papers to collect the literature, provide the descriptive analysis (in this section), develop categories of interest, and evaluate the papers (sections 2, 3, and 4).
Figure 2 illustrates the number of published articles from 2010 to 2019 with the word "big data" in their title and "manufacturing", "supply chain", and "business" in the topic. As can be seen in Figure 2, there is an increase in the number of publications in the field in recent years. It is worth nothing that not much research was published on big data before 2010. About 15% of the big data publications have the word "business" in their topic, and many of publications were dedicated to other fields of study, such as engineering and science. Among the business-related studies, about 46% of publications focused on "supply chain" and "logistics" in their topic, and about 12% have manufacturing as their topic.
Figure 2 Number of publications with "Big Data" in title (Thomson Reuters Web of Science).
Different academic journals cover different subject areas; therefore, journals that publish most of the papers in an area of research can be a useful guide for those who are looking for the available literature or submitting their own contribution. The Thomson Reuters Web of Science was also used to develop a bibliometric of big data publications containing the terms "supply chain", "logistics", and "manufacturing" when looking at the journal title and impact factor, as can be seen in Figure 3.
Figure 3 Publishing journal (impact factor in parentheses) with a topic of a) Manufacturing or b) Supply Chain and logistics.
Figure 3 shows that "Annals of Operations Research", "Sustainability", "Technological Forecasting and Social Change" and "The International Journal of Logistics Management" are the journals which published the most papers that applied big data analytics in supply chain management. The greatest impact factor among these journals is for the "Journal of Cleaner Production," with an impact factor of 6.395.
Information technology developments encouraged many researchers and practitioners to use big data analytics in manufacturing systems, logistics processes, and other functions of a supply chain. Moreover, since the benefits of big data analytics have attracted more researchers into the field, several more papers have been written that review the literature and define possible future directions. According to Figure 2, most research in this area has been performed in recent years; therefore, a current literature review can give more insight for researchers who want to focus their work on big data applications in supply chain management. Moreover, our study categorizes the literature into three key areas: manufacturing systems, logistics processes, and supply chain functions. In each category, papers are grouped based on their main topic, and quality papers are summarized in tables to give more information about each paper. The current study is trying to answer the following questions:
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What are the different categories of big data analytics that are used in supply chain management?
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What are the factors that affect the attractiveness of using big data analytics in supply chain management studies?
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What supply chain management research topics are studied more often by big data analytics?
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What are the hurdles and advantages of using big data analytics in supply chain management research? What must be done in the future?
The remainder of this article is dedicated to reviewing published research in the specialized fields. Section 2 surveys published research that looks at applying big data analytics to manufacturing systems. Sections 3 and 4 review the published research with a focus on using big data analytics in supply chain management and logistics processes. In each subsection the subjects most often studied are introduced, and some of the quality papers are summarized in relevant tables. Section 5 concludes this study by discussing the hurdles and advantages of using big data analytics. Moreover, some directions for the future studies are hypothesized in Section 5.
Source: Iman Ghalehkhondabi and Ehsan Ahmadi and Reza Maihami, https://www.scielo.br/j/prod/a/G644rRdt9XrQ65M9VGsbCmc/?lang=en
This work is licensed under a Creative Commons Attribution 4.0 License.