How does governance minimise data complexity in the financial industry?

By Gary Allemann
Data complexity Most financial giants have been around for decades. Over the years, they have built up a complex array of systems, platforms and variou...

Data complexity

Most financial giants have been around for decades. Over the years, they have built up a complex array of systems, platforms and various other data sources which interconnect across multiple levels. These labyrinthine environments make it extremely difficult to understand what data they have, where it resides, and whether the data can be trusted.

With data being so dispersed and circuitous, using it effectively can be an onerous undertaking. Data is used for many reasons, from gaining better insights into customers and market trends, to improving customer experience, creating new or improved products/services and, of course, complying with regulations.

Making changes also becomes challenging if the impact of any change is not clearly evident or understood. A single change on one system could cause a chain reaction across other systems, affecting functionality and operations.

Adding to this complexity is the impact of regulation. Knowing where data resides, and its relevance and purpose to the organisation, are some of the requirements of the many regulations that financial institutions are obligated to comply with. These include the Financial Intelligence Centre Act (FICA), Know Your Customer (KYC), Basel Committee on Banking Supervision's standard number 239 (BCBS 239), Protection of Personal Information (PoPI) Act, and General Data Protection Regulation (GDPR) - the latter being requirements even for South African banks who are likely to count any number of European citizens among their clients.

See also:

Understanding governance

Although there is no single, one-size-fits-all approach, bank management teams can employ some underlying data governance principles and practices to more successfully collect, manage, protect, and deliver data throughout their organisations.

Data governance is vital for a business to sort through and understand what data it has, where it resides, create a strategy around it and carry it out successfully. One of the biggest challenges with data governance, though, is a lack of understanding around what it comprises. Many organisations confuse governance with the tactical tools and tasks it prescribes to manage data.

With automation becoming so prevalent in banks, it’s important to understand the business processes that their systems are trying to automate, and what data is required to support this. This is especially true of financial institutions where open source programs such as Hadoop introduce a new level of chaos into the fray. Governance introduces the rules and policies needed to create order from that chaos, giving the ability to focus, step by step, on using the right data to solve problems.

Proper data governance has more to do with managing people and their behavior than with managing data. Governance is about assigning accountability for the proper management and use of data. If the people within an organisation understand what data is for; who the right people are to access, manage and use it; and how to use it - then they begin to understand what data is actually useable, quality data.

Making it work

Banks are making significant investments in data science, with the objective of deriving better value from their data. However, without policies, processes and frameworks (data governance) in place, the complexity of their environments could derail the efforts of data scientists. To deliver value, data needs to be trusted. And to be trusted, businesses need a mechanism to identify quality data. Systems can only go so far. The rest relies on creating accountability and responsibility with the people; on creating a governance strategy which defines who is accountable, for what, and for what reason.

Governance is not a tactical solution, and it needs to be taken seriously if financial institutions want to build their enterprise view, understanding data at a global level. There is no value in adopting a top down approach which defines structures if the focus is so tactical that the solution provided is limited to single areas of business. It needs to be broad, addressing priority areas in alignment with overall business strategy. Then it can be broken down into bite size chunks, according to priority.

A framework is essential to ensure the success of master data management. Data governance provides that framework. It eliminated inefficiencies caused by too many people focusing on solving the same problem in different ways - and often a problem that can be more easily resolved if more critical areas are addressed first, in order of priority. Governance enables the business to prioritise, making data management more efficient, effective and, therefore, more cost effective, too.

Gary Allemann, Managing Director, Master Data Management


Featured Articles

Zoom Leads with Post-Quantum Encryption Amid Quantum Threat

Zoom's pioneering move to implement post-quantum encryption highlights the urgency for businesses to fortify defences against the coming quantum threat

NTT Brings AI and Data Innovation to the Indianapolis 500

NTT is harnessing cutting-edge technologies like AI and data analytics to revolutionise how the Indianapolis 500 race is competed and experienced by fans

Salesforce & IBM Partnership to Drive AI, Data Deployment

IBM and Salesforce's expansion of their partnership shows how watsonx’s is making inroads in enterprises across sectors

FC Barcelona & Fortinet: Cybersecurity Takes Centre Stage

Cloud & Cybersecurity

Google Cloud Generative AI Ops Drives Enterprise AI Adoption

AI & Machine Learning

How Publicis Sapient Helps Your Digital Transformation

Digital Transformation