5 Best Practices for Data Observability in the Banking Sector

In today's banking landscape, data is not just an asset; it's the backbone of decision-making and compliance. However, ensuring the accuracy and reliability of this data is a significant challenge. 

Data observability provides comprehensive and integrated visibility over data and its environments to alert data teams of issues related to data reliability, infrastructure, pipelines, operations, and costs, enabling them to take prompt action and prevent future problems. In banking, this means detecting and resolving issues before they impact operations or regulatory reporting.

Banks face numerous challenges, including regulatory compliance, operational efficiency, financial loss, reputational damage, regulatory penalties, legal consequences, inefficiencies in operations, and erroneous decision-making. Failure to deliver can result in dire consequences. For instance, in a 2016 incident, a Wells Fargo fake accounts scandal led to severe fallout, including $185M in fines, a leadership purge, a deeply tarnished reputation, and loss of customer confidence.

The Wells Fargo scandal is a striking example of the serious repercussions of inadequate data observability. It highlights how effective data monitoring and oversight might have helped the bank avoid these consequences altogether.

This article explores best practices for enhancing data observability in the banking sector.

Implement Comprehensive Monitoring Tools

Implementing comprehensive observability tools is essential to ensuring robust data observability in the banking sector. These tools help continuously monitor data pipelines and systems, providing real-time insights into data health and performance. Continuous monitoring aids in the early detection and resolution of issues, ensuring data reliability and regulatory compliance.

Monitoring tools enable banks to track data flow across various systems, identify anomalies, and swiftly take corrective and preventive actions. Corrective actions may include isolating and fixing faulty data pipelines or rectifying data discrepancies. Preventive actions might involve implementing stricter validation checks, automating data quality assessments, and improving data governance policies. By leveraging these tools, banks can maintain data integrity and avoid costly errors or compliance breaches. Real-time monitoring also helps assess the effectiveness of data quality initiatives and make necessary adjustments.

Case study: PhonePe’s success with centralized data monitoring

The fastest route to implementing effective continuous data oversight is through a data observability platform like Acceldata, which offers comprehensive monitoring capabilities.

For example, PhonePe, a leading P2P payments company, leveraged Acceldata to scale its open-source data platform by 2000%, achieving remarkable results. PhonePe managed to oversee over 1500 Hadoop nodes and more than 350 million active users, processing $400 million in cash transactions per month. 

With Acceldata, PhonePe could rapidly identify and resolve issues in their HBase clusters, distinguishing between hardware failures and poorly designed tables. This proactive approach reduced data warehouse costs by 65%, maintained 99.97% availability across its Hadoop infrastructure, and saved approximately $5 million, showcasing the substantial benefits of Acceldata's platform.

Utilize Machine Learning for Anomaly Detection

Machine learning (ML) / Artificial Intelligence (AI) is critical in enhancing data observability within the banking sector, particularly in anomaly detection. By leveraging ML algorithms, banks can automate identifying irregularities and potential fraud in real time, which is crucial for maintaining data integrity and operational efficiency.

Anomaly detection with ML involves analyzing vast amounts of transaction data to identify patterns and deviations that may indicate fraudulent activities or data quality issues. This proactive approach helps mitigate risks and ensure compliance with regulatory standards.

ML models can learn from historical data to predict future anomalies, allowing banks to address potential problems preemptively. This capability enhances the accuracy of anomaly detection and reduces reliance on manual monitoring, which can be time-consuming and error-prone.

For example, JP Morgan uses ML to enhance data observability and anomaly detection. By integrating ML algorithms into its fraud detection systems, the company has significantly improved the accuracy of detecting fraudulent transactions. This proactive approach has not only reduced fraud-related losses but has also enhanced customer trust and satisfaction.

Establish Data Quality Metrics

Data quality metrics are standardized measures used to evaluate the integrity of data. In banking, these metrics can include accuracy, completeness, consistency, timeliness, uniqueness, and validity. By monitoring these metrics, banks can identify and rectify data issues promptly.

High-quality data is crucial for banks to maintain regulatory compliance, ensure customer satisfaction, and make informed business decisions. Poor data quality can lead to significant financial losses, regulatory fines, and reputational damage. 

For instance, Citigroup recently faced fines totaling $136 million from the Office of the Comptroller of the Currency (OCC) and the Federal Reserve Board due to repeated failures in risk management and data governance.

Implementing data quality metrics involves several steps:

• Define metrics. Identify key data quality metrics relevant to banking operations, such as accuracy (e.g., correct customer details), completeness (e.g., all required fields are filled), and timeliness (e.g., data is up-to-date).
• Set benchmarks. Establish acceptable thresholds for each metric, such as a 95% accuracy rate for customer data.
• Monitor continuously. Use data observability tools to monitor these metrics in real time. This helps promptly identify deviations from the set benchmarks.
• Implement corrective actions. Develop procedures for addressing data quality issues. 

Enhance Data Lineage Tracking

Data lineage tracking involves documenting the data’s journey through different processing, storage, and analysis stages. It includes information about where the data comes from, how it is transformed, and where it is used. In banking, this includes tracking data from customer transactions, financial reports, compliance records, and other critical data sources.

Accurate data lineage is essential for banks to ensure data integrity, comply with regulatory standards, and enable effective data governance. It helps identify data discrepancies, understand data dependencies, and facilitate audits. For example, the European Central Bank's BCBS 239 regulation mandates banks to have robust data lineage processes to enhance risk-data aggregation and reporting capabilities.

Implementing data lineage tracking involves several steps:

1. Documenting data flow
2. Implementing automated tools
3. Establishing governance policies
4. Conducting regular audits

Foster a Data-Driven Culture

A data-driven organizational culture is one where decisions are made based on data analysis and interpretation rather than on intuition or personal experience. Such cultures regard data as a critical asset and make its accurate collection, analysis, and use integral to the organization’s operations.

In the banking sector, fostering a data-driven culture is essential for several reasons:

• Enhanced decision-making: Data-driven decision-making helps banks identify trends, anticipate market changes, and optimize operations.
• Improved risk management: Accurate data analysis helps banks more effectively assess risks, leading to better risk-mitigation strategies.
• Regulatory compliance: A data-driven approach ensures that banks maintain transparency and accuracy in reporting, thereby meeting regulatory requirements.
• Customer insights: Leveraging data helps banks understand customer behaviors and preferences, enabling them to offer personalized services.
• Reduced risk of fines and legal fees: By ensuring compliance through accurate and transparent data management, banks can minimize the risk of incurring fines and legal fees associated with non-compliance.

These best practices help address current challenges and prepare banks for future advancements in data management and technology. Adopting these strategies enables banks to maintain a competitive edge, build customer trust, and achieve long-term success.

The Acceldata Advantage

Acceldata's data observability platform is a category leader in the Everest Group Data Observability Technology Provider PEAK Matrix® Assessment 2024. The platform leverages cutting-edge AI to prioritize alerts, improve data integrity, and provide actionable insights. Acceldata is built to scale, supporting vast numbers of transactions without compromising performance.

Schedule a demo of the Acceldata platform and explore how real-time observability can uplevel banking operations in your organization.

Summary

In today's banking sector, data observability is crucial for maintaining data quality, ensuring regulatory compliance, and addressing many challenges, including financial loss, reputational damage, regulatory penalties, and operational inefficiencies. Implementing a best-in-class data observability platform like Acceldata helps banks avoid costly errors, ensure regulatory compliance, and support effective decision-making and operational efficiency.

Acceldata provides robust monitoring that helps banks swiftly identify and address issues such as data anomalies, infrastructure problems, and operational bottlenecks. Its advanced ML-based analytics enhance anomaly detection and improve the security posture. Acceldata enables banks to maintain customer trust, reduce churn, and optimize their financial systems by reducing the risk of prolonged downtimes, erroneous decision-making, and customer dissatisfaction. This comprehensive approach safeguards against immediate risks and supports long-term resilience and growth in the banking sector.