Assessment 2 Business Research Proposal Essay Example

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Benefits of Big Data its effect on Business Research Proposal 4

Benefits of Big Data its effect on Business Research Proposal


Analytics industry has made big strides in global competitive world. Though it seems to be a budding trend in developing countries, developed countries have inducted speeding up processes in primary business functions. Many companies offering analytics have been doing well in the past 15 years. According to Russom (2011), Data analyticscan be defined as “advanced analytic techniques to operate on big data sets”. Big data analytics consists of two elements namely large data and analytics. Together they have formed a new trend in the area of business intelligence. Business operations are affected by several factors that may have roots inside or outside the organization. It is tough to achieve coordination and relationship between detailed data, processing skills and congruence with specific business objectives.

Analytics stand important in any organizational setup as planning and strategizing in competitive world is a necessary protocol before placing a product/service in the market. The scope of analytics may be reduced to monitoring business performance, examination of a raw data to arrive at a fact, to assess the ground zero of a prevailing problem or even forecasting a future marketing trend. Facts are accepted based on logic. These are collected through market research activities by firms. Research activities have occupied primary places in the organizations recently. Earlier organizations never treated research as important but instead worked on an impression of long term spending with return being uncertain. Number of basic research studies has increased over applied ones. Most of the basic researches have been done on nearly 4,000 technologies from 93 countries and 25 industries concentrated in the area of IT. It was recognized that business analytics was the latest trend emerging in global space Bloomberg business week (2011). To recognize and implement a technology that runs analytics on data, managers with a positive mindset on data is very much in necessity to encourage a technological implementation.

Project Objective

The main objective of the project is to get inputs on slow adoption of big data. The sub objectives are as follows:

  • To assess awareness of corporate respondents towards data analytics, and source of data in companies.

  • To determine present stage of inducting big data adoption as well as reason behind rejecting it.

  • To examine perceived benefit and challenges for any possible association.

  • To compare average of means of perceived benefits and challenges.

Project Scope

Technologies extracting data by various ways of automation have been a step forward in data collection methods. As analytics contribute value by predicting future market, data is sourced from social network pages and websites. Simultaneously, information on individuals will also be stored. Later on, these data will be correlated to arrive at consumption habits and purchasing patterns. Social networks have been a common source of data till now. The problem is that though analytics give an undoubtedly exceptional value to the industries, it is not being adopted primarily because of its complexity. Most of the analytic techniques depend on data to produce output and some of them are designed to use process for arriving at conclusion (Gelfand, 2011/2012, p.26). Big data is the trend forming a new era in the area of productivity, competitiveness and growth.

Literature Review

Big data analytics has a vital role to play in organizational productivity. To achieve productivity, adoption should be successful. As companies are investing in data analytics, they don’t evaluate on basis of advantages and important issues to be addressed. Instead, they bother on implication of analytics to their processes by only focusing on risk and return. Malhotra (2000) describes the infusion of data analytics as allowing inflow of real-time information into the organization. According to (Few, 2007, p.8), data analytics pools a large amount of data that makes it very tough to see required information with clarity. That means, some facts may be overstated or some more may get hidden under waves of analysis.

According to (Wilkinson, 2012, p.9) understanding of data analytics relevance to the business processes is very limited among industries. So in the absence of analytics, poor data leads to a disastrous result. Even in presence of analytics, defective data would also take company towards wrong decisions by contaminating downstream. Companies often get caught in cross roads and get biased in the way they see advantages and disadvantages of data analytics. As the adoption process of analytics does not come in handy, it depends on challenges faced by companies and benefits perceived from the competitive edge for diffused use of data analytics (Pettey & Goasduff, 2011, p.5

Even after investing more money on analytics, insurance sector in US did not get expected value out of it. In order to get best return on investment in analytics, after data mining, companies need to store structured data of customers clearly and this is a challenge. Companies that went after investing on data analytics faced two complex problems such as training employees in using technology and developing talent in them to understand value of data. (Philips, 2005, p.7).

With data analytics, corporate can comfortably watch and monitor market trends and build target marketing strategies more effectively. Consumer’s complex decision making is believed to be gold mine of information for marketers. According to (Andrew & Erik, 2012, p.66), tools have arrived in the market to control various kinds of data and their volume. Cost wise they are not so expensive for a company with startup background of 4 years. Data analytics results in social-economic living standards enhancement. The main issue with analytics was planning a vast storage solution (Karthik et al, 2014, p.2570). Later on however distribution systems came to rescue of techies. The storage problem though is not completely dealt. There is a lot of ease in case of storage task. As competitive edge give companies advantage to treat analytics as a benefit, but implementation failure stories makes an organization to view induction of analytics as a challenge.

According to Chen et al, (2012), analytics are implemented in organizations based on three goals. One is to store and analyze learner data or company as a client that use analytics, secondly to monitor company’s data activity as well as predicting results, thirdly arriving at a decision making in a given situation. Industries are aware of this classification to get the best usage of data analytics. Even though there are various purposes analytics cater. Systems can work together for helping learner or client to get intended results. It may be decision making, prediction etc ((Lukoianova & Rubin, 2014, p.11). The research community across the industries will get best of business analytics as they can provide more value to their organizations. Many developed countries realizing power of data have invested in data science centers to get insight into precise information that can give a competitive edge.

Research Questions/Hypothesis

Primary Hypothesis

H1: Corporate perception of benefits and challenges in adoption of big data are different and not biased.

Secondary Questions

  • Is there a proper awareness on benefits of big data among corporate?

  • Are there any challenges perceived by corporate to adopt big data?

  • Are benefits independent of challenges?

  • Are benefits and challenges associated leading to an opinion bias among corporate professionals?

Research Design and Methodology

The study makes descriptive approach to answer the present problem of data analytics adoption. Survey approach among other data collection methods was made to reach objectives. Primary data was collected by interviewing corporate people working in middle and higher level organizational levels in companies across sectors like Transportation, Retail, Telecommunications, Manufacturing, Healthcare, Banking and Finance. A disguised structured questionnaire will be developed to collect the intended data free of bias. As basic literatures on items were scarce, own items were generated by consulting corporate officials. The pilot study with the questionnaire consisting of 30 samples will give promising reliability results. Full-fledged data collection will be undertaken. Chi-square tests help to analyze association and independence aspects when qualitative data is used. It is best applicable data analysis tool in solving business decision making problems and especially when data is measured at Likert scales.

Research Limitations

The study will survey only corporate professionals and not academics. Without academics inducting core analytics in the syllabus, the analytics talent pool cannot be generated effectively in companies. The problem of finding to corporate world is that their perceptions on benefits and challenges has an association signaling low understanding of big data. The positive indicator is that the weight on benefits is more than challenges raising hopes on possible good future for analytics.


The paper attempted to bring out possible bias of corporate on in perceiving benefits and challenges of adopting big data. It is clear that corporate are indeed failing to separately gauge the benefits and challenges in making decision for implementing big data in organization. Data analytics methodologies are based on data mining. The transition phase from a semi or non IT organization to an IT automated organization is a delicate stage where many technology implementations have failed. Corporate respondents have perceived more benefits than challenges in case of big data adoption.


Andrew McAfee & Erik Brynjolfsson (2012). «Big Data» The Management Revolution». Harvard Business Review, 64-67.

Chen, R. Chiang, and V. Storey. (2012). “Business intelligence and analytics: from big data to big impact”. MIS Quarterly, 36(4), pp. 1165–1188.

Karthik, K. Giorgos, K. Vipin, K. Ananth G, (2014). «Trends in Big Data Analytics», Journal of Parallel Distribution Computing, 74, 2561-2573.

Lukoianova, T. & Rubin, V. L. (2014). “Veracity Roadmap: Is Big Data Objective, truthful and credible?”, Advances in Classification Research Online, 24(1), 4-15.

Mark Wilkinson (2012), «Building analytical capabilities for the information economy», SAS publication, 8-10, Retrieved May 30, 2017, from

Pettey, C., & Goasduff, L. (2011). Gartner Says Solving “Big Data” Challenge Involves More Than Just Managing Volumes of Data. Gartner. Retrieved May 30, 2017, from

Philips JG (2005). “Academic Analytics: The uses of Management Information and Technology in Higher Education», ECAR (Educause center for applied research) Journal, 1-7.

Russom, P. (2011). “Big Data Analytics”, TDWI Best Practices Report, Fourth Quarter, 1(4), 23.

Stephen Few (2007) «Data Visualization Past, present and future», Cognos innovation center publication, 8(2), 1-12.