Introduction

Late last year I attended a Data Management Review webinar discussing the impending BCBS 239 deadline and the implications for banks in 2016.

One of the speakers observed that BCBS 239 is largely designed to bring banks in line with sound data management principles already practised in other industries such as the pharmaceutical industry,

Drug companies must ensure that every scheduled drug that leaves their factories is tracked and traceable, in order to manage the risk of addiction or death. One enabler is provable data governance and data quality – not necessarily as an outcome in its own right, but as an enabler to ensure that drug-related data is maintained correctly.

BCBS239 requires banks to implement and track data governance and data quality processes because paying attention to data is proven to reduce costs and risks.

This article delves into the topic of data management within the banking sector and explores whether banks are staying ahead or lagging behind other industries in this crucial domain.

The BCBS 239 Deadline and Implications for Banks

Among the key players harnessing the power of data are banks, serving as custodians of vast amounts of financial information. The efficient management of data is not just a matter of operational excellence; it has evolved into a critical aspect of regulatory compliance and risk management for financial institutions worldwide.

The Basel Committee on Banking Supervision’s BCBS 239 regulation, issued in the aftermath of the global financial crisis, sets out principles for effective risk data aggregation and reporting (RDARR). Its primary goal is to enhance the risk management capabilities of banks and improve their overall stability. The regulation aims to bring banks in line with sound data management principles practised in other industries.

Data Management in the Pharmaceutical Industry

The pharmaceutical industry has long recognized the importance of data management. Drug companies face stringent regulations that mandate the tracking and traceability of scheduled drugs from production to consumption. This approach is crucial to managing the risks associated with addiction and potential fatalities.

Provable data governance and data quality are instrumental in this context. Maintaining accurate and reliable drug-related data is of paramount importance. Any errors or lapses in data management could lead to severe consequences, compromising patient safety and legal compliance.

The Enabler: Provable Data Governance

In the context of both the banking and pharmaceutical industries, provable data governance plays a pivotal role. For banks, this means implementing robust processes to govern data and ensure its accuracy, completeness, and reliability. Effective data governance allows financial institutions to make informed decisions, identify and mitigate risks, and maintain compliance with regulatory requirements.

Similarly, in the pharmaceutical industry, data governance guarantees that vital drug-related information remains accurate and accessible at all times. This, in turn, helps pharmaceutical companies maintain the highest quality standards, minimize errors, and ensure the well-being of patients.

Data Quality as a Game-Changer

Data quality is the foundation upon which data management strategies are built. Inaccurate, incomplete, or inconsistent data can have far-reaching consequences, leading to poor decision-making and increased operational risks.

Both banks and pharmaceutical companies recognize the significance of data quality. In the financial sector, erroneous data can lead to financial losses, compliance breaches, and reputational damage. Similarly, in the pharmaceutical industry, compromised data quality can endanger patient lives and result in costly lawsuits.

BCBS 239’s Call for Enhanced Data Governance and Quality

The BCBS 239 regulation explicitly calls for the implementation and monitoring of data governance and data quality processes in banks. Recognizing the pivotal role data plays in the stability of financial institutions, the regulation emphasizes the need for accurate and timely data that facilitates effective risk management.

Are Banks Ahead or Behind?

Now, the question arises – are banks ahead or behind other industries when it comes to data management? The answer is not straightforward, as it depends on several factors. In comparison to heavily regulated industries like pharmaceuticals, banks have made significant strides in recent years to improve their data management practices.

However, the landscape of data management is constantly evolving, and the pace of change varies across industries. While banks may have made considerable progress, they cannot afford to be complacent. Continuous innovation and improvement are imperative to keep up with the ever-increasing demands of data-driven decision-making.

Challenges Faced by Banks in Data Management

Despite the progress made, banks still encounter several challenges in the realm of data management. Some of the most prominent ones include:

1. Data Silos: Large financial institutions often operate in a fragmented manner, leading to data silos across different departments. This fragmentation hampers data accessibility and integration.
2. Legacy Systems: Many banks still rely on legacy systems that may not be compatible with modern data management technologies. Integrating and streamlining data across these systems can be a daunting task.
3. Data Governance Maturity: Achieving a high level of data governance maturity requires significant effort and commitment. Many banks are still in the early stages of developing robust data governance frameworks.
4. Data Privacy and Security: Banks must grapple with the challenge of protecting sensitive customer information while ensuring data accessibility for authorized personnel.
5. Regulatory Compliance: Financial institutions must comply with a myriad of regulatory requirements, each imposing specific data management standards.

Closing the Gap: The Way Forward

To bridge the gap and move ahead in data management, banks must focus on the following key areas:

1. Investment in Technology: Embracing modern data management technologies, such as advanced analytics, AI-driven solutions, and cloud-based platforms, is crucial to gain a competitive edge.
2. Data Governance Frameworks: Developing and implementing comprehensive data governance frameworks is essential to ensure data accuracy, consistency, and compliance.
3. Collaboration and Integration: Breaking down data silos and fostering cross-functional collaboration will enhance data integration and accessibility.
4. Data Security Measures: Strengthening data privacy and security measures will protect sensitive information from unauthorized access and cyber threats.
5. Talent Development: Nurturing a skilled workforce that can handle complex data management tasks is vital for sustained growth and success.

Conclusion

In conclusion, data management is a critical aspect of a bank’s operations and regulatory compliance. While banks have made significant progress in aligning themselves with data management practices, they face unique challenges in this ever-evolving landscape. By investing in technology, developing robust governance frameworks, and nurturing talent, banks can close the gap and stay at the forefront of data management.