Overview of Big Data and its Significance in Organizations

Question:

How Facebook Network Infrastructure can be impacted by the New Era of Big Data. 

Introduction

Agnellutt (2014, p23) defines big data as the gathering and analysis of large quantities of data with the primary objective of unearthing hidden intelligence or insights. Examples of data collected include machine data, sensor data, or user data. If organizations channel their resources and time in analyzing big data, they can venture into new markets, enhance their competitive advantage in the industry, and access new business opportunities (Gathegi et al. 2014, p.86). Big data compared to other data sources such as structured data is characterized by three unique attributes including velocity, volume, and variety (Janssen 2015, p.123). Big data comprises of both unstructured and semi-structured data including log files, click-streams, video, audio, and text. Big data also allow organizations to analyze their huge volumes of data in real time. For those organizations that have already embraced the technology are accruing numerous benefits compared to their counterparts. For instance, the financial service sector is utilizing big data technology to analyze their customers so that they can determine their qualifications for credit, mortgage, or credit (Abbasi, Sarker & Chiang 2016, p.10). Healthcare providers, on the other hand, are sharing and managing electronic health information of their patients from various sources and multiple practices. Regulatory agencies and pharmaceutical companies are tracking big data solutions so that they can provide their target audience with shorter and more efficient drug development procedures as well as track the efficacy of their drugs. Given the importance of big data in various organizations, the paper aims to analyze how Facebook company network infrastructure can be impacted by the new era of Big Data. 

Overview of Big Data and its Significance in Organizatchions

In the 1990s, the major information technology challenges that organizations faced is the recording and enabling of faster and more business transactions (Connelly et al. 2016, p.32). However, with technology advancement, organizations are focusing on speedy delivery of data to mobile devices, PCs, and systems, made possible by big data. Big data in the current business environment is used to extract value from huge volumes of data. For online and startup companies such as Facebook, LinkedIn, and Google, big data is something new. However, large firms perceive big data as something that enhances their innovativeness. Some organizations contend that they have fed their model and systems with big data, but are yet to experience something revolutionary about the technology (Frizzo-Barker 2016, p.405). Regardless, they are still pursuing big data because of its ability to assess distinct types of data sources and sets.

Firms that were initially used to handling large quantities of data are beginning to be enthusiastic about their capacity to handle various data categories including video, images, log files, text, or voice. For instance, a retail bank is now utilizing big data to handle interactions from their various customers by assessing their log files (Goes 2014, p.6). On the other hand, hotels are utilizing video analytics to analyze their customer lines whereas a health insurer is using the technology to envisage customer dissatisfaction using call center recordings to review speech-to-text data. Ideally, big data permit companies such as Facebook to have a clear overview of their operations and customers by mixing both structured and unstructured data. 

Challenges of Conventional Data Processing Applications and Management Tools

Throughout the history of business, important and successful decisions were based on the analysis and interpretation of data. As a result of the complexity and quantity of data being generated on a single day, conventional data processing applications and management tools have not been effective in data storage and analysis (Liu & Guo 2016, p.23). The major challenge with these conventional tools is their inability to share, search, store, capture, visualize, analyze, and transfer data. Big data compared to conventional information technologies can result in substantial reductions in the time needed to provide new services and products, carry out a computing activity, as well as reduce the costs of operations (Papadopoulos et al. 2016, p.42). It also plays an essential role in making decisions that affect the operations of the organization. Equally, big data are defined by advanced search capabilities and analytics. Businesses are also achieving various objectives through the implementation of big data technology. Some of these objectives include: empowering staff in various departments to analyze and explore information and consequently offer their views regarding the information. Big data is also providing significant insights regarding the strategies that organizations can apply to manage risk and enhance their business outcomes and optimize the decisions made by organization managers or automated systems. Therefore, big data is utilized by organizations such as Facebook to adapt to the transforming needs of their clients and to reshape them based on the industry that they are conducting their operations (Reed & Dongarra 2015, p.60).  

Overview of the Facebook Network Infrastructure

Facebook was designed with the primary objective of helping people share information and connect. The organization in the previous decade has invested in tools that are transforming people across the globe as they communicate amongst themselves. Additionally, the company has spent approximately one billion dollars in upgrading its network infrastructure that serves approximately 855 million customers across the world (Miller 2012, p.6). In 2011, the company spent about $ 606million on data centers, network gear, storage, and on servers. The company in 2012 increased its budget to $ 500million (Miller 2012, p.8).The company success is attributed to its massive storage and servers that work to allow users to create a Facebook page and load their home page. All these actions require the company to access numerous servers and process massive amounts of data.

How Facebook Network Infrastructure can be impacted by the New Era of Big Data

According to Tan (2015, p.225), companies that are gaining a competitive advantage in their respective industries have invested heavily in their ability to analyze, integrate and collect data from their different sales unit and stores. Additionally, they are linking the information collected to their suppliers’ database making it easier for them to adjust prices accordingly and re-order fast moving items in their stores automatically. The use of big data also allows companies to constantly synthesize, test and bundle information making it accessible to all organization employees including the chief executive officer. In this regards, big data helps in tracking sales and purchases as well as provide insights regarding the behavior of consumers in the market (Marr 2016, p.95).

Benefits of Big Data compared to Conventional Information Technologies

Regardless the investments made by the companies regarding the storing, collecting and analyzing data, the quantity of data has exploded in the previous few years. Facebook, whose staff exceeds 1000 on average, has approximately 235 terabytes of data which originate from the company’s customer interactions and financial transactions (Miller 2012, p.5). Data also flow from multiple points and new devices in the company supply chain. Additionally, the company’s sales and marketing department analyzes social media feedback from potential clients or utilizes position data from their Smartphone’s to comprehend their customer’s behavior while using the Facebook platform. Furthermore, employees and other stakeholders in the supply chain might be exchanging data. The availability of this new information in the company database exhibit implications for organizations and their leaders. For this reason, emerging literature posits that organizations that utilize business analytics and data to make decisions are very competitive and record higher profit margins compared to their competitors. Therefore, networked organizations in the era of big data are crucial in engaging suppliers and customers through information exchange in their websites. In this case, the new era of big data will have significant impacts on Facebook network infrastructure.

Big Data and Facebook Network Infrastructure

Data center infrastructures within the previous two decades have been developed in such a way that it closely aligns end users, applications, and data with the primary objective of providing the target audience with high-performance and secure access. However, these data center infrastructures have become common, and network administrators such as Facebook administrators are identifying ways that they can provide their end users with the needed resources to display, execute, and compile data (Morabit 2015, p.93). Before the emergence of big data era, Facebook network infrastructure greatly depended on the three-tier architecture. However, with the era of big data, Facebook network infrastructure will greatly transform. For instance, its data will be distributed horizontally across network nodes. Besides, its storage and server nodes will be considerably greater compared to the nodes between end users and servers. Similarly, the company through big data can generate facts by storage, application, or servers in contrast to an outside source such as system log files.

With the era of big data, Facebook network infrastructure will be characterized by discretely incremental and small data sets. Therefore, Facebook will be required to upgrade its conventional SQL tools or OLTP (online transaction processing) data stores because they are not compatible with big data (Juniper Networks 2012, p.32). As a matter fact, big data needs a horizontally scalable and flat database defined by extraordinary query tools that operate on actual data instead of delineated snapshots. The following table gives a comparison between big data and conventional data.

Components	Big Data	Conventional Data
Data model	Schema-less	Fixed schema
Data relationships	Unknown/complex relationship	Known relationship
Data type	Semi-structure or structured	Transactional or structured
Data volume	Exabytes and petabytes	Terabytes
Architecture	Distributed	Centralized

Based on the above table, it is obvious that the era of big data will have significant implications on Facebook network infrastructure. For instance, the company might be forced to design a network infrastructure that is more horizontally scaled and flexible to control big data capabilities and tools. The design will be enabled using the Hadoop Cluster technology.

Facebook’s Investment in Network Infrastructure for Big Data

The above technology is made up of a distributed file system acknowledged as MapReduce and Hadoop Distributed File System (HDFS). Through this technology, Facebook can create a scalable file system that provides a platform for data management, access, and quick query. Once this technology is implemented, four distinct nodes will be created throughout the Facebook infrastructure including job tracker, name node, client node, and data node. The name code is similar to the address router utilized in the implementation of big data. The node is tasked with the responsibility of maintaining the location and index of each data node. A client node, on the other hand, is the user interface and might be a personal computer with a conventional user interface or server. Data nodes store data and are made of numerous smaller database network infrastructures (Mohanty, Bhuyan & Chenthati 2015, p.56).

Big data will provide Facebook network infrastructure with an opportunity to analyze and capture huge volumes of data. As such, it is vital for the Facebook network operator to take into account the impact of big data era on their operations, networking, storage, and service infrastructure. By understanding this, the company network operators can improve their network infrastructure so that it can support volumes of data demanded by users. To effectively implement big data solutions, the Facebook network administrator must provide answers to the following set of questions: Whether big data application will need integration with and access to other available applications in their data center?; whether the data sources will be utilized in the same manner or differently as the available production data sources and business insights that the company is attempting to realize. Answering these questions will provide Facebook network administrators with an avenue to discuss with external software and hardware vendors as well as internal constituents. All in all, Facebook should keep both big data and traditional analytics to generate a new synthesis. However, to benefit from big data, the company needs to change its architectures, technologies, organizational structures, and leadership and employee skills. 

Conclusion

Conclusively, the implementation of big data will permit Facebook to be innovative and enhance its competitiveness in its industry. Therefore, it is imperative that every Facebook network administrator is active in designing the company’s network architecture so that it can boost big data analytics. The implementation of big data will reduce the company’s operational costs and improve its decision-making process. Additionally, network administrators are expected to identify the network attributes that can support various configurations such as Hadoop clusters.  

References

Abbasi, A., Sarker, S, & Chiang, R., 2016. Big Data Research in Information Systems: Toward an Inclusive Research Agenda. Journal of the Association for Information Systems, 17(2), pp. 1-13.

Agnellutti, C., 2014. Big Data: An Exploration of Opportunities, Values, and Privacy Issues. New York: Nova Publishers.

Connelly, R., Playford, C., Gayle, V, & Dibben, C., 2016. The Role of Administrative Data in the Big Data Revolution in Social Science Research. Social Science Research, pp.30-63.

Frizzo-Barker, J., Chow-White, P., Mozafari, M, & Ha, D., 2016. An Empirical Study of the Rise of Big Data in Business Scholarship. International Journal of Information Management, 36, pp. 403-413.

Gathegi, J. N., Tonta, Y. A., KurbanoğLu, S., Al, U, & TaşKin, Z., 2014. Challenges of Information Management. Berlin, Heidelberg: Springer.

Goes, P. B., 2014. Big Data and IS Research. MIS Quarterly, 38(3), pp. 3-8.

Janssen, M. F. W. H. A., 2015. Open and Big Data Management and Innovation. Cham, Springer.

Juniper Networks., 2012. Introduction to Big Data Infrastructure and Networking Considerations: Leveraging Hadoop-Based Bid Data Architecture for a Scalable, High-Performance Analytics Platform. 

Liu, H, & Guo, G., 2016. Opportunities and Challenges of Big Data for the Social Sciences: The Case of Genomic Data. Social Science Research, pp.40-56.

Marr, B., 2016. Big Data in Practice: How 45 Successful Companies used Big Data Analytics to Deliver Extraordinary Results. Hoboken: Wiley.