The implications of BI on business process and data quality for governmental agencies in Saudi Arabia and the barriers to its implementation: The Case of SIDF
Research Questions
The research questions of the research study are as follows:
How can effectively Business Intelligence support the overall business processes in SIDF?
What the needed improvements in the data quality to generate the benefits from BI in SIDF? Further, what improvement in the quality of data can be gained with the implementation of BI in SIDF?
What factors are creating challenges in the BI implementation for governmental agencies in KSA in general and SIDF in specific?
Research Problems
Business Intelligence has become an important aspect in the organization. Business intelligence helps different departments of the organization to share data and facilitate in improving the business processes. Business intelligence is now used widely in different industries as it has several benefits. These benefits include; online analytical reporting and processing, data mining, data analysis, forecasting and predictive modelling, business performance improvements and several others (Rud, 2009).
Business intelligence allows different departments to share the knowledge and information and then this data is used for optimal decision making. With so many benefits and usage of Business intelligence, it is important that organizations implement it to enhance their business processes and become more efficient. For this purpose, this research study will analyse how governmental agencies can improve their business processes. The research study will use a case study of one of the leading industrial development fund organization, Saudi Industrial Development Fund. SIDF currently does not haveeffective business intelligence, and this research will discuss how SIDF can improve its performance by using BI, and define all related challenges.
Literature Review
For understanding the relationship and impact of business intelligence with data quality, it is critical to understand its need and importance. Hence, the underlying section provides the overview of the involved variables and its respective relationship as well as implications.
Business Intelligence
Business Intelligence can be defined as a term that covers different themes including applications, tools, infrastructure, and best practices that are being used for data and information analysis (Lönnqvist & Pirttimäki, 2006). More specifically, in business intelligence system the data of operations is combined with tools of analysis to present the complex data as the unique information set to the decision makers. It delivers critical information on the time of making decisions (Lloyd, 2011).
As a matter of an identified fact that refers to its direct involvement in the decision making process; therefore, it helps the organizations in developing effective strategies for winning competitive edge while improving its overall performance. Therefore, many researchers have argued that beyond its technological nature, it has also has a strategic nature. Because of its strategic nature, it has many benefits that are dispersed throughout the processes of the business. However, some studies have also noted that the strategic role of business intelligence directly impacts the culture. Hence, its complete role and benefits are difficult to evaluate (Fincher, 1978). A study has found that there are many intangible benefits of business intelligence that are being a part of the information technology benefits (Gibson et al., 2004).
Data quality and its importance
Business intelligence is generated with the data, and it is also aimed at improving the quality of data available for a business decision. Resultantly, it makes it important to understand the idea and concept of data quality. Data quality can be defined as the level of excellence, worth, and value of information (Geiger, 2004). Analyzing data to determine and predict the trends of the market, the business, the product, and the service to improve the performance of the organizations has become an important element to make the organization successful in the competitive environment (Daniel et al., 2008).
On the other hand, the quality of data is itself is the most challenging issue and serious problems for all the organizations of different sizes these days. Therefore, many studies have criticized that the analysis and cleansing of data are very important before going to use it. The study has stated that not only a big mistake in a data but a small error can lead to deteriorate the whole data quality and its resulting decision making. Alongside, it is importance to understand that the data quality problems begin from the little errors and mistakes (Chen, Chiang & Storey, 2012).
The relationship between the data quality and Business Intelligence
The relationship between the data quality and the business intelligence is somewhat interdependent. Some studies have found that business intelligence helps the organization to improve the quality of data (Verbitskiy and Yeoh, 2011); however, in contrast, other studies have found that the challenges of data quality have further increased with the application and implementation of the business intelligence (Marjanovic, 2007). The study of Geiger, (2004) has stated that the business intelligence exposed the data quality as one of the most critical issues. In contrast, another study has argued that due to the high familiarity of the management with business intelligent tools and processes, the demand for quality data has increased. Due to level of interdependence and relationship, the data of poor quality using business intelligence can be very dangerous for the business and the organizations. Business intelligence is used to extract data for the decision making; however, the quality of data can cost as well as benefit the business in billions (Daniel et al., 2008). Therefore, it can be said that data quality and business intelligence are directly related to each other as one factor directly impacts the other one.
Challenges and issues in the application of Business Intelligence in the organization
The introduction to new concepts and ideas are always subject to various issues and challenges in any organization. In a similar way, the implementation of the BI for the improvement of data quality is also subject to various challenges specifically in the implementation phase.
Hannula & Pirtimaki, (2003) in a study found the major challenges in the implementation of business intelligence such as the breakdown of the information from different departments, the challenge in the integration of the system with the operations and management, and the establishment of maintaining the quality of data as expected. Business intelligence can only support and integrate efficiency to the business and the data quality when it goes well to understand the needs of the business and the first priorities for BI implementation (Golfarelli, Rizzi & Cella, 2004). Generating useful information using data is a separate function from using that information for the course of actions. Hence, even if the intelligence based information is extracted from the data, its implementation has all chances to fail which can adversely affect business objective. It needs a proper management of the system along with effective product or service designing based on the information (Gangadharan & Swami, 2004). The analysis of information from multiple processes is another big challenge for business intelligence system. Most of the companies are involved in collecting huge data from the business surroundings, and it often become a problem to deal with that information and data using software and bring all that information together (Chen, Chiang & Storey, 2012).
The role and importance of business intelligence in governmental agencies
A study has found that business intelligence is very important for every aspect of government agencies. With the implementation of BI, the employees of every public organization are more expected to deliver best services to the customers and modernize the decision making process. The process is mainly depending on the environment of the organization regarding its resources and the budget. The government agencies have faced many problems in their management such as execution of information with accuracy and controlling employees regarding their moral with the implementation of the new system (Wilensky, 2015). Despite these challenges, the government agencies are now using business intelligence to improve different areas of their management and service processes. Using business intelligence, the government sectors can make their employees make effective decisions based on useful and extracted information from the huge data (Nycz & Polkowski, 2015).
Hypothesis
The hypotheses of the research are as follows:
Business intelligence plays an important role in improving business process (in term of performance and decision-making).
Business intelligence is positively related to the data quality.
Also data quality produces an impact on the business intelligence.
Challenges in implementation negatively influences business intelligence.
Improved data quality as a result of business intelligence improves business performance.
Improved level of data quality with the implementation of business intelligence, contributes to the improved decision making.
Based on the above, hypothesis, following conceptual models derived:
Aims and Methodology
The objectives of this research study are as follows:
The relationship between the business intelligence and data quality is an undeniable fact. Hence, the aim, being the central idea of the study, revolves around the facts to ascertain the effectiveness of the BI in the specific context of the environment in government organizations in KSA. Studying the relation of the BI and data quality in the selected context will enable the organizations in identifying and determining factors that can benefit most from the implementation of the BI. As a matter of fact, the systems in the government organizations are subject to wider range of factors as compared to the other business organizations (Subashini and Kavitha, 2011), hence, review of the relationship will also provide evidence of the aspects that can create the challenges while implementation of the BI.
The research study will collect data using primary and secondary data. The researcher will collect primary data from the employees of SIDF. Both qualitative and quantitative methodologies will be used and the participants will be asked by online questionnaires and recorded interviews.Moreover, the researcher will collect secondary data from different secondary sources such as books, journal articles, newspaper, online sources and other authentic secondary sources.
Keywords
Business Intelligence, Data quality, Business Processes, Data Analytics
Reference
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, vol.36, no.4, pp. 1165-1188.
Daniel, F., Casati, F., Palpanas, T., & Chayka, O. (2008). Managing Data Quality in Business Intelligence Applications. In QDB/MUD (pp. 133-143).
Fincher, C. (1978). Institutional research as organizational intelligence. Research in Higher Education, vol.8, no.2, pp. 189-192.
Gangadharan, G. R., & Swami, S. N. (2004, June). Business intelligence systems: design and implementation strategies. In Information Technology Interfaces, 2004. 26th International Conference on (pp. 139-144). IEEE.
Geiger, J. (2004). Data quality management: the most critical initiative you can implement. Data Warehousing, Management and Quality, Paper, 098-29.
Gibson, M., Arnott, D., Jagielska, I., & Melbourne, A. (2004). Evaluating the intangible benefits of business intelligence: Review & research agenda. In Proceedings of the 2004 IFIP International Conference on Decision Support Systems (DSS2004): Decision Support in an Uncertain and Complex World (pp. 295-305). Prato, Italy.
Golfarelli, M., Rizzi, S., & Cella, I. (2004, November). Beyond data warehousing: what's next in business intelligence?. In Proceedings of the 7th ACM international workshop on Data warehousing and OLAP (pp. 1-6). ACM.
Hannula, M., & Pirttimäki, V. (2003). Business intelligence empirical study on the top 50 Finnish companies. Journal of American Academy of Business, vol.2, no.2, pp. 593-599.
Lloyd, J. (2011). Identifying Key Components of Business Intelligence Systems and Their Role in Managerial Decision making (Doctoral dissertation, Intel Corporation).
Lönnqvist, A., & Pirttimäki, V. (2006). The measurement of business intelligence. Information Systems Management, vol.23, no.1, pp. 32.
Marjanovic, O. (2007, January). The next stage of operational business intelligence: Creating new challenges for business process management. In System Sciences, 2007. HICSS 2007. 40th Annual Hawaii International Conference on (pp. 215c-215c). IEEE.
Nycz, M., & Polkowski, Z. (2015). Business Intelligence in a Local Government Unit. Proceedings of Informing Science & IT Education Conference (InSITE), 301-311.
Rud, O. P. (2009). Business intelligence success factors: tools for aligning your business in the global economy. John Wiley & Sons.
Subashini, S. and Kavitha, V., (2011). A survey on security issues in service delivery models of cloud computing. Journal of network and computer applications, vol.34, no. 1, pp.1-11
Verbitskiy, Y., & Yeoh, W. (2011, January). Data quality management in a business intelligence environment: from the lens of metadata. In ICIQ 2011: Proceedings of 16th International Conference on Information Quality. University of South Australia.
Wilensky, H. L. (2015). Organizational intelligence: Knowledge and policy in government and industry (Vol. 19). Quid Pro Books.
