Research proposal: Merits and Demerits of using Big Data in business organizations
Big data is supposed to curate and manage a magnificent archive of information that no other software or other expertise is unable to capture. The philosophy of Big data is to impose an order in the enormous amount of present yet unstructured (since some of the data are inaccessible) data, though the interface also takes structured or semi-structured data into consideration. In the advent of technological expertise, the accumulation of diverse data have become easier through a wide range of cheap data-sensing IOT (Information Of Things) devices and this preserved yet anonymous data can be typically characterized as the agent of disorder in the prevalent archive. As the global technological capacity of preserving information has been roughly doubled, it has become an imperative criteria for the leading business organizations to adapt to big data analytics and auxiliary expertise in order to enhance the accessibility of requisite information within a tolerable duration of time.
The current research suite is supposed to cater the pursuit of identifying the potential advantages and disadvantages of utilizing Big data analytics in the business premises. Furthermore, they need to adapt the requisite technological expertise coupled with the upgraded version of data integration to reflect introspective insight from the diverse and intricate set of data (Loebbecke& Picot, 2015).
The corresponding research suite is dedicated to derive the potential advantages and drawbacks associated with the adaptation to big data analytics. As it can be cited as an indispensable technical obligation for the business units across the globe, the researcher is supposed to cater the moot moral enquiry in order to present some introspective recommendations in the current context of business volatility (Loebbecke & Picot, 2016). Since the ability to obtain and store magnificent amounts of data have grown considerably in an unprecedented proportion, the requisite technical capability to arrange and evaluate these vast volumes of data has become essential for the reputed business units to invest and eventually procure the embedded insight that have the potential to ensure a competitive edge for the respective units. The respective project objective can be arranged as;
- To derive how the advent of big data analytics have advocated the alteration of fundamental business protocols
- To derive how the advent of big data analytics have influenced the governing business strategies and decisions
- To divulge the potential advantages and drawbacks regarding the unanimous adoption to big data analytics
- To propose introspective recommendations that bear the potential to be served as remedies of the aforementioned drawbacks
The scope of this current research suite is to propose that the big data analytics as a reliable agent to attain a relative competitive edge for several business concerns. It also addresses that the generation of potential adversaries owns the potential to leverage the strategies that are empirically data-driven in order to introduce a dimension of innovation in the business ventures. Furthermore, the researcher appears adamant to enhance the disclosure of important information with an improved level of data transparency in this course of study. The concern of the project suite also appears determined to encourage the preservation of transactional information in the virtual interface. On that note, the researcher has presented the advantages and drawbacks of current data analytics tool that are very likely to occur.
Moreover, from the current project suite, the researcher expects to transcend the prevalent practice of empirical analytics on the premises business venture where the moral obligation of the current suite is to patronize the precise analytics that may advocate the decision-making faculties while discovering the cryptic insights (Loebbecke & Picot, 2015). In order to derive the potential advantages of switching to big data analytics, the corresponding risk analysis has been extensively encouraged since this study is directed towards a new horizon to redefine the analytic tool as a trustworthy apparatus to carry out a predictive analytics with the assistance of relevant social and economic factors.
The term ‘big data’ intends to indicate the notion of predictive and behavior analytics or specific other upgraded information analytic methods that are typically employed to uncover insightful value from a given set of information. As prescribed by Wixom (2014), the magnificence and accessibility of the available data cannot be the indicative characteristic feature of this recently evolved ecosystem of information. As per the comprehensive study of Baesens, Bapna, Marsden, Vanthienen & Zhao (2014), the per-capita technological expertise and availability to store and manage data has roughly doubled in every 40 months and this unprecedented growth of Data sets herald large enterprises to determine and establish big data initiatives that might affect the entire organization across the hierarchy. As the prevalent database management systems face considerable difficulty while handling the vast amount of unstructured data, this phenomenon develops a stable demand of software that can run multiple servers parallely. The scholarly article of Kwon, Lee & Shin (2014) reflected that, the notion of big data varies across the capabilities of the respective tools and potential users and the eventual expansion of the prevalent capabilities transform big data into a dynamic target. The basic facilities that drives the impulse to adapt to big data analytics is that it facilitates the notion of decoding the underlying information of massive proportion in order to derive introspective and valuable insight.
Though the business units are typical to employ business intelligence instead of big data, the evolving enquiry related to this tends to distinguish the mode of operation of this analytic and predictive tool. Evaluating the respective study of Loebbecke & Picot (2014), the business intelligence can be characterized by the usage of descriptive statistics coupled with a rich information density that are usually employed to decipher trend while measuring things. On the other hand, the notion of big data can be typically characterized by the usage of inductive statistics that tends to employ the underlying apprehensions of nonlinear system identification to infuse laws from the magnificent metrics of information with relatively low data density to divulge the interdependencies and relationships that assists to perform the predictions and the detection of the behaviours of users.
The corresponding literature review have outlined the potential origins of concern that owns the potential to indicate the major issues which can morph itself to become a potential drawback of the big data analytics. Furthermore, the literature review have gone through an enormous amount of pertinent literature in order to derive the potential advantages that may pave an organization towards a competitive edge. One of the introspective apprehensions of (Wixom, 2014) suggests that the enhanced capability of storage is not the governing characteristic feature of this new ecosystem of data storage rather the ability to impose a structure on the enormous amount of information can be presented as the paramount concern of these softwares of data analytics. Since the notion of big data not only refers to the vast amount of data but also the requisite technical expertise in order to attain the desired performance, the above mentioned statement can be designated as the governing statement of this entire pursuit. The prevalent literature also suggests that the installation of big data analytics may assist the faculty of decision making and can be posed as a comparative verdict in case of evaluating a specific image.
As per the gap of the pertinent literature suggests that there exists an emerging demand in the global business ambience to adapt to the big data analytics though most of the leading industries and retails hesitate to switch to it few years back. In this emerging scene of technological expertise where the requisite devices need to answer to the increasing demand of preserving structured data, it seems essential to the researcher to pose a system study in order to transmit the potential advantages and hazards associated with the installation of big data analytics since it enables the organization with the ability to collect information from any process of data generation such as search engines, social media or utility infrastructure.
The comprehensive statement ofhave shaped the entire impulse of carrying out this research suite where the scholar addressed that the characteristic feature of big data is not to flaunt the concept that it is an mammoth entity and it is dedicated to alternatively serve the notion where it is committed to arrange and curate the enormous amount of information. In this course, it is imperative for any business unit to derive celestial aptitude in order to procure the capability to classify the available data and embrace the requisite. Since innovation is an indispensable grease for any business unit to sustain (for instance Google Analytics), big data analytics can be served as an exclusive repository of the rich and innovative idea to grow (Wixom, 2014).
Though there lies a potential possibility for the processed information to be deceitful since the shrewd players of several analytical module enjoys the freedom to manipulate the existing data in order to deceive the user. On that note, it can also be added as an integral dimension that the ability of the analytic software to retain the confidentiality is commendable as it is potentially able to disclose the virtual identity of an invader. This possibility can be further contradicted that sometimes it fails to disclose some significant and time-intensive information that can be served as a potential loophole (Akter et al, 2016).
As the validity of any information is pragmatic, the big data analytics software’s allow swift alterations of the prevalent information as per the available updates. Moreover, the underlying authenticity of the available information endows the respective user with accurate introspection. On the other hand, sometimes this prudent interface fails to update the relevant information as it still have the deficiency to cope up with the speed of evolving information. As it is chiefly devised to function under a heavy load of data it is easy to anticipate how mammoth it is. However, it is impossible to acquire the relevant data within a given interval of time as it lies unstructured and the user have to tussle to accomplish the requisite information (Akter et al. 2016).
Fundamentally, as it stated earlier, the moot impulse of this research suite is driven by the purpose to evaluate the current analytic interface in accordance with the proportion of order it is able to dispense. This heralds the current researcher towards the foundation of the corresponding hypothesis where it has been attempted to draw a transparent parallel among the introspective insight about the pros and cons of big data analytics and the elegant trajectory towards the requisite structure of information.
Design and Methodology
In order to carry out this entire research suite, the researcher have employed both of the methods of qualitative and quantitative in order to gather ample amount of information and process it further to acquire an cumulative overview about the utility of big data analytics in business premises. The researcher has visited a reputed retail outlet in order to collect the potential respondents for the interview session. It is imperative to admit that the respondents have been nominated across the hierarchy of a standard organizational model in order to acquire authentic information. Afterwards, they have been prepared to undergo a precise metric of research questions that are designed to extract the authentic information and the respective opinions regarding the installation of big data analytics (Kambatla,Kollias, Kumar & Gramma, 2014).
Moreover, in order to procure and enhance the accessibility of the available information a quantitative primary research has been employed. This research suite in terms of survey is dedicated to provide authentic archives and databases from the user's end.
Accumulation and respective processing of the acquired information suggests that big data is an apprehensive tool to avail the requisite exposure of such information in order to anticipate the trend patterns (Baesens, Bapna, Marsden, Vanthienen & Zhao, 2014).
Most of the accepted information suggests that the processed information simultaneously pave towards unprecedented generation of revenue. Majority of the opinions agreed about the fact big data analytics enable the user with an immediate view of the respective market and further encourages them to work on the successive strategies in order to accomplish the desired edge. Some of them sniggered over the fact that it is not that easy to sort and process such enormous amount of information and the notion of time-intensive is superficial. The designated analysts and data mining experts among the potential respondents appear pedantic about the presence of potential hindrances in the trajectory of seamless exploitation of big data analytics software.
The current research suite is committed to address only the potential advantages and drawbacks of the big data analytics system. On that note, the current research suite is unable to arrange the available information outcomes as per the basis of unique business environment. Moreover, since the technological advancement is that much rapid the acquired data can lose its authenticity and validity within a given course of time due to its vulnerability to time-intensive entities. It apparently seems possible to herald a faculty of decision-making of a respective organization by enabling them with the awareness of the potential advantages and drawbacks of installing a new analytics software but it is empirically toilsome to acquire the desired outcomes. As the prevalent arrangement of the available data is clumsy and scattered it is literally impossible to impose an order in the pertinent archive of information. Furthermore, as it potentially impossible to delve into the tangible aspects associated with big data analytics and management, the current research has restricted its concern within the empirical premises. Apart from that, this research suite cannot be served as a manual of big data analytics since it has taken a judgemental approach to analyse the cryptic notions regarding the advantages and disadvantages of employing big data analytics in the business premises (Kwon, Lee & Shin, 2014).
As the analytical outcomes suggests, the derivation of potential advantages and drawbacks appear imperative for the leading business units to acquire due to the underlying vulnerability of the virtual domain. Moreover, as the enormous amount of information is destined to be scattered and clumsy it is highly necessary for the business units to impose an order in the prevalent lay-out in order to process it and work accordingly. The corresponding research suite have successfully addressed the underlying notions regarding the appointment of big data and also have been able to support them with introspective arguments. The entire research suite has been able to conclude that the governing statement on which the research suite is intellectually based on is correct and simultaneously encourages the characterization of big data in terms of curating the comprising informations. In an moral point of view, the research suite is potent enough to endow the potential users and budding business executives with an in-depth introspection and the simulations might pave a business concern towards competitive edge in this volatile business ambience.