Motivation and Background

Chapter 1. Introduction

1.1 Motivation and Background

Moore’s Law states that processor speed, or overall process power for computer will be doubled every two years. This same logic can be applied to technology and engineering as well.

In the 21st century, internet, software and hardware are evolved tremendously rapid. According to Institute for Information Industry, there are approximately 49.5% of smart devices in Taiwan, which mean every two people will own at least one smart device. This report indicates that the internet has become essential in the 21st century. In addition, everyone and everything can be, also will be connect to the internet. From that, many of the traditional industries are forced to change themselves tremendously, and new innovative businesses have emerged accelerated. For example, People usually go to traditional supermarket for grocery shopping. However, ever since Amazon was founded in 1994, the behaviors people shop for consumer goods has become

incredibly different. With Amazon’s two days delivery and enormous variety of product lines, people purchase their consumer goods from Amazon instead of traditional retailers. There are additional examples such as Uber, Yelp and Airbnb, which they all have one common advantage.

They are all connected to the internet.

Banking is one traditional industry that has not yet been evolved dramatically, but faces huge challenges. Because of the internet and smart devices are broadly available, most of the people can access and transact their bank accounts through the internet. Smart devices allow people to handle their daily financial needs without going to an actual bank. “This is the way banking will be done from this day forward, without exception. We’re never going back to a world without internet banking access, mobile phones, social media and multi-touch” (King,

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2012). Therefore, financial industries need to refocus the ways they communicate with customers and investors. Furthermore, financial industries need to be prepared for the internet era.

1.1.1 Big Data, Big Value

The words “Big Data” are emphasized extremely frequent in the past few years. Big Data is not a new technology or strategy. In fact, Big Data does not simply mean the size of the data, but more importantly the strategies and the engineering applied within. For instance, text-mining and machine learning are part of the technical techniques in Big Data. Moreover, Most of the enterprises are able to store and collect huge amounts of data because the cost of hardware declined periodically. In addition, cloud computing technology is improved enormously for the consumption of computing resources. Therefore, a lot of Big Data applications are available such in retailers, medicals, telecommunications and governments.

Big Data includes structured, semi-structured, and unstructured information from demographic and psychographic information about consumers to product reviews and

commentary; blogs; content on social media sites; and data streamed 24/7 from mobile devices, sensors, and technical devices. The digital era is pushing the financial institutions on many fronts, such as customer data, market expectations, and operational efficiencies (PWC, 2013). Therefore, processing increasingly large volumes of data in a timely manner has become a major challenge for financial institutions. Financial institutions should focus on using Big Data to get the right information to identify the right markets and customers at the right time, which enable institution to make the right strategic decisions. In fact, 62% of companies believe that Big Data has

significant potential to create competitive advantage (PWC, 2013). Moreover, the Big Data market is at 5.1 billion in 2012 and is expected to grow to 32.1 billion by 2015 (Alspach, 2012).

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1.1.2 Implementing Big Data System for Enterprises

Financial institutions must find a new way to analyze and interpret their customers. More importantly, they have to be ready for the internet era and the Big Data boom. Therefore, implementing a Big Data system is essential for the financial institutions. However, many problems and difficulties will occur during the implementation. For instance, in 1990,

Enterprises Resource Planning is first introduced by the Gartner Group, and the ERP system is defined as seamless integration of all information through a company. The system provides integration includes, but not limit to financial information, human resource information, supply chain information, and customer information (Davenport, 1998). There is no surprised that the system solves a lot hassles for companies. For instance, many managers struggle with

incompatible information systems, and inconsistent operating practices. Therefore, ERP system provides easy solutions and consistent operating practices across all departments effortlessly.

Besides the fact that ERP systems offer various benefits, many problems and difficulties can occur during the implementation because every company is distinctive. ERP implementation can be categorized in three processes, which are pre-implementation (Setting-Up),

implementation and post-implementation (Motwani, Subramanian, Gopalakrishna, 2005). In addition, each category has several key components to be considered before ERP implementation.

For example, during the pre-implementation, company should have clear understanding of strategic goals for ERP. Moreover, the commitment from top-level managers is also important during the pre-implementation stage. After the first stage, companies have to choose which ERP package selection that best fits with their current business models. According to the article

“Critical factors for successful ERP implementation” by Motwani, IT Leveragability and knowledge capability are important during the implementation stage. Furthermore, company

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should assemble an extraordinary team to lead the implementation and monitor on the progress.

Lastly, companies usually evaluate their ERP implementation by monitoring their ERP system’s implementation. In the post-implementation stage, companies can be more adaptable to the change of programs and adjust their system by continuously monitoring to derive the maximum benefits from ERP.

By taking a glance back to the ERP era, companies understand ERP provides beneficial contributions to the company. However, many problems and difficulties can be occurred during the implementation. Companies need to consider several components include, but not limit to clear business objective, comprehension of the nature of changes and understanding of the project risk (Mandal, Gunasekaran, 2003). In addition, strong leadership and constant watch to budget are essential as well (Wagle, 1998).

From the previous descriptions and reasons, the implementation for the Big Data system can be both beneficial and rewarding in compare with ERP system. However, it can have several challenges also similar to ERP. Therefore, this research explores on questions such as what processes are needed or developed for financial institutions implementing the Big Data system.