Data is an asset central to every sector, such that its absence or poor record leads to fatal consequences that could amount to the relinquishing of a business. Among every other sector, the financial sector stands tall in the utilization of data, as evident in the global data usage report in 2021, where the sector contributed 20% of global data consumption, making it one of the top data-consuming sectors alongside tech and retail. As crude oil is the fuel for Nigeria’s economy, so is data for the financial sector.
It’s a critical asset that helps compile quantitative and qualitative information guiding decision-making, customer service, regulatory compliance, and operational efficiency. Unfortunately, the Nigerian financial sector battles data inefficiencies, posing significant hurdles in redirecting the flow of quality-driven operations. This is proven by the PricewaterhouseCoopers (PwC) report in 2022, indicating that about 60% of Nigerian banks struggle with issues related to data integrity, data reporting, and risk in compliance. These issues are not far from the complexities of global and local financial regulations and the recent shift from traditional to digital banking.
As a rescue, the role of business analysts becomes vital in coordinating and preserving data for several benefits. Addressing these issues demands that their professional value tail data from the gathering stage to the utilization stage, ensuring that it is accurate, complete, secure, and in compliance with appropriate regulatory standards. This article is therefore poised to explore the underlying data challenges faced in Nigeria’s financial sector and proffer strategic solutions to business analysts for better maintenance and management of data.
Importance of Data Quality in the Banking Sector
Data quality is so important to the banking sector that without accurate data, funds, savings, and the investment of clients cannot be monitored or accounted for. Preserving data majorly helps for decision-making, allowing banks to stay informed on the actual scheme of things. When there’s poor data, wrong decisions can be taken, presenting an evaluation and assessment risk. This is directly linked to the 2021 report of the Central Bank of Nigeria (CBN), where 25% of unpaid loans were as a result of inadequate data collection and assessment.
Data quality also improves overall operational efficiency, as accurate data fuels the action of reasonable courses that advance the prospects of a firm. For instance, knowing that there’s a decline in “current” account users, a strategy can be coined to attract people towards being users, which in turn translates to profitability for the bank. In correlation, McKinsey’s report in 2020 revealed that banks that maintain data quality in terms of management are bound to experience a 30% improvement in operational efficiency.
Also, when banks practice high data quality, it often points to submitting to regulatory authorities to minimize the fears of customers when they notice that banks aren’t enrolled or approved by national financial bodies. Hence, Nigerian banks comply with regulations such as Know Your Customer (KYC) and Anti-Money Laundering (AML), and in cases where authorities like these are not aligned with, CBN penalties might come into play, further leading to reputational damage and asset loss.
Data preservation and maintenance, therefore, shouldn’t be an option but a mandatory demand, as it holds the key to financial flourishing in areas like risk management, customer experience, regulatory compliance, and foreign investors.
Challenges the Nigerian Banking Sector faces in Data Quality
Inconsistent and Inaccurate Data Entry:
Data entry in some firms is rendered manually, which is labour-intensive and can result in human errors that cause inconsistent reports or data across units. There’s a lack of access to modern technologies that could transform this process; hence, data quality threatens operational cohesion. This is in consonance with Accenture’s survey, revealing that 47% of Nigerian banks battle multiple errors as a result of traditional data entry formats.
Incomplete and redundant data:
Data in some banks is either incomplete or too old to be useful. While data are carefully preserved in some banks, they are not updated or aligned with recent trends. This often hampers effective financial analysis and decision-making, as new dimensions or strategies would barely see actionable light. A prime study in this regard is a 2019 survey in First Bank where inconsistent customer data led to a disturbing service delay, thereby reducing customer trust and satisfaction.
Data Integration Problems:
Financial operations in some banks are set on a decentralized system, having its data scattered across diverse platforms. The lack of a central information base causes inefficient communication, where it’s difficult for data to be seamlessly shared across departments, especially when they are needed on the spot to resolve pressing issues.
Regulatory Compliance Issues:
For the policies of standard bodies like the CBN’s KYC and AML to be fully adopted and appropriately complied to, data must be accurate and properly managed. In situations where data from banks is haphazardly collated in relation to regulatory compliance, heavy fines are attached, as in the case of the NGN 1.5 billion imposed on several banks because of a poor data quality system.
Strategies for Improving Data Quality: A Business Analyst’s Approach
Data Governance:
The introduction and implementation of data governance is a key way to ensure data quality. This involves establishing internal regulatory frameworks that outline set rules and guidelines for data management to ensure data is accurately managed and applied in ways that are relevant to set objectives. These frameworks should include stewards who are particularly assigned to ensure data quality, helping to reduce risk and promote regulatory compliance. Recent research connotes that companies with good practices of data governance always record a 20% or more in improved data and accuracy.
Data Validation and Cleaning Techniques:
Data validation tools are automated technologies that verify the coherence and accuracy of data at the entry level. This is encouraged as it helps in the prevention of errors and making sure data inputs are collated to desired standards. This goes handy with periodic data cleansing that thoroughly vets data to eliminate errors or duplications at any point. These tools include Ataccama ONE, Trifacta, Openrefine, TIBCO Clarity, and lots more. Fidelity Bank Nigeria made use of data validation and cleansing tools as part of strategies to improve data and recorded a 15% increase in customer satisfaction as a result of improved customer data structure, reducing service delay.
Centralised Data Management Systems:
Many Nigerian banks allow departments to have silo-defined operations, which gives room for data to be spread across different systems, and it often resorts to incomplete and inconsistent data. However, with centralized systems, data can be easily retrieved and measured as and when due. Attesting to the value of centralized systems, in 2020, Zenith Bank revealed that central information systems allowed data redundancies to reduce by 25%, in turn leading to operational efficiency.
Employee training and capacity building:
Business analysts should ensure that employees are technically efficient for the job. This includes organizing capacity-building workshops that involve knowledge sharing in data management processes—data collection, data entry, data storage, data validation, data cleansing, and data integration—in order for high-quality data to be maintained. When there’s sufficient data literacy amongst employees, a study revealed that data errors can be reduced by 30%, and this was quite evident in the 25% reduction in compliance-related errors at Access Bank after their data management training program in 2019.
The Role of Technology in Enhancing Data Quality
Technology has a grave impact on improving data quality; business analysts should recommend technologically-driven solutions to have high-quality data.
Through Data Management Platforms (DMPs), banks can easily collate data from multiple sources. Platforms like this are best for the profiling of customers, helping to fill in comprehensive information about a customer, managing risk and improving efficiency. Sterling Bank adopts data management platforms, and it’s led to a 20% increase in data accuracy under one year of usage.
Integrating technology with data quality is easy with artificial intelligence (AI) and machine learning, where they help to detect data anomalies and ensure that data are cross-checked and evaluated in comparison to competitors for better decision-making. According to IBM (2023), banks using AI for effective data control attest to at least a 35% improvement in data management and consistency.
For maximum data security and accuracy, the blockchain technology is a complete game changer! Its decentralized system helps data to remain tamper-proof, such that fraudulent intrusions or patterns are easily detected. In 2020, Deloitte, through their extensive research, confirms that blockchain possesses the potential to improve the processes of KYC through the provision of a central system for customer data.
Aside from the role of technology in ensuring quality data in banking operations, it’s also important that intentional steps are taken to meet regulatory standards. Business analysts must make sure that all data management practices are in line with KYC and AML regulatory requirements. This can be enhanced by regular data auditing and reporting. This could be done by the IT team to ensure that data across departments are all in compliance with CBN standards, giving room for credibility and quality. According to the International Finance Corporation (IFC), banks with regular data audits record a high rate of reduced errors.
Business analysts must bear in mind that data improvement is continuous, hence must implement Key Performance Indicators (KPIs) to measure quality degrees and track areas of weakness for improvement. Analysts should also look out for regular feedback both internally and externally (real-time data dashboards) to ensure that not only data quality is achieved but there’s sustained effort to keep quality in data management, compliance, and efficiency.
Conclusion
Data is fundamental to an industry as vital and sensitive as the finances. It’s therefore important that business analysts shoot at strategic cues to ensure that data within the financial sector achieves high quality to enable informed decision-making, ensure regulatory compliance, and achieve operational efficiency. Focusing on relevant technologies and continuous improvement issues impeding data quality can be addressed and allow for digital transformation within the banking industry; hence, business analysts must move ahead to have these efforts converted into a good course to achieve long-term success.
Success Ajilore is a highly seasoned accounting professional and business analyst with over eleven years of experience, specialising in enhancing operational efficiency, policy improvement, governance, and process optimization of various companies in Nigeria.
ALSO READ THESE TOP STORIES FROM NIGERIAN TRIBUNE
and then