Introduction

It is clear that banks and other financial institutions need to implement an enterprise-wide data driven culture if they are to succeed in meeting the regulatory demands of BCBS239. With the directive demanding that banks strengthen their risk data aggregation capabilities and improve their data quality, the need for robust data governance structures that can deliver high quality accurate data has never been more urgent.

Before the financial crisis there were minimal levels of formal data governance. What was in place existed within business silos and without any over-arching unification or management. Data management and governance are at the heart of BCBS239 compliance, but firms need to overcome the lack of standardisation in the governance and structure of their data. We commonly find that each source of risk or finance data frequently has its own format, standards and interfaces, and that each firm is likely to have its own terminology and language for describing risk and finance information. These challenges are compounded by legacy technologies and processes which hinder the changes needed to meet the demands of the regulators.

Banks can improve data governance by promoting and elevating the value of data inside their institutions. It is rare to find a culture of producing and maintaining high quality data inside many financial organisations. For many, the real value of data is not always appreciated or understood, let alone incentivised, and as a result, users generally work without good data discipline.

The process of implementing strong data discipline and raising the profile of data should begin by establishing a ‘data organisation’ approach. This approach needs to be empowered by senior managers, who should view data as the fourth organisational pillar, alongside business, operations and technology.

The data organisation inside an enterprise, functions best when it is recognised and supported at the highest level and differentiated from the technology organisation in the firm who traditionally own the data function. In best practice organisations, the Chief Data Officer (CDO) reports to the CEO, CFO or COO.

Structures should be introduced that overcome traditional boundaries and enable a consistent data operating model to become established. We have seen a federated structure successfully adopted by many clients, whereby functionally aligned CDOs are appointed within each business line that report directly to the head of that business unit, but also have a ‘dotted line’ to the group CDO function that is responsible for setting policy, standards and managing cross silo data processes and initiatives. The business aligned CDO is responsible for ensuring that group CDO functions are properly supported, managing business-specific data initiatives and teams.

Finally, once a group data function has been established, one of the first things that should be agreed on is a comprehensive data operating model, along with comprehensive data standards. The most important objective of an enterprise data operating model is to elevate the value of data within the enterprise. Data communication and incentives to promote positive cultural change within the organisation should be encouraged, whilst unstructured data manipulation should be discouraged.

Most data operating models set the roles and responsibilities of the key stakeholders within the wider data organisation, with titles such as ‘business data owner’ and ‘data producer’; it is here that the data organisation can really influence cultural change within the firm. This can be achieve by ensuring that the right people are placed in these roles, and by adequately training them and giving them the correct tools to achieve the best outcome for the organisation.

Conclusion

Non-stop regulatory change is now ‘The New Normal’. This increase in regulatory scrutiny calls for an enterprise-wide data-driven culture and approach. Notwithstanding regulatory pressures, the fundamental driver for implementing this culture should be to improve decision making within the firm, driven by the information extracted from business data, as well as meeting regulatory compliance.

Data governance is an enabler for revenue protection and generation. Without high quality data (accurate, timely and complete data delivered through an agile infrastructure), firms will continue to run the risk of making poor decisions that ultimately lead to a loss in revenues and competitiveness.