Discussion on Qualitative Research

Module 11 Discussion: Qualitative Research

 

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This is good but chapter 5 is all about your own project and reflections, and therefore there should not be any references included, unless absolutely necessary.

Applied Project

Merits of My Work

Data collection and analysis of the same is an essential part of a research study. An interpretation of data validates the purpose and objects of research work. To supplement initial funds of knowledge, a qualitative study was conducted. Unlike other pieces of work by my colleagues, my type of study, qualitative, proofed more competitive concerning the nature of the study: the problem statement, and the research questions. According to Rahman (2017), qualitative approaches in business provides rich, concrete data and information in greater detail than other means such as quantitative studies. Besides, qualitative data collected in this study has a competitive advantage of proofing a trail of association between different variables in the field. Therefore, it is more advantageous to use the qualitative data in my research to ascertain the practicality, status, and potentialities of utilizing business intelligence in a variety of businesses regardless of its size.

On the other hand, the data used were sourced from small-to-medium businesses in the United States. I would recommend the use of data from such businesses because it forms a large portion of the capitalist economy by a larger figure than large scale businesses. Unlike other studies by my colleagues, participants sampled from small-to-medium businesses are more willing to provide feedback than their counterparts in big corporations who might be restricted from sharing information related to their work experience and the use of business intelligence (Ali et al 2017). It is quite notable that my study utilized the use of interviews to collect their data. These were conducted virtually as a safety measure to combat infections during the period of COVID-19. Results from the study represent high-level knowledge and sophisticated information since participants tend to provide extensive information when they respond in their areas of comfort for example interviews from home. Other peer studies may lack this element since methods involved in quantitative studies would require them to collect data in a face-to-face approach which may hinder data collection during this period of the pandemic.

Results from the study complied with the needs and deficiencies in the market about the use of business intelligence. Every innovation is meant to solve actively and proactively solve problems. Regarding this, my study took into consideration this factor by a review of literature that guided data collection and selection of the participants. Many problems exist in the small-to-medium scale businesses since most of them run under low-costs of maintenance and operations. On the contrary, large scale businesses would not be a good choice in the study of the use of business intelligence as they have sufficient resources since it runs under economies of scale. Thus, a focus on small-to-medium scale businesses would provide big data that validates the relevance of my study to solve contemporary problems in the market, unlike my colleagues who collected data from large scale businesses.

Recommendations for Future Work

The study conducted herein supplies additional funds of knowledge to existing literature. However, there is much to be done in examining the potentiality of affordable and efficient business intelligence tools in small-to-medium scale businesses (Rouhani et al 2016). Therefore, the following recommendations should be considered by future research:

  1. Explore ways of improving systems in small-to-medium scale businesses for example Enterprise Resource Planning to avoid commoditization of the same.
  2. Determine ways of incorporating decision-making support systems into business intelligence to promote its efficiency and convenience.

 

 

References

Ali, S., Miah, S. J., & Khan, S. (2017). Analysis of interaction between business intelligence and SMEs: Learn from each other. JISTEM-Journal of Information Systems and Technology Management14(2), 151-168.

Rahman, M. S. (2017). The Advantages and Disadvantages of Using Qualitative and Quantitative Approaches and Methods in Language” Testing and Assessment” Research: A Literature Review. Journal of Education and Learning6(1), 102-112.

Rouhani, S., Ashrafi, A., Ravasan, A. Z., & Afshari, S. (2016). The impact model of business intelligence on decision support and organizational benefits. Journal of Enterprise Information Management.

 

 

 

 

 

 

Feedback from Professor

 

This is OK, BUT….and as I mentioned in online class and sent notes, in order to organize Chapter 2, you will – first start with an introduction about the general problem and your topic., which you did…… – Then you will provide an advance organizer, which indicates what will be covered in the literature review. After you have introduced the three related areas, you will locate and synthesize three to four research articles (with empirical data) for each of the three areas related to the topic. Each section should start with a brief introduction about the area and end with a summary paragraph to recap the main points and limitations within the area. At the end of the literature review, there should also be a summary that ties together all of the literature related to the topic.

 

 

 

 

 

 

 

 

 

 

 

Table of Contents

Literature Review.. 2

Introduction. 2

Big data Overview.. 3

Data in the SME Context 5

Challenges Business Intelligence Face. 6

Business Intelligence Limitations. 7

Trends and Future Business Intelligence. 7

Artificial intelligence and Constant Intelligence. 8

Context Placement of Data. 8

Building Dynamic Capabilities and Competitive Advantage Through Big Data Analytics. 9

References. 12

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Literature Review

Introduction

Business intelligence’s strategies and technologies are used by companies and enterprises for data analysis purposes. The technologies are used in provision of current, predictive and historical business operation views. This technologies as well help business managers, executives and even workers in making informed decisions for the company. Different methodologies, strategies, new technologies adaptations and policies are produced in an organization to integrate its operations and values with business intelligence solutions. With the aid of Kitchenham (2004)’s protocol, this section provides a systematic literature review.

Kowalczyk (2017) lists key process in organizations as data extract, data warehouse, transform, data sources and load process. To get reliable and rightful data used to develop reports, companies focus on different data sources. The basis of this reports is on: customer analysis, growth analytics, compilation management and sales analysis. Organizations improve their performance using Key Performance Indicator (KPI). To implement best techniques and improve business strategies of the organization, this project will provide a comprehensive business intelligence infrastructure for operations of the business. This business intelligence journey will lead to total transformation of the raw and complex data into simplified visualized business analytics using data from different sources.

Business analyst will guide business leaders to consider business requirements in understanding the process of the business and making better decisions in an organization. Also business analyst will develop comprehensive visual analytics that is insightful and colorful providing an overall understanding of the business for the management. The goal of this literature review is to understand the challenges business intelligence face in both retail sectors and SMEs, its limitations and the trends of business intelligence and its future.

Big data Overview

Over the years, big data as well as its associated significance, has witnessed astonishing diversity and growth. Its diversity and volume have influenced the way data is utilized and processed. As a result, deluge of specialization is being witnessed in various industries and sectors ranging from military and scientific applications to retail consumer analytics. For instance, Tan et al. (2013), asserts that the real-time transformation of Walmart’s database on customer behavior, activities on various devices, preferences, and market trends was 2.5 petabytes in 2011. By 2016 they deluge shifted massively to 40 petabytes. Also, on military applications, they note that the United States of America’s Airforce has various video footages from Iraq and Afghanistan that covers events from more than the last three decades. Also, Tan et al., (2013) assets that CERN, in 2010 alone, produced 13 petabytes of data. Big data defines various big news of various large companies and allow them to gain insight why customers buy what they want and help them build string loyalties in the out-of-date fickle retail marketplaces.

In the current century, big data is increasingly becoming an area of interest in business management and strategy. Mikalef et al. (2018) asserts that the new focus on data has been influenced by wide-scale use of artifacts that are associated with Internet of Things, social media as well as social media products and services and reward cards. From different ontological perspective, there is a different epistemological perspective of data. Thus, the usefulness of data from a marketing manager in a small cosmetics company is very different from that of a sensor manufacturer. There are various debates in various literatures arguing on the exact definition of big data, with some scholar having much focus on the origin of big data such as whether data originate form mobile devices of social media, sensor for engineering applications, financial or news streams (Johnson, 2012; McAfee et al, 2012; Sun et al., 2015; Babar and Arif, 2017). According to McAfee et al., 2012 big data can be likened to its predecessor, data analytics, where its ultimate objective is to make use data’s competitive advantage and make it have some sense.

Scholarships in big data are shifting away from specific data avoidance application to taxonomy that defines and explain the categorizations of five domain words, namely, veracity, value, volume, velocity, and variety (Akter et al., 2016). This taxonomy shift elucidates on data’s economic variability and if the cost of data investment brings forth real values to small and medium sized enterprises. In other word, how sensible is it for small and medium sized businesses to come up with the necessary dynamic capabilities from limited resources? In the process of finding answers, Data veracity categorizing data from trusted sources, is authentic and protected. Besides, Demchenko et al., (2013) asserts that data veracity should be an additional process. Furthermore, Seddon and Currie (2017) add that by adding data visualization concepts we add patterns and trends to interpret some degree of ease for instance in the study of online financial trends.   All these big data definitions define the rate at which data is morphing and increased into diverse forms for harvesting (Mikalef et al., 2018). For these reasons, accessibility and variability of data that is there for generating dynamic opportunities for small and medium size businesses to scale above their heights using evaluation tools that was previously the exclusive domain of large corporations is increasing. However, Small and Medium Sized enterprises are slow adaptors of big data concepts and are at the risk of being left floundering in the jet stream of their huge, well established competitors. As are result, this is a major concern as large-scaled businesses build capabilities in big data that places them in a position to detect market trend earlier before SMEs thereby having further dominance. Since SMEs contribute most of the funds in the economy of a nation as well as is a vital component of the global economy, both consumers and governments should be concerned equally.

Data in the SME Context

The rate at which data is being generated worldwide every day is doubling after every four months. In 2016, Coleman et al. note that the data volume created worldwide each day is 106 X 2.5 terabytes. The five Vs, veracity, value, volume, velocity, and variety of data is increasingly becoming complex and overwhelming. Data complexity is largely dependent various generations of data in various formats such as structured, image, sensor, textual, and unstructured formats. Traditionally, businesses obtain data through customer record databases, monitoring, and recording. The nature and adoption of data in SMEs vary considerably. According to Coleman et al. (2016) diversity and in KPMG’s 2015 report differs greatly. The same is witnessed in data analytics across various industries. Also, in the insurance industry, leaders apply and deploy diverse and advanced data analytic techniques.  According to data variability Coleman et al. (2016)   note that banks and supermarkets follows closely, and recently, healthcare and government revenue companies are following this variability trend. The rapid pace of high refreshment levels in data analytics that is tailored to meet the necessities and needs of a given market is another concern for small and medium sized businesses. The ever-upsurging gulf will lead to suitable competitive gain in long term basis and SMEs are thus forced into a more specialized market niche to survive.  E-skills UK, highlight this trend in their 2012 report that indicated that 0.2 percent of SMEs in the United Kingdom were actively adopting data analytic mechanisms. They also indicated that 25% of large scaled or organization are adopting these processes.

TechNavio’s report forecast that the compound annual rate of growth of Small and Medium Sized businesses in the field of big data will expand over the period of 2014 and 2018. Therefore, for Small and Medium Sized business that are still using traditional methods of operation will find it difficult to manage and analyze data with this rapid growth rate in the size of big data. As a matter of fact, there are few noteworthy empirical research works in the problems and issues that are facing SMEs in the process of adopting big data analytic process.

Challenges Business Intelligence Face

Business intelligence is very expensive hence only managed by organizations transacting large volume of data. Ravasan and Savoji, (2019) explains that it is important to build markets commitments depending on strong reality. Business intelligence in-cooperates data utilized in market development hence removing unnecessary procedures and recognizing chances of enhancement of your organizations. Presently the predictions of small businesses regarding price and attempting to execute BI are very minimal due to high costs of BI installation.

Also getting certified professionals for instance information science specialists, consulting executives, IT basics experts and obtaining proper software application may be challenging. Business intelligence applications such as Microsoft BI, DOMO, Tableau Desktop, tableau server and other tools upgrade, mantaineance and installation are costeffective.at initial stages the organizations financial department will be burdened with this establishment and the BI usage and it might take time before adapting. Furthermore, only experienced and skilled workers can accomplish the analysis of the report and its usage (Ramakrishnan et al., 2016). In executing business intelligence, a lot of time is consumed in individual coaching and arrangements of committed hardware and exclusive software application. Also business intelligence solution needs extensive hardware employees in the earlier stages. Employees need a substantial coaching in data administration tools.

Another challenge organizations and SMEs face are lack of business intelligence strategy. Without organizations defining problems they want to solve; it can be challenging to the right business intelligence solution to implement. Trying BI without a proper strategy can be costly, frustrating and also chances of failing are very high.

Business Intelligence Limitations.

Sharda et al, (2018) explains that most of the companies are utilizing business intelligence applications in constructing their information recognition. However, most SMEs still choose free answers sources. The following are some of the limitations to be reviewed in cases where BI system is implemented.

BI system is not user friendly hence affecting its acquiring .one of the major issues making BI to be overlooked is customer acquisition. For its efficiency, a range of dedication from high to low level of a company is needed. Customers will not build productive utilization of the system in case the outcome of the intelligence is critical and complex to authenticate. This might make the business worth to decrease. Since most of the free software tools are based on programmer’s ideas while developing, customers are not kept in mind hence making the system not friendly to the customers.

Trends and Future Business Intelligence

Currently, organization may not enquire on what kind of BI solution will meet their aims and requirements and if the organization needs problem solving and business intelligence analysis. Companies function with our BI specialist advisers at Smart employees in creating employees that can take the business to improved levels of opponent and extensive business intelligence plan (Sherman, 2015). It is incredibly important to hold up on the appearing modifications and movements in business intelligence even though pursuant in information enhances development of technologies.in the thought of 2020, BI specialists and concepts such as practical analysis artificial intelligence and data standard management are high. For instance present movements analysis on what business intelligence will keep in future and changing BI attempts is one of the 2020 thought.

Artificial intelligence and Constant Intelligence

The machine learning (ML) and artificial intelligence (AI) is influencing both existing individuals and those doing over companies and industries work (Brook et al, 2016). Furthermore Roffel (2020) adds that a discussed analysis moves how information is examined, divided and shifted over the companies. Also discussed analysis allows default programs use in improving and speculating information a solo researcher will not be able to study. Executives will improve highest volume of information with the thorough use of machine learning (ML) hence exposing extra business inputs. Constant intelligence (CI) influences business intelligence by utilizing the present information in developing business commitments depending on the simultaneous analysis business functions.

Context Placement of Data

Companies will have authentication over large volume of data in the current days. the capacity to computerize, maintain, determine, evaluate and examine data has increased more than ever. However, all these procedures might not be useful in cases where executives and data specialists are not able to use different ways to create an effective address of the data to others. Doable analysis and scripting of information also influences business intelligence by conveying current information findings and upcoming expectations. Companies view doable data in exhibiting the utilization of prescriptive and anticipating analytic applications to predict future business. Anticipating analysis and a few loyalties measures what might occur in the upcoming days using data received from current information groups.

Building Dynamic Capabilities and Competitive Advantage Through Big Data Analytics

Akter et al. (2016) indicate that through leveraging of big data analytics, SMEs can witness noticeable performance gains. However, much of his research attention was in defining the specific resource needed to come up with capabilities to leverage such competitive gains.  As a matter of fact, much research work is silent on how micro foundations are established in Small and Medium Sized businesses and they are orchestrated in a manner that develops strong capabilities of big data analytics. Consequently, there is significant literature gap especially where empirical research is used as “Everything as a Service” (XaaS) bespoke package in SMEs in cost effective way and to develop competitive advantage.

While building on the resource-based theory, Gupta and George (2016) suggest that dynamic capabilities and competitive gain is in resources of an organization. Resource-based theory, developed in 2007 by Barney, is based on the premise that an organization can have competitive advantage by coming up with unique resources that are heterogeneous with respect to its competitors. Also, it can do so by coming up with unique and difficult resources to imitate.  Therefore, the resource-based theory stand point has a different perspective to that of a more standard porter’s approach that assets that competitive advantage is gained by looking into and analyzing external market forces. Instead, by employing this model, SMEs will be looking at their inward resources that are either intangible or tangible as well as their knowledge and human skills.

The taxonomic trait of big data is in volume, variety and velocity of data (Mikalef et al., 2018). However, in most cases, data fitness and quality are overlooked.  While in most organization data is increasingly becoming a critical resource, McKinsey’s 2011 report identified that data is equally critical as traditional tangible assets such as commercial assets, labor, and capital. Therefore, together with organization’s property, infrastructure, IT systems and cash, owning valuable data is a critical tangible asset. Data acquisition as well as its effective use is governed by an organization’s tangible assets. The process entails keeping knowledge and skills that are not always readily available for SMEs. Tallon et al. (2013) acknowledges the significance of managing growing data volumes in a firm and the significance of detailing processes and structures that are necessary for management. While dynamic capabilities exist in various perspectives, there is a failure in identifying the fundamental issues the SMEs face. For data to be effective in these businesses, it must be operationalized and packed with little effort and cost. Together with presupposed resources, elaborate framework does not was with SME operational realities. Data-driven culture is a significant factor for continued success of an organization (LaValle et al., 2011). LaValle et al.’s understanding of data brings the dynamic capabilities of data in organizations where the culture for implementing big data should be embraced by top management to individual worker.

Human skills, competencies, and knowledge are other pillars of resource-based theory. Elaborate organizational are necessary in this case especially where division of responsibilities for deploying and harvesting big data delignates through cloud services managers, programmers, database managers among other useful individuals in an organization. SMEs do not benefit from multi-functional approach. Inevitable top management support and learning culture is important regardless of an organization size.

Despite the much-heralded potential of big data as well as the hype surrounding it, much research is needed in dynamic capabilities of big data in SMEs. There is not active research area and the solution may be in SMEs forming strategic alliances with data harvesting specialists. By doing so, bounded rationality potentials are reduced while relational competencies are increased, and thus, these businesses will grow and cultivate inter-relational skills.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

References

Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016) ‘How to improve firm performance using big data analytics capability and business strategy alignment?’, International Journal of Production Economics, 182, pp. 113-131.

Barney, J. B. and Clark, D. N. (2007) Resource-based theory: creating and sustaining competitive advantage. Oxford: Oxford University Press.

Babar, M., and Arif, F. (2017) ‘Smart urban planning using Big Data analytics to contend with the interoperability in Internet of Things’, Future Generation Computer Systems, 77, pp. 65- 76.

Brook, H., King, C., Paulli, G., & Frith, A. (2016). Artificial intelligence. Tulsa, OK: EDC Publishing.

Coleman, S., Göb, R., Manco, G., Pievatolo, A., Tort-Martorell, X., and Reis, M. S. (2016) ‘How can SMEs benefit from big data? Challenges and a path forward’ Quality and Reliability Engineering International, 32(6), pp. 2151-2164.

Demchenko, Y., Grosso, P., De Laat, C., and Membrey, P. (2013) ‘Addressing big data issues in Scientific Data Infrastructure’, in 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego, CA, USA: IEEE, pp. 48–55.

Gupta, M., and George, J. F. (2016) ‘Toward the development of a big data analytics capability’, Information & Management, 53(8), pp. 1049-1064.

Johnson, B. D. (2012) ‘The Secret Life of Data In the Year 2020’, The Futurist, 46(4), pp. 20–23.

Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University33(2004), 1-26.

Kowalczyk, M. (2017). Study E: Business Intelligence and Analytics – Decision Quality and Insights on Analytics Specialization and Information Processing Modes. The Support of Decision Processes with Business Intelligence and Analytics, 99–116.

LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., and Kruschwitz, N. (2011) ‘Big data, analytics and the path from insights to value’, MIT Sloan Management Review, 52(2), p. 20- 32.

McAfee, A., Brynjolfsson, E., and Davenport, T. H. (2012) ‘Big data: the management revolution’, Harvard business review, 90(10), pp. 60-68.

Mikalef, P., Pappas, I. O., Krogstie, J., and Giannakos, M. (2018) ‘Big data analytics capabilities: a systematic literature review and research agenda’, Information Systems and e-Business Management, 16(3) pp. 547-578.

Ravasan, A. Z., & Savoji, S. R. (2019). Business Intelligence Implementation Critical Success Factors. In Applying Business Intelligence Initiatives in Healthcare and Organizational Settings (pp. 112-129). IGI Global.

Ramakrishnan, T., Khuntia, J., Kathuria, A., & Saldanha, T. (2016). Business Intelligence Capabilities and Effectiveness: An Integrative Model. 2016 49th Hawaii International Conference on System Sciences (HICSS).

Roffel, S. (2020). Introducing article numbering to Artificial Intelligence. Artificial Intelligence, 278, 103210.

Sharda, R., Delen, D., Turban, E., Aronson, J. E., Liang, T.-P., & King, D. (2018). Business intelligence, analytics, and data science: a managerial perspective. New York: Pearson.

Sherman, R. (2015). Business Intelligence Applications. Business Intelligence Guidebook, 337–357.

Seddon, J. J., and Currie, W. L. (2017) ‘A model for unpacking big data analytics in highfrequency trading’, Journal of Business Research, 70, pp.300-307.

Sun, E. W., Chen, Y.-T., and Yu, M.-T. (2015). ‘Generalized optimal wavelet decomposing algorithm for big financial data’ International Journal of Production Economics, 165, pp.194- 214.

Tallon, P. P., Ramirez, R. V., and Short, J. E. (2013). ‘The information artifact in IT governance: toward a theory of information governance’ Journal of Management Information Systems, 30(3), pp.141-178.

Tan, W., Blake, M. B., Saleh, I., and Dustdar, S. (2013). ‘Social-network-sourced big data analytics’ IEEE Internet Computing, 17(5), pp. 62-69.

Awaiting Professor’s feedback

 

Abstract

 

Despite the advancement in business intelligence and business analytics in most businesses, small and medium-sized enterprises are far behind in adopting business intelligence and analytics. However, there is no study in literature reviewing Business intelligence and Business Analytics in SMEs despite the need to survive and monitor their business processes and use their resources effectively. Despite their economic Size SMEs need access to essential and well-informed information to ensure they acquire market shares. In this area, business intelligence has been proved to be effective. Due to this factor, among other operational issues, SMEs are typically vulnerable and are not robust enough to withstand the waves of economic competition, especially from large-scale enterprises. Hence, a broad research topic is needed to address various Business Intelligence and Business Analytics components and solutions, including mobile and cloud business intelligence and analytics. A paper also needs to offer recommendations, adaptation process, implementation, and discuss the benefits of business intelligence and business analytics.

This paper recommends ways to recognize the significance of information and use it to define a resource and make informed decisions. The paper acknowledges that informed business decisions are made from quality information by analyzing large quantities of internal and external data. The paper also acknowledges the various complexities and difficulties of adopting Business Intelligence and Business analytics systems.  The paper also defines and addresses some of the social factors that are causing SMEs and their administrations to be far behind in acquiring and adopting Business Intelligence and Business Analytics systems.

As data warehousing is increasingly becoming an integral part of modern business support systems, competitiveness is becoming a norm, and large information is being collected. This paper notes that there is a need for SMEs to embrace business intelligence systems.

High hardware infrastructure requirement, High operational cost, User experience complexities, Irrelevant functionalities, Low flexibility to curb the fast-changing dynamic business setting, and Low attention to data access and significance should not frighten these businesses as the advantages of Business Intelligence, and Business analytics is much likely to give them a competitive edge.

Complex development processes cause most SMEs’ projects to fail, and a fully functioning and work philosophy is necessary. SMEs need an efficient, cheap, simple, lightweight, and flexible solution for their operation. By reviewing various literature, interviewing, and observing most SMEs’ operational processes, this paper proposes and adapted an original business intelligent solution from SMEs. Besides, this paper provides an extensive review of Business Analytics and Business intelligence in SMEs for future references and research work.

By synthesizing, analyzing, and collecting various research work on this domain, this research paper combines the needs and deficiencies of SMEs’ business intelligence. Among the finding, this paper notes that many issues exist in SMEs ‘ attempts to embrace business intelligence and business analytics.  These issues range from social, political, economic to data, and information management. Therefore, a focus on data management and use as a resource these businesses an edge and ensure their continuity. Further, research directions and gaps for future studies are presented to facilitate research work on Business Intelligence and Business Analytics in SMEs.

 

 

 

Professor Feedback

 

 

This is good.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table of Contents

Chapter One. 3

Introduction. 3

Statement of the Problem.. 6

Objectives of the Study. 8

Specific Objectives. 8

Research Questions. 9

How Business Intelligence Technologies create value for most operational and production activities?  9

What are the Business Intelligence and business Analytics Challenges that every company facing?  9

How Business Intelligence Applications influence the process of decision making?. 9

What are the leading technologies in Business Intelligence?. 10

What is the future of Business Intelligence future trends?. 10

Significance of the Study. 11

Limitation of the Study. 11

Scope of the Study. 12

References. 13

 

 

 

 

 

 

 

 

Chapter One

Introduction

Llave (2017) affirm that Small and Medium-size Enterprises account for approximately ninety percent of enterprises and more than 50 percent of employment in the world. SMEs play a major social and economic role and therefore they are a source of economic development. It is thus critical to improve the competitiveness of these businesses worldwide. Currently, these business are typically vulnerable and are not robust enough to withstand onslaught of global and economic competition. For this businesses to survive they have to monitor their businesses processes and use resources efficiently (Eder & Koch, 2018). Furthermore, they have to recognize the significance and use of information as a business resource of effective business continuity and decision making.

Despite the economic size of SMEs, access to important and relevant information is key to ensure the success of acquiring market shares.to achieve this, Business intelligence has proven to be an effective tool. Gartner’s surveys, for instance, affirm that Business intelligence and analytics are the top technological priorities for business (Gartner, 2015; Chiang et al., 2012). To most businesses, Business intelligence and Analytics facilitate the decision making process of businesses by providing quality information based on the analysis of large amount of external and internal data. However, Business intelligence systems and analytics are characterized by their complexity and difficulties. Also, both economic and social factor are hindering most SMEs as well as their administrations to proceed with the acquisition of business intelligence analytic systems (Lawton, 2009). Many a times, Business intelligence analytic systems calls of considerable funding and up-to data maintenance practices.

Moreover, most SMEs lack specialize Information Technology department; they depend on owners abilities which may lack advanced technological knowledge. For this reasons, Business intelligence analytics and applications are not available for most SMEs. Them that have successfully implemented these systems experience expensive maintenance costs, difficulties in using, and lack excellent technological training. The current market indicate that these applications meet the need of large enterprises that are characterized with appropriate resources that aid their proper functioning.

Despite all these issues and challenges, better information provision in either business in any market place, as facilitated by Business intelligence systems and analytics, means better decision making and consistent competitive advantage. Successful confrontation of these issues and problems stemming from the specific characteristic of SMEs is a prerequisite. With the increased advancement in technology, Business intelligence System suppliers have come up with applications and analytic tools that effectively address the needs of real small businesses (Grabova et al., 2010).  Others are available online and SME owners have to subscribe to enjoy the benefits that come with them. These systems are easy to use, affordable and are members of the cloud system category; hence a business is likely to enjoy real-time cloud based services such as information backup and security. Also, such installations are appropriate for Small and medium-size enterprises as they are likely not to incur maintenance and additional installation costs.

To most businesses, Business Intelligence and analytics defines a set of process, methodologies, technologies and architectures that transform raw materials into useful and meaningful information that allow them to make informed decisions with real-time data (Evelson, 2008).  Yeoh et al. (2008) note that business intelligence, as coined by Luhn in 1958, has features like analytics, reporting various technology choices. However, as all these features get commoditized, new set possibilities emerge. Evelson (2008) indicate that technology is evolving, and enterprises, on the cutting edge of this trend, can gain competitive advantage in their diverse industries. According to Burstein and Holsapple (2008), Howard Dresner reintroduced business intelligence when he noted Business Intelligence as broad category of solutions and software for analyzing and consolidating, and providing access to data in a manner that let organizations make business related decisions.

The novel term business analytics begun in 2000s and had much focus on Business intelligence. Together, these terms were unified to describe information-intensive methods and concepts for improving decision making. The Gartner’s survey in 2015, noted that Business analytics and Business Intelligence are the top of technological choices of CIOs for increasing competitiveness in the current technological infested market.  Therefore, according to Chaudhuri et al (2011) it is hard to find successful business that have not leveraged Business Intelligence and Business analytics.

For large companies, such Information Technology systems and tools are not considered a privilege as most services they offer are meant for the requirements and needs of Small and medium-size enterprises. Cloud based business intelligence systems are thus meant to offer competitive advantage and ensure the success of Small and Medium-sized Enterprises.  In this paper, nonetheless, a wide spectrum of issues related to Business intelligence Analytics in SMEs is addressed. Various SME managers and Business intelligence practitioners might find this research brief, but it is a concise summarization of useful business intelligence analytic concepts in Small and Medium-size enterprises as it attempts to apply this cutting-edge technology in specific business sector including business culture, decision-making and organization, business continuity and even technology.

Statement of the Problem

Over the years, data warehousing as become an integral and essential component of modern business support systems. To be competitive even small and middle-sized enterprises are collecting large volumes of data and are interested in analytic tools among other business intelligence systems. SMEs are significantly important on global, national and local basis and play an integral role of any economy. In spite of various advantages of these business models, current decision support systems are inaccessible and insufficient for SMEs because of:

  1. High hardware infrastructure requirements
  2. High operational cost
  3. User experience complexities
  4. Irrelevant functionalities
  5. Low flexibility to curb the fast changing dynamic business setting
  6. Low attention to data access and significance in both large scale and Small and medium-sized enterprises.

Besides, many projects fail before and after maturity because of complex development processes. As work philosophies of large-scaled and small and medium-sized enterprises differs, it is not commendable to use analytic tools that are destined for large-scaled enterprises. In addition to one size does not fit all problem, a lot of issues exist in information identification in building data warehouse. SMEs need cheap, lightweight, simple, flexible, and efficient solution. To aim at business intelligence system with these solutions, SMEs can take advantage of cheap online and cloud based systems and web interfaces. For instance, large corporation utilized web technologies for data warehousing but there is even countless plea for such systems in SMEs.

Finally, SMEs needs real-time data analysis which induces storage and memory issues. Traditional On line Analytical Processing, OLAP, tools are often based on cumbersome h9 software and hardware architecture and thus they call for significant resources to ensure high performance. Data aggression limits their flexibility. At the same time, in-memory databases offers noteworthy performance improvement. Lack of input and output operations allow fast query response. In-memory databases does not require pre-aggressions, recalculations, and indexes. They thus cause systems to be more flexible as analysis is likely to be more detailed without its pre-definition process.  Moreover, by 2012, Schlegel et al. (2006) indicated that 70% of global 1000 firms will load data details into memory as the main approach of optimizing Business Intelligence application performance. Has business, both SMEs and large-scaled adopted this process of loading data into in-memory to optimize Business Intelligence application performance?

Notwithstanding its significance in businesses and decision making processes, there is a shortage of research on Business Intelligence and Business Analytics in Small and Medium-sized Businesses, as most of Business Intelligence and Business analytics are mainly adopted in international, multinational, and large-scaled enterprises and thus most research work focus Business analytics and Intelligence in these businesses (Grabova et al. 2010; Scholz et al., 2010). In a literature review conducted by Jourdan et al (2008) on business intelligence and business analytics, various articles published from 1997 to 2006 in leading information system journal were collected and analyzed. Despite that they focused mostly on Business intelligence and Analytics in general but not on Business Intelligence and Analytics in Small and Medium-sided Businesses, their extensive literature review yielded no extent literature on business intelligence and business analytics in Small and Medium Sized businesses.

Objectives of the Study

Thus, this paper proposes an adapted and original Business intelligence solution for Small and Medium-sized enterprises. To this aim, this study first reviews various research papers that are related to Business intelligence and analytics in Small and Medium-sized enterprises. Then it gathers facts through interview on how SMEs have adopted and are using business intelligence for their competitive edge. Besides, this paper provides an extensive review of literature on Business Analytics and Business intelligence in Small and Medium-sized enterprises.  By synthesizing, analyzing and collecting various literatures within this domain, this paper presents the current state of research topics on Business Intelligence and Business analytics and reveal various prospective literature gaps that are needed for further studies.

Specific Objectives

  1. To analyze how business intelligence and business analytics has influenced decision making processes in Small and Medium-sized enterprises.
  2. To analyze how business intelligence and business analytics has enhanced operations and production processes in Small and Medium-sized enterprises.
  3. To analyze how the current body of literature has discussed issues related to business intelligence and business analytics in Small and Medium-sized enterprises.
  4. To come up with a literature artifact that is rich in research and practice that can facilitate the adoption process of Business Analytics and Business intelligence in Small and Medium Sized enterprises.

Research Questions

How Business Intelligence Technologies create value for most operational and production activities?

Most business operations and metrics together with business intelligence and database systems are likely to benefit for real time-data that provide analytics. This data have latest information about services and products and can influence desi son making processes.

What are the Business Intelligence and business Analytics Challenges that every company facing?

Major issues in Business Intelligence and business Analytics consumption in SMEs is the lack of intelligence concepts and inability to use even traditional systems to retrieve outputs. Besides data visualization practices are not effective and cannot transform complex business analytics to simplified insightful and colorful analytics for every level of employees to understand the business goals. Data sources are the biggest sources of issues for most organizations to adopt Business intelligence tools and related technologies. Poor data sources means poor analytics and thus poor data decisions. Moreover, some organizations may find it hard to install and configure Business Intelligence Applications and they even feel a burden to maintain these applications. Basic troubleshooting, coding and simplified data structure is mandatory in business intelligence and business analytics. Most managers and business leaders lack these expertise and are not effective in handling data related issues and thus affecting Business intelligence implementation.

How Business Intelligence Applications influence the process of decision making?

Larsen and Keller (2017) note that every business making decisions for their growth. However, how effective are these decisions, are these decision geared to growth and continuity? What are perquisites do they consider while making these decisions and significant business considerations.

Decision making is critical to all organizations and it increases with issues in an organization so that in every business issue, right decision can be made to influence processes and operations. These decisions also ensure that employees produce quality and are productive. Business intelligence processes and activities including intelligence, esource availability, data analysis, and internal components are important while making informative and real-time decisions.  Besides, Business Intelligence applications uses all these components to provide the needed visualizations to business managers and leaders.

What are the leading technologies in Business Intelligence?

Currently, Tableau software application is among the leading Business Intelligence application and software. Its key component include Tableau Desktop, Tableau online and Tableau Server that ensure that business applications are easy develop, maintain, and deploy to meets  an organization’s business analytics requirements. With this components, users can comment, view, modify and interact with analytics form tablets, mobile, laptop and desktop devices (Sherman, 2015). Tableau software application has a greater interactive platform that enable most organizations to adopt is a primary Business intelligence tool. Tableau software application is simple, easy to use and learn and users can came up with various modules per department. Besides, Tableau software application handle many reports, analytics, graphs and statistical analysis. Thus it is an ideal tool for most business.

What is the future of Business Intelligence future trends?

By 2022, most Small and medium-sized enterprises and large-scaled corporations will be using Business intelligence and Business analytics to performance, enhance productivity and reduce risk factors. Cloud competing together with Business intelligence and business analytics is likely to produce the need quality of analytics with real-time data and will be an easy way out for most organizations. Business Intelligence technologies will enhance the significance of data analysis as most enterprises will be using data as a resource and through a dashboard. By the same year, SAA, SMB BI, and MSBI among other organizations will be coming up with business intelligence applications among other analytic components.  Currently and days to come, data will be a key resource and will determine the success of an organization as well as its future because every enterprise s doing business based on historical data.

Significance of the Study

This research increases the pool of research on the significance of business intelligence and business analytics in Small and medium-sized enterprises. To SMEs this research affirms that Business intelligence and Business analytics are meaningful and useful process for comprehensive business analytics and for identifying various business trends. Besides, it affirms that these businesses can alter and analyze promotional activities relative to various market trends.

When implemented, the knowledge acquired in this paper will increase returns on investments and enable SMEs to reach their goals. Furthermore, employees in these businesses will be more focused to meet business expectations as all business process will be automated and goal oriented. Based on the data collected, there will be much focus on pressing business areas and allocation of right responsibilities will be a major considerate as information will be used as resources.

Limitation of the Study

As most research data is form the United States, other business environments my experience issues while implementing most concepts in this research due to technological differences. Also, as the research is qualitative and depends on interview for data collection, Covid-19 passed a lot of issues while collecting from managers. Connectivity issues and online meetings were challenging.

Scope of the Study

This research paper is limited on the effects, advantages and disadvantages of Business intelligence and business analytics in SMEs. Also it discusses the extent to which small scaled businesses have adopted business intelligence and business analytics. The technical bit of business intelligence and business analytics will not be discussed in this paper. Also, other effects of business intelligence and business analytics that are not business related will not be discussed in this paper.

 

 

 

 

 

 

 

 

 

 

 

 

References

Burstein, F., & Holsapple, C. W. (Eds.). (2008). Handbook on decision support systems 2: variations. Springer Science & Business Media.

Chiang, R.H., P. Goes, and E.A. Stohr. (2012). Business intelligence and analytics education, and program development: A unique opportunity for the information systems discipline. ACM Transactions on Management Information Systems (TMIS). 3(3): p. 12.

Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). An overview of business intelligence technology. Communications of the ACM54(8), 88-98.

Eder, F., & Koch, S. (2018). Critical success factors for the implementation of business intelligence systems. International Journal of Business Intelligence Research (IJBIR)9(2), 27-46.

Evelson, B. (2008). Topic Overview: Business Intelligence. Forrester.com. Retrieved from https://www.forrester.com/report/Topic+Overview+Business+Intelligence/-/E-RES39218.

Gartner. (2015). Flipping to digital leadership: insights from the 2015 Gartner CIO agenda report.

Jourdan, Z., Rainer, R. K., & Marshall, T. E. (2008). Business intelligence: An analysis of the literature. Information systems management25(2), 121-131.

Grabova, O., Darmont, J., Chauchat, J. H., & Zolotaryova, I. (2010). Business intelligence for small and middle-sized entreprises. ACM SIGMOD Record39(2), 39-50.

Grabova, O., Darmont, J., Chauchat, J. H., & Zolotaryova, I. (2010). Business intelligence for small and middle-sized entreprises. ACM SIGMOD Record39(2), 39-50.

Lawton, G. (2009). Users take a close look at visual analytics. Computer42(2), 19-22.

Larsen & Keller Educ. (2017). Business Intelligence and Analytics.

Llave, M. R. (2017). Business intelligence and analytics in small and medium-sized enterprises: A systematic literature review. Procedia Computer Science121, 194-205.

Schlegel, K., Beyer, M. A., Bitterer, A., & Hostmann, B. (2006). BI Applications Benefit from In-Memory Technology Improvements. Gartner, October.

Sherman, R. (2015). Business Intelligence Applications. Business Intelligence Guidebook, 337–357.

Scholz, P., Schieder, C., Kurze, C., Gluchowski, P., & Böhringer, M. (2010). Benefits and challenges of business intelligence adoption in small and medium-sized enterprises.

Yeoh, W., Koronios, A., & Gao, J. (2008). Managing the implementation of business intelligence systems: a critical success factors framework. International Journal of Enterprise Information Systems (IJEIS)4(3), 79-94.

 

This is OK BUT you should consider the following. Writing Chapter 4: EXPERIMENTAL RESULTS (As seen in you CANVAS announcements) Based on your work on chapter 3, this chapter will be used to present the results gained from your algorithm. You are to use representations such as statistical, visual, or others to showcase your work. This will need to consider possible comparisons with other related work to show merits of your own work. (10 to 15 pages) 1. Present the results followed by a short explanation of the findings. For example, you may have noticed an unusual correlation between two variables during the analysis of your findings. It is correct to point this out in the results section. However, speculating as to why this correlation exists, and offering a hypothesis/project about what may be happening, belongs in the discussion section of your paper. 2. Present a section and then discuss it, before presenting the next section then discussing it, and so on. This is more common in longer papers because it helps the reader to better understand each finding. In this model, it can be helpful to provide a brief conclusion in the results section that ties each of the findings together and links to the discussion. The critical part of writing Chapter 4 is to present the findings from the data collection process in Chapter 3. Basically, you are informing the reader of what was discovered. This chapter integrates a narrative, numerical, and/or tabular presentation of the outcomes of the study, depending on whether you have conducted a qualitative or quantitative study. In Chapter 4, you will report the results of the data analysis for each variable and measurement instrument that was discussed in Chapter 3. It will be a good practice to “explicitly state” the HARDWARE platform and SOFTWARE platforms/packages/utilities that were used for your experiment. For example, if you conducted a qualitative study, You would provide a narrative (Setup/Scenario) description of the findings in relation to the research questions. If you conducted a quantitative study, You could include descriptive statistics for each participant or for the entire group (or both). • Descriptive statistics are the basic level of statistical analysis for a data set from a sample group. Typically, reported statistics include the mean, median, mode, variance, and standard deviation. If you conducted an intervention for a large group or more than one group of participants in the study who received different treatments, you could apply inferential statistics to indicate any differences observed in performance before and after the intervention or between the two groups (if appropriate). • Inferential statistics are the higher level of statistical analysis where inferences are made from a sample to a population. Inferential statistics may also include hypothesis/project testing and set probability levels to test for statistically significant differences between groups (or treatments). Also table titles are put above the table. Figure titles are put below the figure. Please include such also inside your text. Also, be very careful with references since chapter 4 should be your own work, again be very careful with including references that might indicate either plagiarism or being there with no actual use. Do these changes as you submit your final assignment.

 

Contents

Methodology. 3

Setting. 4

Participants. 4

Measurement instruments. 5

Procedure. 6

Data Analysis. 8

References. 12

 

 

Methodology

Companies face numerous issues and challenges while conducting corporate practices. Business management in companies is the synthesis of several industries, teams, units, so it is necessary to coordinate with each component to make smarter decisions to establish the correct organizational strategies for company. When firms automatically improve their standards and raise their market stability, they would have issues and development difficulties. In order to develop an efficient approach to solve problems and resolve difficulties, the organization needs a sophisticated and comprehensive strategic strategy. The corporate activity of any company is interconnected with computer technology and computer processing.

Enterprise development is dependent on the management of systems and data processing, but corporate executives and management tycoons consider a metric study of company success to recognize the demand and competitive performance expectations of the organization. To improve the operation, the organization wants an efficient approach in the form of high loads, complexities, and problems. Market Analysis Technologies will be the answer and these applications or dashboards will improve visual and interactive insights of large databases (Joshi, 2017). It will inspire the actions of the staff of the organization and incorporate new concepts to minimize risk factors and enhance the operation of the business.

The processes of Market Intelligence have multiple aspects, such as data criteria, data analysis or compilation, data analysis and creation of the interactive dashboard framework. For each agency, these dashboards are different and subsequent administrators may be able to grasp the existing condition and formulate better strategic methods centered on the BI dashboard ‘s outcomes. These dashboards and analytics are based exclusively on performance in real-time, but they are reliable analytics.

Setting

Our ability to answer the mysterious to the established is inspired by our inquisitive impulses, which are the foundation of the analysis. As such, this chapter deals with the preferred analysis method and the rationale for the subject matter. The rationale for the chosen population as well as the system of data collection and review procedures are also established in this portion (Ravasan,& Savoji,2019). The study suggested would concentrate on investigating and evaluating the importance of utilizing Market Intelligence as a medium for decision-making and importance generation. This research is carried out in the sense of qualitative study since it encourages a subject to be analyzed and the researcher to draw conclusions. The reason for preferring qualitative analysis, of course, resides in the topic of the report, the dilemma definition, and the test questions.

Participants

In order to assess the advantage of utilizing Market Intelligence as a method for decision-making and value generation, this analysis aims at small mid-size enterprises (SMEs). By integrating Business Intelligence to simplify their corporate operations and to get a strong vision of developments in the target industries, this category of corporations has earned large attention for witnessing changes. As such, the Small Mid-Size Businesses are identified by this study, since they work in many locations so that they can be conveniently accessed. The research may approach members in institutions with decision-making positions. The head of divisions, specifically administrators, would be the primary category since they are engaged in decision-making processes including the implementation of Market Intelligence in organizations. However, it may often target those workers who have worked in an enterprise with Market Intelligence applications in the decision-making processes. This research considers a 5% level of accuracy, 95% confidence level and 50% level of inconstancy.

A community of 150 small and medium-sized companies in the United States has been listed for this study. Notably, the U.S. has over 30 million small to medium-sized enterprises. A random population of 150 firms would, however, be regarded for the intent of this study. From the above estimate, a sample size of 110 small to medium sized companies appears. As such, the data collection method utilized for this study would be applied completely to a sample size of 110. I am confident the sample size will have accurate outcomes for the focus population. The analysis methods are structured to quantify and provide a realistic picture of what they plan to quantify.

Measurement instruments

The details would be obtained by holding interviews with the numerous retail company administrators, i.e. small mid-size companies (SMEs). From questioning the different staff of these retail companies, details can also be compiled. Semi-structured interviews may be included as it facilitates further research to obtain more knowledge from the interviewee. It is also distinguished by great precision, allowing all the details required in one round to be obtained. The new Corona Virus pandemic prevents the usage of face-to – face interviews to obtain details, so telephone calls to the corresponding interviewers would be the primary way of performing the interviews (Pyylampi, 2019).  Face to face interviews, however, may still be held, but only as required. The use of interviews as the technique for data collection was mostly focused on its broad variety of advantages; its comprehensive existence, which allows to obtain the required details as well as additional knowledge that can point out the different criteria in these industries. The usage of telephone calls to perform the interviews helps the interviewer to gauge the verbal signals of the subject that may help assess their feelings on the subjects being addressed.

As the interview can be done anytime and at any time at the discretion of both the investigator and the interviewee, this approach often saves a lot of time. This approach also has its benefits, such as increasing the likelihood of collecting more specific and accurate details, considering the limited usage of face-to – face interactions with the participants, since the investigator may participate in a more dynamic dialogue with the client. The participant may now be more engaged in the discussion and may thus be willing to ask for clarity on any of the issues that can include an accurate date for the participant. During the interview, the interviewer is therefore able to retain track of the attention of the interviewee, while eliminating any kinds of disruptions that might result in a pause in the overall data collection method. The researcher will often record the participant’s verbal and non-verbal signs, which may be an indicator of the frustration of the questions being posed.

Procedure

The majority of our interview questions are open-ended because they enable respondents to provide unrestrained answers, thereby facilitating the achievement of the study goal to collect more accurate details. About 20-30 minutes each of the interviews will continue. For a given moment, one interviewee may be questioned, and one question being answered at a moment. The order of the questions to be answered would meet the following guidelines.

  1. Factual queries, including those concerning the emotions and assumptions of the individual, would be posed before the more contentious questions.
  2. For the whole duration of the interview, fact-based questions would be interspersed.
  3. Before any regarding the past as well as the future, concerns about the current would be addressed.
  4. An ability to provide some further detail as well as a general opinion and feedback should be offered to the interviewee at the conclusion of the interview.

At the conclusion of the research report, my key goal is to perform four interview classes, two of which will include interviews with the separate administrators of the chosen SMEs, and two will include the gathering of data from the sampled workers of these organizations. Just two of the interviews with the administrators and workers of one of the retail sectors were planned at the time of this study proposal. I would use the data obtained from managers to try to determine their thoughts on the need to implement Market Intelligence into their organization, since this will help accomplish the aim of this project. I can arrange the remaining two interviews with two separate organizations, one with the supermarket sector supervisors and the other with the workers. At the conclusion of all the interviews, any statements and suggestions taken during the interview would be recorded.

The exact data logging protocol for the study is discussed below.

I will collect the history and general operational knowledge from records and websites of the organization. Participants in this study would include heads of teams, in specific administrators, in the organization chosen, whose work and activities provide a substantial contribution to the decision-making process. For this study, the chosen subjects would then be included in interviews for data collection. The records and websites of the company would be used as appropriate.

Emails specifying the intent of the interview and the reasons for using the chosen participants in the study will be submitted to the respective participants prior to the interviews. In September 2020, mobile interviews and face-to – face interviews with participants will be organized over a span of 31 days. These interviews can, on average, last approximately forty minutes for each person. All these interviews will be registered, however after securing the interviewees’ permission. Notes that would be taken in place of incomplete records would follow these notes (Joshi, 2017). The interviewees would be entitled to schedule their own date and time for telephone interviews during the month of September 2020. Nevertheless, with face to face interviews, the interviewer and the interviewee would settle on the day, period, and place.

The participants would be briefly exposed to the context of the study and the explanations for their participation before the interview starts. This brief introduction will be approximately four minutes long. As the conversation continues, additional documents would be required dependent on the topic and only the interviewee is able to provide it.

Data Analysis

After listening to each of the interviews many times, I would transcribe each of the documented interviews in order to provide the participants with vivid accounts. I would then cross-reference all the transcripts with the relevant members of the reported interviews, in which I can work out all the clarification and misunderstandings. This is critical since it impacts the quality of the data gathered and the interpretation of the findings. By following the third (cross-case research) stage of Cope’s four-step research, I would evaluate the knowledge gathered through the interviews.

In this report, cross-case research of how corporations profit from the usage of Market Intelligence as a method for decision-making and value development would be cross-participant analysis, because the participants’ perspectives are the topic of interest in the chosen situation. Via content review, I would also reflect on the similarity and differences. We can extract this from our results.

Market processes and analysis organizations link to database systems and Business Intelligence software apps will use real-time data linked to database systems to incorporate the current product or service information in the analyses delivered such that the latest outcomes are available or analytics results or live statistics such that either the company mana made a big difference Decision-making guided by facts may influence the principles and expectations of organizations.

Organizational methods may affect the actions of employees such that BI analytics will impact the activities and management processes of employees such that it inevitably contributes to enhancement in company results. It would help to maximize consumer happiness, performance, standard of service, and eliminate time-wasting.

A lack of awareness of the new BI technology and resources is the main problem because most firms do not have successful market intelligence frameworks, so they are unable to recover planned performance (Brook & Frith, 2016). The practice of data visualization can be productive and turn complicated market analytics into simplified, vibrant, and concise analytics so that the company priorities can be grasped at any level of an employee.

Data is efficient in addressing scenarios, but management feels it is impossible to tackle BI technologies at the point. Sources are the greatest obstacles for enterprises to accept BI technology because analytics would be hard to build and that will contribute to low quality analytics. Employees of organizations can find it challenging to install and customize the BI Applications and they feel a responsibility to manage the application method. The simple coding and simplification of the data structure is the obligatory perception that often staff, or company leaders are not

The key factors are how successful their business growth is and what are the fundamental components they use when making the decisions are critical considerations for each organization making decisions about their business growth. Choice-making is a vital aspect of the company, as it can lift the organization’s challenges such that the correct decision will impact corporate processes in the circumstance as workers will deliver the standard of attempts to improve efficiency (Brijs, 2016).  Intelligence, data processing, efficiency of tools, and internal elements play crucial roles when making choices, and all these elements can be utilized by BI apps to create visualizations for corporate leaders. The choice will affect the everyday job style of the employee and predict a better future.

Tableau software is actually one of the leading and most significant Business Analytics software solutions. It includes Tableau Web, Tableau Browser, and Tableau internet, so these tools are valuable for designing, managing, and implementing market analytics for the enterprise. Mobile, tab, notebook, or screen analytics can be accessed, updated, commented, and interacted with by business users (Akgül & Tanriverdi, 2018).  It has a more social framework, but most enterprises use the Tableau framework as their BI method. In each organization in the company, it is quick to use, quick to understand and quick to introduce. Many kinds of analytics, papers, maps, and predictive research can be done by Tableau.

Relevant and validated knowledge is utilized by BI Technology such that detailed analytics can be used as main analytics to identify corporate industry patterns and adjust customer performance and change advertising practices dependent on consumer dynamics. Facts and reality are entirely different, however BI dashboards can explain the evidence provided on an organization’s gathered results, however plans can be built based on the information such that corporate priorities can be effectively accomplished.

 

 

 

References

Akgül, E., Üstündağ, M. T., & Tanriverdi, M. (2018). Business Intelligence Application For          Campaign Management In The Retail Sector. Artificial Intelligence Studies, 1(1), 8–25.

Brijs, B. (2016). The Business Case for Business Intelligence. Business Analysis for Business        Intelligence, 85–98.

Brook, H., King, C., Paulli, G., & Frith, A. (2016). Artificial intelligence. Tulsa, OK: EDC            Publishing.

Hamer, P. den. (2013). Business intelligence. Den Haag: Academic Service.

Joshi, K. (2017). Business intelligence. Place of publication not identified: Pearson.

Kowalczyk, M. (2017). Study E: Business Intelligence and Analytics – Decision Quality and        Insights on Analytics Specialization and Information Processing Modes. The Support of          Decision Processes with Business Intelligence and Analytics, 99–116.

Larsen & Keller Educ. (2017). Business Intelligence and Analytics.

Oltmann, S. (2016, May). Qualitative interviews: A methodological discussion of the interviewer             and respondent contexts. In Forum Qualitative Sozialforschung/Forum: Qualitative      Social Research (Vol. 17, No. 2).

Pyylampi, M. (2019). Creating Interview Guides for the Minimalist Organizational Design            Project.

Ramakrishnan, T., Khuntia, J., Kathuria, A., & Saldanha, T. (2016). Business Intelligence Capabilities and Effectiveness: An Integrative Model. 2016 49th Hawaii International         Conference on System Sciences (HICSS).

Ravasan, A. Z., & Savoji, S. R. (2019). Business Intelligence Implementation Critical Success      Factors. Advances in Business Strategy and Competitive Advantage Applying Business          Intelligence Initiatives in Healthcare and Organizational Settings, 112–129.

Roffel, S. (2020). Introducing article numbering to Artificial Intelligence. Artificial           Intelligence, 278, 103210.

Sharda, R., Delen, D., Turban, E., Aronson, J. E., Liang, T.-P., & King, D. (2018). Business          intelligence, analytics, and data science: a managerial perspective. New York: Pearson.

Sherman, R. (2015). Business Intelligence Applications. Business Intelligence Guidebook, 337–   357.

Williams, S. (2016). The Strategic Importance of Business Intelligence. Business Intelligence        Strategy and Big Data Analytics, 51–68.

 

 

 

 

 

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