Big Data Analytics Technologies

Subject: Tech & Engineering
Pages: 8
Words: 2809
Reading time:
10 min
Study level: College


Organizations have been pushed to adapt to remain relevant in today’s highly competitive marketplace due to the development of information technology, increased customer expectations, economic globalization, and other modern competitive priorities. Therefore, competition between businesses now occurs between companies and their supply chains rather than just between firms. In today’s cutthroat business climate, we, as supply chain professionals, face an uphill battle when it comes to managing the massive amounts of data required to achieve an integrated, efficient, effective, and agile supply chain. There is an inherent demand for developing technologies capable of intelligently and rapidly analyzing massive volumes of data due to the exponential expansion in the volume of data and the variety of data types throughout the supply chain. The capacity to analyze big data, also known as big data analytics capability (BDA), is one of the most effective methods that can assist us in solving our challenges. “The concept of Big Data has been around for a while, but it was not until recently that Big Data has revolutionized the business world”(Nikita,2022).BDA is a tool for discovering valuable patterns and information hidden inside an enormous volume of data. Customer wait time is still king for winning the competitive edge in SCM. Big data is the tool necessary for achieving the desired competitiveness essential to become and remain a top-tier SCM organization.

What is Big Data Analytics?

BDA is used to find previously unseen patterns, correlations, and market trends that can help firms make better business decisions by analyzing enormous amounts of data. “On a broad scale, data analytics technologies and techniques give organizations a way to analyze data sets and gather new information.” (BDA,2022)This process can be pretty complex. On a much larger scale, the application of data analytics technology and methods provides businesses with a means to conduct data set analyses and acquire new information. Business intelligence (BI) inquiries respond to fundamental questions on the operations and performance of businesses. Advanced analytics is a subset of analytics that entail the use of complicated applications that include components such as predictive models, statistical algorithms, and what-if analysis that are powered by analytics systems. Advanced analytics includes big data analytics as a subset.

Where did Big Data Analytics Come From?

In the middle of the 1990s, the term “big data” was initially used to refer to the ever-increasing volumes of data. In 2001, an expansion of the definition of “big data” took place. This expansion represented the expanding volume of data that companies were storing and using, the increasing variety of data that organizations were generating, and the increasing velocity, or speed, at which that data was being created and updated. These three variables are now commonly referred to as the “3Vs of big data.” The introduction of the Hadoop distributed processing architecture is also seen as a pivotal moment in the evolution of the big data industry. Hadoop was initially presented as an open source project by the Apache software foundation in the year 2006. Because of this, the groundwork was laid for a clustered platform that would be built on top of commodity hardware and would be capable of running big data applications. For managing large amounts of data, the Hadoop framework of software tools is frequently utilized. By 2011, enterprises and the general public were beginning to take a severe interest in big data analytics, Hadoop, and other big data technologies. In the beginning, as the Hadoop ecosystem began to take shape and start to mature, the primary users of big data applications were giant internet and e-commerce corporations like Yahoo, Google, and Facebook, in addition to analytics and marketing services providers. More recently, a wider variety of users have begun to see the importance of big data analytics as a critical technology driving digital transformation. Retailers, financial services corporations, insurers, healthcare organizations, manufacturers, energy companies, and other types of businesses are some entities that use the platform.

Identify applicable analytic methods

Big data is defined as extremely large or complicated data sets, often covering an area greater than an exabyte. It outperforms conventional systems, which have restricted storage, handling, monitoring, decoding, and displaying capabilities. These days, the amount of data created is growing exponentially, with expectations to exceed one zettabyte yearly. The academic community and working professionals agree that this influx of data creates modern opportunities. Many organizations have attempted to develop and upgrade their BDA capabilities to reveal and gain a deeper understanding of their (BDA) values. The study of big data is continually evolving and expanding. The majority of the characteristics of big data extend into a concept known as “5 V’s,” which consists of variety, verification/veracity, velocity, volume, and value. BDA is an essential aspect that influences organizational effectiveness. By advancing BDA, firms have the potential to leverage a more profound knowledge of the demands of their customers, provide outstanding service to satisfy those customers’ needs, increase sales and income, and break into new markets. Several research studies have pointed to big data applications in various industries, including banking, marketing, logistics, manufacturing, and insurance. In order to have a complete comprehension of the effects and applications of BDA, the first step is to have a solid grasp of what BDA is. As a simple definition, big data alludes to a vast quantity of data. Big data refers, in particular, to massive data sets of such a scale that they cannot be stored in memory in their entirety because of their sheer volume. It is possible to collect, store, communicate, aggregate, and evaluate these data sets. The quantity of information that needs to be analyzed has increased, which means that the tools used to capture data need to be updated. In contrast to how things were done in the past, these data should not be organized into clean columns and rows like standard data sets for modern technology to evaluate them properly. Big data is present in a variety of different types of data. They consider all forms of data gleaned from all sources. They may have an organized, semi-structured, or utterly unstructured format. The advanced analytic technologies available today allow us to derive knowledge from all data types. Math and statistics are combined in analytics, which is applied to massive amounts of data. BDA refers to applying statistics and mathematics to analyze large amounts of data. Without large amounts of data, analytics is nothing more than mathematical and statistical methods. These massive amounts of data can yield valuable business insights if appropriately analyzed. All these things are made feasible by the vast amounts of processing power available now. This power is found at significantly lower prices than in the past. The combination of big data and analytics, on the other hand, produces a variety of tools that assist decision-makers in gaining valuable and meaningful insights and transforming information into business intelligence.

Justify the selection of the method for customer wait time. The number of organizations that make up the supply chain, from the suppliers of raw materials to the producer or central organization, distributors, retailers, customers, and end users, are all included. Not only does the supply chain consist of physical flows such as the transfer of raw materials and finished goods, but it comprises informational and monetary flows. Supply chain analytics, often known as SCA, is the process of utilizing BDA methodologies to glean previously hidden valuable information from supply chains. You can divide these analytics into three categories: descriptive, predictive, and prescriptive. Decisions that have been carefully planned out and carried out contribute directly to the bottom line by reducing the costs of procurement, transportation, storage, stocking out, and disposal. Therefore, utilizing BDA strategies to find solutions to supply chain management issues has a beneficial and valuable impact on the overall performance of the supply chain. For a considerable time, researchers and managers have relied on statistical and operational research methods to find solutions to issues involving balancing supply and demand. On the other hand, recent applications in analytics have presented managers and researchers with fresh opportunities. Academicians and researchers have started paying attention to the potential integration of big data in SCM due to the various benefits of leveraging data-supported decision-making. Consequently, there has been a meteoric surge in the number of scientific articles published on this subject during the past several years. For a firm to have any shot at being successful in today’s competitive marketplaces, it must implement BDA tactics into its supply chain management practices. Since 2010, many articles have been published that emphasize the use of BDA in SCM and the notable accomplishments it has brought about. BDA methods typically use predictive and prescriptive approaches rather than descriptive ones. What has taken place, what is happening now, and why are these occurring? This process identifies new opportunities and challenges through visualization tools and online analytical processing (OLAP) system, backed by reporting technology (such as RFID, GPS, and transaction bar codes). The raw data of previous occurrences can be collected, described, and analyzed with the help of a statistical method called descriptive statistics. It does so by reviewing and describing past events, then transforming that information into something people can interpret and comprehend. An organization can gain insight from its own history and a better understanding of the link between variables and how that relationship can influence future results, thanks to descriptive analytics. For instance, it can be used to indicate variations in annual sales, the number of goods currently in inventory, and the average money. A firm’s financials, sales, operations, and production reports can all benefit from descriptive analytics. The topic of what will occur in the future or what is likely to occur is answered with the help of techniques known as predictive analytics. These techniques involve analyzing past data patterns using statistical, programming, and simulation methods. These methods are utilized to elucidate the reasons behind occurrences and occurrences of phenomena, as well as provide an accurate forecast of the future or fill in data or information that does not currently exist. The future cannot be accurately predicted using one hundred percent precision statistical methods. Predictive analytics allows one to detect and forecast the future trend of sales activities by making predictions regarding purchasing patterns, customer behavior, and purchase patterns. These methods are also utilized to predict customers’ wants and records of inventory and operations. Prescriptive analytics guides alternative decisions based on descriptive and predictive data review, using this method leads to optimization of decision-making capabilities.

What are the draw backs of Big Data Analytics?

The application of big data analytics is not without its share of difficulties. Accessibility of data is one shortfall that must be recognized. “Unfortunately, big data is so large that none of the traditional data management tools can store it or process it efficiently.” (Adlin, 2021).When there is a greater volume of data, storing and processing that data becomes more difficult. Big data must be stored and managed correctly to make it accessible to data scientists and analysts with a lower level of skill. Data quality maintenance is another inefficiency of big data. Data quality management for big data demands a lot of time, effort, and resources to maintain it effectively. This is as a result of the large amounts of data that are being gathered from a variety of sources, presented in a wide range of formats. The complexity of big data systems creates new and exciting issues for data security. Effectively resolving security risks within an ecosystem as complex as that of big data can be difficult and time-consuming. Deciding which instruments to use can also proove complicated. The vast number of big data analytics tools and platforms that are currently on the market can make it challenging to make a pick from among them. Because of this, businesses need to be aware of how to choose the most suitable tool for their users and their existing infrastructure. Some companies are having difficulty filling the gaps in their services due to the high cost of employing competent data scientists and engineers and the possibility of an internal skills shortage in analytics.

Industries that benefit from Big Data Analytics

The field of logistics can oftentimes feel like a confusing labyrinth to navigate when it comes to information. The current boom that we are experiencing in the sector is the primary reason why the information that was studied is readily available. This is the most important quality of the material that was investigated. Since the technical breakthroughs that have occurred in the ecommerce sector over the past decade, the management of supply chains has emerged as the primary focus of modern business enterprises. Because of the developments in the e-commerce sector, the field of supply chain management is now more competitive than ever, and logistics companies are in a constant state of competition with one another to maintain their lead. As a result of the competition, a new powerhouse has emerged in the sector. Although the idea of data analytics has been present since the inception of the trading business, the use of technology to compile, understand, and take action on the data is a rapidly evolving industry that calls for consistent innovation. The technology that we have access to today is not nearly as advanced as the technology that will be available tomorrow.

The negative aspect of this research is that it may become challenging to acquire sources that have been examined by other experts in the field. The academic field of logistics is vulnerable to rapidly falling behind the times due to the rapid pace of technological innovation. This industry boom is currently in its prime, and similar to the state of psychology during the days of Freud, there will be occasional gaps while academia works diligently to stay up with real-time information. This university is wonderful since most of the professors are still actively engaged in SCM. This helps a lot as we collaborate to remain competitive and is one of the things that makes this university stand out.


Big data analytics is like going to the grocery store to buy all the ingredients to bake a cake. However, Big data has yet to evolve to the point where it can use the data to ask all of the questions required to bake the cake. BDA has developed into a significant problem that needs solving in many domains, including SCM. There is plenty room for improvement in using the correct analytical methods in this field. BDA has essential applications throughout the supply chain, from beginning to conclusion. BDA is beneficial in a wide variety of applications that span the entirety of the supply chain. BDA is utilized in various supply chain activities and provides support for these activities. These activities include supplier relationship management, product design and development, demand planning, inventory management, network design, production, procurement, logistics, and distribution, as well as the reverse. The use of big data sources and analytics techniques has resulted in many enhancements to the processes involved in supply chain management. In addition, BDA can lend a hand in the formation and advancement of supply chains that are responsive, dependable, and environmentally friendly. In an intricate global supply chain, BDA can manage and integrate massive data sets of various types. Numerous researchers have used multiple BDA strategies in various industries, including healthcare, finance/banking, and manufacturing, among others, including these and many others. BDA strategies will also be helpful in multiple other sectors, including the hospitality industry, technology, the energy sector, and other service sectors. Within an organization, the decision-making process is heavily influenced by several elements, including its culture, politics, environment, and management team. Because of this, having sufficient resources that are also equipped with analytical capabilities has become one of the main issues for many supply chains in the modern day. To answer how data can help generate SCM results, the supply chain must establish close and continuous links between data experts and their business function, and then apply appropriate BDA techniques according to the context of their application in their decision making, processes, and activities. Therefore, it is necessary to establish mutual coordination and cooperation between the various supply chain units, to make use of BDA techniques effective, and to ensure that there is both the ability to share data and information as well as access it throughout the entirety of the supply chain.


Beatrice, Adilin (2021). All About the Basics of Big Data. Web.

Big Data Analytics. (2022). Web.

Chen, Daniel Q., Preston, David S., & Swink, Morgan (2015). How the Use of Big Data Analytics Affects Value Creation in Supply Chain Management.. Journal of Management Information Systems, 32(4), 4-39.

Duggal, Nikita (2022). Top 7 Bebufuts if Big Data Analytics and Reasons to Consider it for Your Next Career. Web.

Farmer, Donald (2022). 8 Benefits of Using Big Data for Businesses. Web.

Harvey, Cynthia (2018). Big Data Pros and Cons. Web.

Lawton, George (2022). 10 Big Data Challenges and How to Address Them. Web.

Mostaghel, Rana, Oghazi, Pejvak, & Patel, Pankaj (2019). Marketing and Supply Chain Coordination and Intelligence Quality. Journal of Business Research, 101, 597-606.

Rauch, Stuart (2022). Top Four Industries Using Data Analytics. Web.

Schlegel, Gregory L., & Trent, Robert J. (2014). Supply Chain Risk Management. CRC Press.

Weldon, David (2021). How to Build an All-Purpose Big Data Pipeline. Web.