Evaluation Of Research And Its Impact On Databases And Business Intelligence Practice/Organizational
Identification of the organizational data management problem
In any organization, data is considered as the major foundations of information, knowledge as well as ultimate wisdom for making correct decisions and actions. Data management is the function of planning, controlling and delivering data effectively in the enterprise. Effective data management can be helpful to minimize errors, improve efficiency, protection from data related issues and risks and enhance the quality of data. In the present study, the organization data management has been discussed along with the impact of the problem. In addition, the reason for existing the issue and evaluation of the articles are discussed in the study.
Bell, Bryman, and Harley (2018) stated that it is important to deliver customer value as well as creating competitive benefits required by an organization in order to acquire information external to the enterprise. The sales of the organization interact directly with external environment customers and competitors along with another market. On the other hand, social business intelligence has emerged as one of the major components of a business organization. The innovation of IT has been forced taking responsibility for cloud data management as well as cut costs of business. There are 69% of organizations believed that data protection; privacy as well as compliance is the responsibility of cloud service provider (Wamba et al., 2017). It has a significant effect on data breach as well as regulatory compliance.
On the contrary, electronic ticket provider services are increasing fast that makes competition between the organizations (Wamba et al., 2017). In addition, most of the organizations include the same type of services to provide service to the customers. The growth of present technology allows the organization taking data from social media. Hence, the organizations take social media data to make an analysis. Apart from these, the data warehouse developed for travel organizations provides the comparison of performance based on social media data (Sallam et al., 2014). In order to explore present information as well as react faster to the changing business conditions, enterprises consider real-time data warehousing technique for achieving operational business intelligence.
The organizations are realizing potential value in sharing intelligence through a common form of gaining intelligence. However, the most general form of gaining intelligence for buying data from a third party and not sharing intelligence with the competitors along with third parties. The private organizations will be helpful to mark documents, reports as well as analysis, which are sold to others for buying data from a third party. In market intelligence, it is the type of data helps the organizations gathering data (Abbasi, Sarker & Chiang, 2016). From the perspective of intelligence, studies have a intelligence as a service is considered as a more interesting domain for exploring compared to data as a service. It is one of the web-based services whether it is important to bring web-based service.
The impact of the problem
Business information, as well as business analysis in the business processes, are considered as the key components that lead to the process of decision-making and actions. It leads to enhance business performance. Business intelligence applications can allow users using more applications and reliable information as well as knowledge. In addition, business intelligence helps to take a timely decision (Soto-Acosta et al., 2016). Dynamic decisions are taken rapidly. The findings gained by the enterprise are the organization that will process the ability reacting constantly according to the movements.
Mitri and Palocsay (2015) stated that processing data commented from Facebook and Twitter, social media is achieved through the processing of converting words. As data warehouse is integrated and subject-oriented, non-volatile database as well as time-variant. It can provide support to the process of decision-making (Sallam et al., 2014). It is explained as the database, which can store present as well as historical data of the possible interest to the managers of the organization. The data from internal sources are generally combined with the data from external sources along with recognized into a central database. It is designed for reporting of management and analysis. Data mining is utilized in order to perform data analysis for discovering the unknown data characteristics, dependencies, and relationships (Soto-Acosta et al., 2015). The tools of data mining can initiate analysis of creating knowledge. The organizations require managing data assets carefully in order to ensure that the process is evaluated as well as utilized by employees and managers across the levels of the organization.
Use of market intelligence, it is required to develop an economic model in order to capture the specific pattern of producing as well as sharing market intelligence throughout the social network of the organization. On the other hand, business intelligence becomes inseparable from information technology systems and big data, There are several suppliers like to see actual intelligence as they are afraid of making own analysis (Shollo & Galliers, 2016). Through, analyzing the data from the articles it can be stated that lack of uniqueness of the reference table in distributed on various servers that cause loss of information existing in the identification documents of the same source with several points. In addition, lacks of management of the versions, as well as revisions that are specified, are the collaborators that are accessed for various information of the same source.
In present years, the amount of information has been increasing within the organizations. Hence, several organizations have deployed analytics as well as business intelligence for the solutions (Fraj, Matute & Melero, 2015). The emergence of business intelligence and tools can improve business decision-making process through providing ability to compare actual as well as planned performance (Trumpy et al., 2015). However, business intelligence and performance management of the business can processes data in the data warehouse, which is considered as the issues outlined I the direct access to the business transaction data. On the other hand, the summarized data can be managed in an organization of data warehouse. The performance issues of data caused through centralization of data can solve the performance issues through spreading business intelligence processing in the multiple stores.
The reason for exist the problem
There are several articles written regarding the issues of developing independent data marts directly from the operational data. The CDR data is considered as a different situation. CDR information is utilized for identifying fraud and quicker analysis (Sallam et al., 2014). The instance of the type of processing is considered as trading operations. Business applications can analyze web. The applications of capturing data and tracking the applications to analyze the process are important in the organizations. Hence, it is required to analyze the issues properly that and take appropriate actions so that data management problems can be solved. Tools of business intelligence can be helpful to analyze the issues and solve the issues.
Conclusion
The issues involved with data marts is developed directly from databases of the business. In addition, it is quicker for the organization rather than data warehouses. Several types of data are disconnected with the data warehouse along with the data marts leading to face issues in data consistency. Investing in effective business intelligence system is significant for an enterprise as it helps to improve efficiency of the organization. The business intelligence can be helpful to share information across multiple department of the organization. Hence, use of business intelligence tools like sisense, looker and tableau are helpful to bring competitive benefits in the market.
References
Abbasi, A., Sarker, S., & Chiang, R. H. (2016). Big data research in information systems: Toward an inclusive research agenda. Journal of the Association for Information Systems, 17(2).
Bell, E., Bryman, A., & Harley, B. (2018). Business research methods. Oxford university press.
Chae, B. K. (2015). Insights from hashtag# supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research. International Journal of Production Economics, 165, 247-259.
Chen, D. Q., Preston, D. S., & Swink, M. (2015). How the use of big data analytics affects value creation in supply chain management. Journal of Management Information Systems, 32(4), 4-39.
Shollo, A., & Galliers, R. D. (2016). Towards an understanding of the role of business intelligence systems in organizational knowing. Information Systems Journal, 26(4), 339-367.
Fraj, E., Matute, J., & Melero, I. (2015). Environmental strategies and organizational competitiveness in the hotel industry: The role of learning and innovation as determinants of environmental success. Tourism Management, 46, 30-42.
Mitri, M., & Palocsay, S. (2015). Toward a model undergraduate curriculum for the emerging business intelligence and analytics discipline. Communications of the Association for Information Systems, 37(1), 31.
Sallam, R. L., Tapadinhas, J., Parenteau, J., Yuen, D., & Hostmann, B. (2014). Magic quadrant for business intelligence and analytics platforms. Gartner RAS core research notes. Gartner, Stamford, CT.
Soto-Acosta, P., Popa, S., & Palacios-Marqués, D. (2016). E-business, organizational innovation and firm performance in manufacturing SMEs: an empirical study in Spain. Technological and Economic Development of Economy, 22(6), 885-904.
Trumpy, E., Bertani, R., Manzella, A., & Sander, M. (2015). The web-oriented framework of the world geothermal production database: a business intelligence platform for wide data distribution and analysis. Renewable Energy, 74, 379-389.
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365.