Data Quality in Banking System: Case of Azerbaijan

  • Ali Dadashzade University of Bath
Keywords: Data Quality, Banking, Business, Azerbaijani Banks

Abstract

Data quality in banking and financial sector is one of the most researched topics nowadays. With the increasing regulatory burden and increased importance of targeted sales, data quality directly influences funds and performance of banking system. In this paper, the author is aiming to define universal reasons and causes of data quality problem and apply the case to local Azerbaijani banks taking into account local managers’ personal view based on their banking experience. Key finding of the research is that unintegrated software, wrong data insertion, aging of data with the growing speed of market, corporate governance and inability to calculate true costs of low data quality to the local banks are the reasons of data quality issue in the local banks. Moreover, main costs of the data quality issue are time and money, appearance of hidden data factories, obstacles to apply and measure KPIs, uncorrelations in sensitivity analysis and ineffective marketing strategies.

References

Crosby, P. B. (1979). Quality is Free: The Art of Making Quality Certain. McGraw-Hill.

Debbarma, N., Nath, G., & Das, H. (2013). Analysis of Data Quality and Performance Issues in Data Warehousing and Business Intelligence. International Journal of Computer Applications, 79(15), 20-26.

Doyle, M. (2015). Is bad data wasting your marketing efforts. Retrieved 09 10, 2018, from https://www.b2bmarketing.net/en-gb/resources/blog/bad-data-wasting-your-marketing-efforts

Ecosystems, Insight. (2009). Data Quality and Integration in Banking. Retrieved 08 17, 2018, from https://www.insightecosystems.com/wp-content/uploads/2015/01/Data-Quality-And-Integration.pdf

Garg, A., Grande, D., Miranda, G. M.-L., & Sporleder, C. (2017, April). McKinsey & Company. Retrieved 08 28, 2018, from https://www.mckinsey.com/industries/financial-services/our-insights/analytics-in-banking-time-to-realize-the-value

Hanmath, M., & Shivaji, W. (2014). Risk Management in Banks - Regulatory Prespective.

Haug, A. (2011). The costs of poor data quality. Journal of Industrial Engineering and Management, 4(2), 168-193.

Hayler, A. (2011). Poor corporate data quality strategy impedes business progress. Retrieved 09 03, 2018, from https://www.computerweekly.com/opinion/Poor-corporate-data-quality-strategy-impedes-business-progress

Lynch, R. L., & Cross, K. F. (1992). Measure Up!: The Essential Guide to Measuring Business Performance. Mandarin.

MarketingSherpa. (2007). E-commerce Benchmark Guide 2007. MarketingSherpa.

Masayna, V., Koronios, A., Gao, J., & Gendron, M. (n.d.). Data quality and KPIs: A link to be established.

Michelberger, K. (2016). Corporate governance effect on firm performance; A Literature Review. Regional Formation and Development Studies, 3(20), 86-94.

Neri, M. (2012). Data quality in banking : regulatory requirements and best practices. Journal of risk management in financial institutions, 5, 146-161.

Oracle. (2017). Database Data Warehousing Guide. Retrieved 08 20, 2018, from https://docs.oracle.com/database/121/DWHSG/concept.htm#DWHSG8075

O'Reilly, C. A., Chatman, J., & Caldwell, D. F. (1991). People and organizational culture: A profile comparison approach to assessing person - organization fit. Academy of Management Journal, 34(3), 487-516.

Redman, T. C. (2013). Data’s Credibility Problem. Harvard Business Review.

Redman, T. C. (2016). Bad Data Costs the U.S. $3 Trillion Per Year. Harvarad Business Review.

Shahin, A., & Mahbod, M. A. (2007). Prioritization of key performance indicators, An integration of analytical hierarchy process and goal setting. International Journal of Productivity and Performance Management, 56(3), 226-240.

Singh, R., & Singh, K. (2010). A Descriptive Classification of Causes of Data Quality Problems in Data Warehousing. International Journal of Computer Sciences Issues, 3(2), 41-50.

Statista. (2018). The Statistics Portal. Retrieved 09 21, 2018, from https://www.statista.com/statistics/282197/global-marketing-spending/

Steerman, C. (2016). Data Quality – Overcoming the Biggest Hurdle for Compliance and Risk Management. ComplySci.Inc.

World Bank. (2007). Doing Business 2007: How to perform. Washington: World Bank.

World Bank. (n.d.). Operational Risk - Risk Assessment. Retrieved 09 05, 2018, from http://siteresources.worldbank.org/EXTFINANCIALSECTOR/Resources/282884-1239831335682/6028531-1239831365859/G-Operational_Risk_Assessment-Fulton.pdf

Yerby, J. (2013). Legal and ethical issues of employee monitoring. Online Journal of Applied Knowledge Management, 1(2), 44-55.

Published
2018-09-12
How to Cite
Dadashzade, A. (2018). Data Quality in Banking System: Case of Azerbaijan. International Journal of Finance & Banking Studies (2147-4486), 7(2), 1-8. https://doi.org/10.20525/ijfbs.v7i2.877
Section
Articles