The Application Of Big Data In Retailing

Published Date: 02 Nov 2017

This research intends to identify the definition of Big Data, the opportunities and challenges in implementing Big Data in the retailing sector. This research had been done by reading and reviewing previous writings about Big Data. Results found out after numerous readings show that Big Data is appropriate to be used in retailing and that it could open lots of opportunities such as increase in retailers’ population, how big their chains could go and the grow of range of products. However, where there are opportunities, there bound to be challenges, such as the specialists who could manage and maintain Big Data application in the sector. There are also the challenges in choosing the appropriate hardware and software required to gather and analyse the Big Data.

Keywords: Big Data, opportunities, challenges

Introduction

Big Data is collection and processing of very large data sets and associated systems and algorithm used to analyze the massive datasets (Begoli & Horey, 2012). Big Data spans to five dimensions, IBM mentioned four, namely volume, velocity, variety and veracity (http://www-01.ibm.com/software/data/bigdata/). The fifth dimensions are value (http://www.genengnews.com/gen-articles/big-data-brings-big-challenges/4612/).

Retailing is a sector where people trade with each other with variety of products or items and services, globally. It is a sector where people need to be alert and aggressive to survive. Retailing has undergone various changes in terms of administration and the way it is carried out due to various researches and personal experiences.

Implementing Big Data into any sector provides challenges that need to be faced and be accustomed to. In retailing sector, there are actually plenty of opportunities and challenges that need to be grasped and faced with a common purpose, which is to broaden and provide variety to the retailing sector. Big Data will eventually be a big help into the sector, where administration would be easier and become more efficient.

Review of Big Data

Literally, big is referred as considerable of size, number or quantity and data is referred as factual information, especially information organized for analysis or used to reason or make decisions (http://www.thefreedictionary.com/). Big Data itself can be defined as data sets that are too large and complex to manipulate or interrogate with standard methods or tools (http://oxforddictionaries.com). In the real world, data is not just a big set of random numbers; it tends to exhibit predictable characteristics and for one thing, as a rule, the largest cardinalities of most datasets are small compared with the total number of observations (Jacobs, 2009, p39).

According to Walter (2013),

The increase in data volume is so fast that it is astonishing to find out that we create more than 2.5 quintillion bytes of data every single day, according to IBM research. It does not stop at this growth rate; every second the growth rate of data increase is faster than the previous one. Comparatively, we create more data during every new second than in the previous one. This is very interesting as it means that 90 percent of today’s total data was created in the last two years! This trend will prevail in the the future too. As per normal trends since last three decades, the data volume doubles after every three years or so.

Big Data is not only just the matter of the data in size, but Big Data also has characteristics such as comprehensive, integrated, minimal maintenance. Integrated is about simplify and acceleration of introduction Big Data technology to enterprise and enable integration with information supply chain including databases, data warehouses and business intelligence applications (Singh,S., Singh, N., 2012).

In every industry sector, data is very important factor for their daily production activity. In addition, they stated Big Data create some values to the business sector in order to develop or to transform the business model, products or services to be more innovative. According to McKinsey Global Institute (2010), some issues were rose up in dealing with Big Data. The obviously seen issues is the data policies that requires a lot of storage and it start from petabytes (PB) and turn to rise over zetabytes(ZB) even more. The study from IBM found in 2000, the data stored were about 800,000 petabytes (PB) and it will increase to almost forty zetabytes (ZB) in 2020. However, Big Data also as new growth opportunities to industry sector in order to analyze data. Many of the major players of the industry sector will be as a middleman in order to obtain the information data flow about their suppliers, buyers, customers, products and service and many more in order to capture and make some analyze about the information gathered as claimed by McGuire, Manyika, and Chui (2012). In addition, according to Walter (2013),

Big Data will not only keep increasing its volume but the expected increase is much more than present trend. Many people in the world have still not started using the Internet; the Big Data will increase drastically once those people start using the newer technologies and the Internet. This means the demand of data use of the existing users is increasing very rapidly. Smartphones and wireless technologies are fuelling the rapid swelling up of Big Data of the world.

Big Data Application in Retailing

Retailing has literal definition as the functions and activities involved in the selling of commodities directly to consumers (http://www.thefreedictionary.com/). Examples of retailing activities are department store, kiosk or subordinated services such as delivery. One of very popular company in retail is Walmart. According to Kelly (2013),

Walmart pioneered the use of Big Data to improve operational efficiency in the retail industry well before the term Big Data even existed. The company understood that by harnessing the power of data, it could streamline its complex supply chain to take advantage of economies of scale, thus limiting excess inventory and reducing associated costs. It passed on some of these Big Data-enabled savings to customers in the form of low prices that (in some cases significantly) undercut the retailer's competition.

Opportunities

There are a lot of opportunities for Big Data in retail sector, such as dynamic pricing, staffing, personalization and predictive analytics. Dynamic pricing is about "retailers are using Big Data-backed techniques to dynamically price goods and services, both online and in stores" (Kelly, 2013) and to dynamically price "This requires taking data from multiple sources, such as competitor pricing, product sales, regional preferences, and customer actions to determine the right price to close the sale" (Mehra, 2013).

Another important thing is about staffing. "Retailers, particularly national and multinational retailers with diverse and geographically dispersed workforces, have long struggled with optimizing in-store staffing. Many factors impact staffing requirements, including weather forecasts, promotional campaigns, time of year/month/week/day" (Kelly, 2013).

Eric Williams answered in interview to describe one of the opportunities,

For example, companies want to be able to understand as quickly as possible if the launch of a new product is working. Given that there are more than 20,000 new products introduced in the US marketplace annually, products like new flavors of gelatin or yogurt, this isn't easy. It used to take months to understand how well a new product was doing by looking at sales data, and adding surveys and exit interview data. By pulling in detailed sales data and using analytics, companies can figure out in a matter of days whether a new product is a success, and quickly decide to expand the launch or discontinue it. Now these companies can literally have access to that data in a matter of days after the launch of a product. (http://www.sas.com/news/sascom/big-data-in-retail.html)

Challenging

The basic challenges that be related with Big Data are volume, velocity, variety and veracity. These challenges can affect and exist when Big Data is used in retail sector. Mehra (2013) explained that volume challenge will exist if most businesses in retail generate much more data than what their system were designed to handle; Velocity challenge exists if a company’s or retailer’s data analysis or data storage runs slower than its data generation; Variety challenge will affect and exist because of the need to process is different types of data to produce the desired insights. Veracity challenge establishes trust because "1 in 3 business leaders don’t trust the information they use to make decisions" (http://www-01.ibm.com/software/data/bigdata/). As for value challenges applies to deriving valuable insights from data (Mehra, 2013).

Conclusion

As a conclusion, Big Data is a feature or an element of opportunity maker. It creates lots of advantages although there may be some disadvantages to it. Big Data would also create a new field of professionalism in the world of IT. As far as retailing sector is concerned, Big Data will further push the limits of what the sector may do or get to. It may help both the entrepreneurs and the customers. Big Data will also improve the performances of statistics, management, administration, software, hardware, human skills and the community as a whole. Since the goal of IT is to make humans’ lives easier, Big Data is definitely one of the means to achieve that goal.