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Governance Minimizing Data Complexity in the Financial Industry

By CIOReview | Friday, June 8, 2018

As numerous financial giants have been around for years now, they have built up a complex array of platforms, systems, and data sources that interconnect across many levels. This intricacy makes it difficult to understand what data they have, where is that data and whether the data is trusted.

Using data effectively is quite formidable as it is hugely dispersed and circuitous, thereby, if a change is not understood well enough, it becomes difficult to make changes. Adding to this complication is the impact of regulation. Financial institutions have to comply with some of the requirements of the numerous regulations, for example, where it resides and what is its purpose. Some of the regulations include the Financial Intelligence Centre Act (FICA), Know Your Customer (KYC).

Since there is no one-size-fits-all approach, some underlying data governance principles need to be employed by bank management teams that will help manage, protect, and deliver data throughout the organizations.

Data governance is essential for businesses to sort and understand data, where it resides, create strategies and carry it out successfully. The biggest challenge that data governance poses is the lack of understanding of what it comprises, therefore, many organizations tend to confuse governance with tactical tools and tasks it recommends to manage data.

As automation is becoming prominent in banks, it essential to understand their business processes that the systems automate and the data required for support. This particularly happens in financial institutions where there are open source programs such as Hadoop. Governance brings in policies and rules that are essential to control the chaos, ensuring focus, and usage of the right data to resolve issues.

With the aim of deriving better value from data, banks have started significant investments in data science. However, without frameworks (data governance) or policies in place, the efforts put in by data scientists could be derailed due to its complexity. To deliver value, data has to be trusted, for which businesses need a mechanism to recognize quality data.

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