Questions
- Knowledge and Understanding: Demonstrate understanding of the nature of research and different research methods2. Knowledge and Understanding: Identify and critically review literature from a variety of sources and organise this information
3. Knowledge and Understanding: Demonstrate the broad methodological knowledge and practical skills required for the placing of research, data collection and analysis, with an appreciation of how methods link to reviews
4. Intellectual, practical, affective and transferable skills: Communicate research findings in a professional manner.
Solution
The Effect of Big Data on Business Financial Analysis and Management in the Real-Estate Industry
Big data is now a common concept in business, with implications on the way companies conduct their operations, including management of their finances. Individuals have become highly dependent on data from numerous sources, within and outside the business. The nature of the data makes it critical for social computing and affects decision-making at different levels of the company. Therefore, businesses have to develop big data analytical capabilities to be able to collect, and manage the vast amounts of data. They need complex information systems that can collect the data, process it, and present it for decision-making at the diverse levels (Frizzo-Barker, Chow-White, Mozafari, & Ha 2016). The data emerges from different sources; thus, they require a mechanism to determine which is useful to the business and which they can discard, and still perform efficiently (Erl, Khattak, & Buhler 2016). Various actors within the company benefit from the big data. However, research is limited regarding the perceptions of the impact of the data on financial analysis and management in the real estate market. Available resources focus on the far-reaching effect of big data on the business. Thus, the current research will bridge the gap by researching within the real-estate industry to explore the impact of big data on their financial analysis.
Background
As more companies assume the data-centric approach to their business management, they are increasingly using big data potential. Many companies in the real-estate industry are generating vast amounts of data during their daily operations. They also use external sources of data, such as market performance and customer preferences (Galloppo & Previati 2014; Erl, Khattak, & Buhler 2016). They rely on big data, which depends on their ability to analyze it and present it to numerous users in a meaningful format. Interestingly, the use of big data will continue to increase in the future, which means that companies will need to work even harder to catch up with the trend and ensure that the trend supports their operations instead of becoming a barrier. The number of businesses using big data is expected to grow two-fold soon (Yu et al. 2015). The impact will be huge in different companies, but the most affected will be those that depend on market analysis to be effective, such as the real estate market. Therefore, it is necessary to understand the role and impact of big data on specific companies within different sectors, such as real estate.
The importance of the proposed study is to establish the effect of big data in the financial analysis of real-estate companies. The firms in the industry are information-intensive since they have to analyze, and understand the market to make their decisions (Fogelman-Soulié & Lu 2016). For example, they collect data about market performance and changes in consumption trends within the market to adjust their operations accordingly. Changes in the level of income of the consumers can have a considerable effect on their performance, either positively or negatively, depending on the direction of the change (Frizzo-Barker, Chow-White, Mozafari, & Ha 2016; Sharef, Zin, & Nadali 2016). Consequently, they should learn how to generate and analyze big data to improve their market and financial analysis. The proposed study will answer the question: How does big data affect business financial analysis and management in the real estate industry? The answer to the question will help businesses in the industry to improve their analytical procedures because the data continues to become even bigger in the future.
Literature Review
The literature review focuses on the research conducted so far on the use of big data in business. Numerous authors have explored the subject since the development of the concept of big data resulting from the emergence of computers and the internet (Erl, Khattak, & Buhler 2016). They have access to huge amounts of data at their disposal. Success in business depends on the way they analyze and make the data useful to support their operations. The review begins with a definition of the concept of big data and proceeds to explore big data analytics. The review will narrow down to the effect of big data in financial analysis and management and further down to the impact on real-estate. It will compare the research that other authors have conducted to answer the research question, consistencies and inconsistencies in their work, and identify the gaps to be filled by the current study.
Big Data
Big data is a common concept in the world of business today. Marz and Warren (2015) reveal that although the concept has its genesis from the world of technology, its impact has become far-reaching and influences the operations of businesses in all sectors. The concept of big data emerges from the term data, which suggests the quantities, characters, or symbols on which computers perform operations, which they could store or transmit to other computer systems, electronically or recording in external memory (Gandomi & Haider 2015). Therefore, Kitchin (2014) moves a step further when defining the concept of big data. The article suggests that while big data is simply data from a conventional sense, it has a new defining characteristic, and a huge size. Chen and Zhang (2014) add that big data is a concept that scholars and practitioners apply in describing a collection of data in vast amounts, and continuing to grow exponentially. The term suggests that the data is so huge that conventional data processing, and management tools might not process or store efficiently.
Research has focused on the characteristics of big data that helps workers to understand its nature and usefulness in their companies. Chen and Zhang (2014) suggest the 3Vs as the defining characteristics of big data. They suggest the huge volume of data in various settings, the data’s wide variety in computer systems within and outside the company, and the velocity, which is the speed at which businesses are generating, collecting, and processing the big data. Doug Laney first identified the characteristics of the big data, before a Meta Group Inc.’s analyst advanced them further in 2001 (cited in Kitchin 2014). They were further popularized by Gartner following the acquisition of Meta Group in 2005. Some scholars have added new Vs to the original three, veracity, value, and variability. Gandomi and Haider (2015) suggest that big data does not suggest any particular amount of data. However, the deployment and use of big data usually entail terabytes (TB), petabytes (PB), and even exabytes (EB). It is a massive volume of data that a company is capable of collecting and managing overtime. Notably, many companies have developed the capability to collect, process, and manage big data to achieve their business and organizational objectives.
Big Data Analytics
Companies should have the capacity to analyze their big data in a meaningful form to become useful to their business decisions. The capability informs the need for effective big data analytical tools. They usually collect the data in various formats, some of which cannot make sense without further analysis. Big data combines structured, semi-structured, and unstructured data that the organization collects from numerous sources. According to Chen and Zhang (2014), the usefulness of the data depends on the ability of the company to mine it for information, and use analytical capabilities to make it meaningful to the business, such as in supporting decision-making. Companies use various data analytics tools, such as machine learning programs, predictive modelling, as well as other analytics applications, depending on the type and nature of the business. Gandomi and Haider (2015) further reveal that systems for processing and storing big data are an important part of companies’ data management architecture. Businesses are investing in the capability to process and use big data to gain a competitive advantage in the market.
Big data analytics plays an important role in the ability of the company to benefit from it. The analytical processes examine vast volumes of data to reveal concealed meanings, patterns, and correlations between the different pieces or sources of the data. Modern technology makes it possible to perform data analysis and get useful information in real-time (Kambatla, Kollias, Kumar, & Grama 2014). Gandomi and Haider (2015) concur that modern business analytical tools have addressed the limitations of traditional business intelligence solutions. Companies analyze the data accumulated in their systems to create sensible solutions for various aspects of their businesses (Hu, Wen, Chua & Li 2014). As a result, companies are investing in complex data analytics tools to gain a competitive advantage. They also reveal considerable cost savings when they generate useful business intelligence to make various decisions affecting their businesses. As a result, big data analytics has become a new source of value for companies in all sectors, including real estate. Those avoiding the investment are at the risk of business failure since they compete in the same market with businesses with major big data analytical capability.
Impact of Big Data in Business
Businesses are using big data to achieve a competitive advantage. They have numerous applications of the vast amounts of data to support their operations, from production to marketing. Erevelles, Fukawa, and Swayne (2016) reveal that firms, are using big data to meet customer needs and preferences, and increase profitability through the growth in the capacity to study and understand their markets. Akter et al. (2016) further reveal the benefit of making fast and accurate decisions through the use of powerful big data analytical tools. They ensure that the management has the necessary information at the right place and time to decide promptly. They also improve their marketing skills, and campaigns as a result of more informed understanding of their customers, and market segment.
Impact of Big Data in Financial Analysis and Management
Companies, including those in real-estate, use data and information to conduct financial analysis and management, as well as provide the executives with adequate knowledge to make timely and accurate decisions. As a result, Akter et al. (2016) suggest the expected benefit of big data and big data capabilities in improving financial management by taking advantage of internal and external sources of the data, as well as the structured, semi-structured, and unstructured data. However, while many companies, including those in the real estate industry, use their big data capabilities for marketing purposes, many have neglected the importance of the vast volumes of data in their financial management (Hu, Wen, Chua & Li 2014; Begenau, Farboodi & Veldkamp 2018). Many firms use structured information within their companies for the analysis and to make financial decisions affecting their businesses. Srivastava and Gopalkrishnan (2015) add that for many other companies, the use of big data to support all their operations is a work in progress, which means that they are yet to take complete advantage of big data to support all their operations.
‘Big data has huge implications on financial management, depending on a company’s ability to collect, process, store, use, and even share the information and knowledge generated from the structured, semi-structured, and unstructured. Besides, big data is affecting many professionals in the workplace besides the marketers. Accountants and finance professionals are increasingly relying on various sources of data to analyze and manage their firms’ financial performance. Srivastava and Gopalkrishnan (2015) conducted a survey in companies to ask accountants and finance professionals regarding their use of big data in the workplace. They focused their study on who, and what drives the use of big data, the areas of application of the capabilities, and the stage of application of big data and analytical tools. The results of the study match others that reveal some extent of the use of big data in companies’ finance and accounting departments. Still, huge gaps compared to the application in other departments, such as marketing. Therefore, research reveals considerable opportunities for firms, such as in the real estate, to increase their use of big data and analytical capacity.
Gaps in Research
Research has focused on the role of big data in businesses operating in different sectors. However, most of the research on this topic focuses on the impact of big data on marketing since companies use their knowledge, and intelligence to understand the market, and make better and more correct marketing decisions. Probably, businesses focus more on marketing due to its impact on the success of their operations. However, research remains limited in the role of big data and analytical capabilities in improving financial analysis and management in companies such as within the real estate industry. The financial management aspect of a business is as important as marketing, which informs the importance of the current research to understand how big data affects the function, focusing on the real estate sector. The results will not only address the gap in research but also help business practitioners to improve their use of big data capabilities to gain a competitive advantage and increase profitability.
Although companies, including their finance managers and accountants, could benefit from big data, they lack relevant knowledge of its analysis and interpretation. Furthermore, the data is too large that it causes challenges for users if they lack knowledge and tools to analyze it effectively. The huge chunks of data are unusable in an environment that lacks the means to collect, analyze, and understand the information. Despite the importance of the knowledge, recent research remains limited surrounding big data analytics and use for finance management. As a result, the findings of the proposed research will have an impact on the use of big data in financial analysis and management in real estate companies.
Research Design and Methodology
The section outlines the proposed research design, and the methods that the researcher will use to collect the data. The study will use a qualitative approach to collect data from real estate companies in the country. The specific paradigm that informs the study is interpretivism, which suggests that people do not just react to their social environment. The paradigm borrows from ‘social action theory, ‘which suggests that people shape their social environment, and that their experiences are socially-constructed (Montano & Kasprzyk, 2015). The study will use a qualitative model to collect data from participants due to the need to understand their perception of the extent of the use of big data in their financial analysis and management. Moreover the study will use interviews, which will provide the qualitative data for analysis and to provide the general picture of the use of big data in real-estate companies.
The source and the size of the sample are critical since it provides the number of people who will provide data for the study. For the current study, the population of the study will be accountants and finance professionals from real-estate companies. The study will use a purposive non-probability sample since all accountants and financial managers in the target companies will provide the data. The study will collect the data from five real estate companies in the same city who will have one chief accountant and one finance manager. Therefore, the total sample will have 10 participants. The researcher will collect data from the ten to have a detailed understanding of the extent of the use of big data in real estate companies.
The data collection process will provide the information that the researcher will analyze and use to inform findings and conclusions. The data collection method in the study is interviews with the ten participants. The researcher will prepare an interview schedule beforehand, and test it in a pilot study to determine its reliability and validity. After the pilot study, the scholar will make relevant changes to the interview schedule before collecting data from the ten participants. In addition, the study will occur in the organizations to prevent disrupting their daily tasks at their workplaces. The interviewer will conduct face-to-face interviews with the ten participants who will record the responses using an audio recorder for later transcription and analysis.
The scholar will analyze the qualitative data using thematic content analysis. The first step in the analysis is to conduct a transcription, which involves listening to the responses in the audio-recorder and recording them on paper or using a computer package, such as a word processor. The researcher will identify common themes, and report the findings through the categorization of the identified themes. Thematic analysis is the most appropriate method for analyzing research interviews (Brewster, Velez, Mennicke & Tebbe 2014). The method presents data in related themes and allows comparison of findings to identify further gaps
Ethical Issues
The researcher will consider numerous ethical issues that might emerge in the study. One of the ethical considerations is the need to complete an ethical approval form. The form will be completed and signed by the ethical board or management of the target companies to affirm that they have given their permission to conduct the interviews in their workplaces. The second ethical issue that the researcher will consider is informed consent. Participants in the study should agree to participate without being coerced. Therefore, signing the consent means that they will participate voluntarily. The scholar will conduct a briefing to explain the purpose of the study. The third consideration is the confidentiality and privacy of data. The study will take place in a company, which requires protection of the firm’s private data and the researcher will ensure that the information collected from the participants and relating to the company will only be used for the study. Nothing will be disclosed to a third party without the permission of the subjects. Besides, the researcher will not use any information that identifies the participants, such as names, social security numbers, and addresses. The study will use codes to identify participants.
Timelines
The study will be conducted for two months, within the first month, the researcher will complete the proposal, seek approval, and prepare the interview schedule to be used in the interview. Within the second month, the researcher will seek ethical approval from the organization and begin conducting the study. Since data will be conducted from five organizations within the city, the data collection process will take a week. The remaining two weeks of the second month will be used to analyze the data and report the findings. The complete report will be ready by the end of the second month of the study.
Reference List
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Brewster ME, Velez BL, Mennicke A, & Tebbe E 2014, ‘Voices from beyond: A thematic content analysis of transgender employees’ workplace experiences,’ Psychology of Sexual Orientation and Gender Diversity, vol. 1, no. 2, pp. 159-165
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Erevelles S, Fukawa N & Swayne L 2016, ‘Big Data consumer analytics and the transformation of marketing,’ Journal of Business Research, vol. 69, no. 2, pp. 897-904.
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