Is Big Data Analytics A Supply Chain Just More Hype? Justify Your Answer

Since the 1970’s databases and report generators have been used to aid business decisions. In the 1990’s technology in this area improved. Now technology such as Hadoop has gone another step with the ability to store and process the data within the same system which sparked new buzz about “big data”. Big Data is roughly the collection of large amounts of data – sourced internally or externally - applied as a tool – stored, managed, and analyzed - for an organization to set or meet certain goals.

Technology companies are using big data to analyze millions of voice samples to deliver more reliable and accurate voice interfaces. Banks are using big data techniques to im- prove fraud detection. Health care providers are leveraging more detailed data to im- prove patient treatment. Big data is being used by manufacturers to improve warranty management and equipment monitoring, as well as to optimize the logistics of getting their products to market. Retailers are harnessing a wide range of customer interactions, both online and offline, in order to provide more tailored recommendations and optimal pricing.96

Despite the potential challenges, it is still believed that big data is the new way of the world, and that businesses should aim to overcome these challenges. The first typical challenge small businesses will face is finding “talented people who know big data and analytics” (Taylor, 2012, para. 6). Unfortunately, since small business owners tend to not be familiar with big data, then they will be challenged to know how to use it. Big businesses have the money, reputation, and influence to hire people who are very knowledgeable with big data. Also, if the storeowner is technologically challenged, it’ll be even harder for him/her to know how big data can help him or even what big data is in order to go out and look for someone to help his/her store with big data. Even if the storeowner is comfortable with technology and is familiar with big data, the storeowner could be averse to change, and that would be another reason why using big data would be a challenge for this category. The storeowner who is used to doing things a certain way will not be eager to start using big data to improve his business. S/he will probably think that if the business is doing well, then they don’t need to change. This will prevent them from increasing profits and will stagnant them from keeping with current times and current technology. For

Savvas (2015) stated that the reasons why Marks and Spencers uses big data because it helps them to gain a better understanding of the customers of multichannels. It also helps them to understand more about the shopping habits of their customers, which in turn helps them to understand about inventory turnover. The higher the inventory turnover, the better their sales are. Marks and Spencer also uses big data because it helps them to answer key questions about IT support, even if they do not have any IT backgrounds, and at the same time, it helps empower their

Supply chain networks are important because they help company’s remain consistent with the consumer buying pattern changes. Companies often seek new channels to market their product. The company utilizes analytics to identify different ways to present their product to the consumers and how the supply chain would need to operate for each case. They determine the optimal strategy to determine the overall strategy based upon the product being produced. For example, the supply chain for a tablet will differ from that of a candy bar. The delivery of the tablet is produced on a time sensitive basis because of the ever changing demands of the high tech consumer market. The supply chain network design allows for organizations

Today, the world’s trend in operating business focuses on data availability to enact the best suitable decision to improve, develop, and increase business revenues. Moreover, the availability of data helps to monitor and control the quality of provided products or services. However, the availability of data without proper analytics operations would have no meaning (1). Data analytics provide an important aid to an organization to figure out their position in the market in comparison with their competitors. Also, data analytics helps to identify what is the organization’s competitive ability in the market, what they should bet on, and what they should strive for. With that being said, many of today’s most successful organizations utilize

With “big data” and analytics being a hot topic as of late, drawing headlines like “The Sexiest Job of the 21st Century: Data Analyst” (Morris, 2013), it’s clear that this isn’t just a fad passing through. Data analytics has been around for a while now, but it is finally getting the recognition in deserves by many businesses. More businesses are seeing the value in using their data to make better business decisions, and one method of doing that is through using predictive analytics. According to Teresa Meek, “Predictive analytics can improve retailers’ pricing, inventory control, customer service, and ultimately, their bottom line” (2015). This paper is going to focus on looking at the different types of predictive analytics in relation to the retail industry. After highlighting the techniques and different software available to do predictive analytics, different retail businesses will be examined on how they are currently using predictive analytics as well.

In a fast paced, business ordinated technological world the overall welfare of a company is tied to the success or failure to make the tough decisions. On one instance a company’s CEO might be able to make the choices based on experience, advice, or simple gut instinct. However, this is not the only skill one needs. There is a great deal of information to be found in being able to see investments in data and analytics. These decisions are based off of big data. Big data is a catch-phrase, used to describe a massive volume of both structured and unstructured data that is too large to process using traditional database and software techniques. The volume of data is in most cases is too big, moves too fast or it exceed the processing capacity the company has. Despite these potential drawbacks, big data contains the potential to help companies by improving operations and making faster, more intelligent decisions. This can be broken into three key parts, knowledge, data, and information.

This article is about several trends in the supply chain in the year 2012.Throughout 2012 there were evolutions in the supply chain technology due to companies' realization that a supply chain that is highly visible is necessary for success in the business climate today. Generally, IT trends like big data; cloud and mobile have greatly impacted the supply chain industry. Apart from these IT trends there are specific trends which were in the forefront in 2012.All these trends are related to various aspects of the supply chain.

As stated earlier, big data is imperative for a business to succeed. More data results in more accurate analysis. More accurate analysis results in better decision making. Better decisions result in more efficient operations, cost reductions, and diminished risk. More efficient operations, cost reductions, and diminished risk result in an increase in revenue and a more engaged audience. Let’s take a look at exactly how data can help a company.

Given how turbulent global economies are and the industries that compete within them, it is understandable to see analytics and Big Data continually increase in popularity throughout enterprises globally. The greater the level of turbulence in economic conditions, the more reliance on technologies, processes and systems that are adept at mitigating risk. The continual investment in analytics is setting a solid foundation for completely redefining how businesses manage the decision making process. It is also changing forever how businesses manage customer expectations relative to experiences, and how these factors are all combined to manage the new product development and introduction (NPDI) process.

The amount of data in our world has been rapidly increasing and analyzing these large data sets, or big data, has become crucial for businesses in increasing their success. Many businesses use big data to model their business structures, control processes, and run the business. The availability of this data leads to a more accurate analysis of the target market. More accurate analyses lead to more confident decision making and better decisions means greater operational efficiencies, cost reductions and reduced risk. There are many ways in which big data can be successfully implemented in an organization. Big data allows businesses to segment their target market, creating more precisely tailored products and services. Big data is also used to conduct controlled experiments to make better management decisions. Finally, big data can unlock value by making the captured information transparent and usable at much higher frequency (Manyika, “Big data: The next frontier for innovation, competition, and productivity”).

Firstly, the main problem is deciding which data should be selected. The data, explaining customers’ desires and needs, is important to be collected while most of the enterprises are confusing about what data they should concentrate on. A recent Gartner report (2014) stresses that 64% of firms raced to plan or launch a Big Data project, though they did not have enough professional knowledge yet. To understand what customers need through Big Data possibly turns into the core of companies’ target. The large data volumes and different varieties of data lead to data complexity.

Big data is an extensive collection of structured and unstructured data. It is a modern day technology which is applied to store, manage and analyze data that are not possible to manage, store and analyze by using the commonly used software or tools. Since all of our daily tasks are overtaken by the modern technologies and all the businesses and organizations are using internet system to operate, the production of data has increased significantly in past

A relevant case for using big data to analyse company’s situation is IBM. IBM created a software called Counter Fraud Management Software, which used to check streamline data then assist the entity to detect the fraud. The process of detecting fraud is based on the big data analysis ( Scott, n.d.). If the software refer to big data can be used prevalent, that will be a significant reformation for auditing, but still can not replace the auditor’s work because auditors can summarise data and predict the future instead of merely checking numbers. Another case of KPMG explained how to apply big data reasonably during auditing process. In 2016, KPMG adopted the big data power to achieve blueprint of the company, to be specific, the environment of financial health(EY,2015). For this aspect, using big data to get big picture of the industry could assist auditors during pre-engagement, planning and so on. Another example of KPMG provided more details about using big data. A large supply chain in production companies will lead to many problems such as the company cannot take action instantly since the large chain reduces the sensitivity of the company in market. Otherwise the entity could adopt the big data to compare and make decision. Therefore, auditors are capable to use the information to make judgement about the context of the company, then auditors should find business risks of the company (KPMG,2017).