Riding the Finance Industry’s Big Data Waves

Today’s financial industry is in constant flux. Competitive pressures are challenging financial services companies to provide innovative customer experiences, while also ensuring that they meet compliance requirements and adhere to the latest alphabet soup of regulations coming down the pipeline.

Each new industry Regulation Regulation Like any other industry with a high net worth, the financial services industry is tightly regulated to help curb illicit behavior and manipulation. Each asset class has its own set of protocols put in place to combat their respective forms of abuse.In the foreign exchange space, regulation is assumed by authorities in multiple jurisdictions, though ultimately lacking a binding international order. Who are the Industry’s Leading Regulators?Regulators such as the UK’s Financial Conduct Authority ( Like any other industry with a high net worth, the financial services industry is tightly regulated to help curb illicit behavior and manipulation. Each asset class has its own set of protocols put in place to combat their respective forms of abuse.In the foreign exchange space, regulation is assumed by authorities in multiple jurisdictions, though ultimately lacking a binding international order. Who are the Industry’s Leading Regulators?Regulators such as the UK’s Financial Conduct Authority ( Read this Term makes big waves on the data lake. It’s enough proverbial motion to make anyone, especially IT managers, seasick. As regulations pour in, IT must work to improve big data architectures to meet risk reporting, data aggregation and governance requirements. As regulations ebb and flow, organizations are forced to devote more and more of their annual budgets to supporting IT.

IT managers are tasked with developing big data architectures that can handle constantly changing requirements, offer robust data integration and transformation and provide governance for big data discovery.

Waves on the Horizon

The most difficult aspect of regulatory compliance is that each requirement creates different sized waves. It makes selecting the right data architecture an arduous task. Financial services data, for instance, comes in a number of classifications including client and product reference data, transactional data, security data and more.

Within an organization, it is likely that a different team manages and analyzes each type of data. When a new regulation makes a splash, all of these different teams must work (often in isolation) to ensure that their data storage, processing and Analytics Analytics Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision-making. In the trading space, analytics are applied in a predictive manner in an attempt to forecast the price more accurately. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analy Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision-making. In the trading space, analytics are applied in a predictive manner in an attempt to forecast the price more accurately. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analy Read this Term adhere to the proper compliance requirements. Sometimes that requires the creation of an entirely new set of analytics and reporting. Regardless of the solution, it’s fairly clear that there isn’t a one-size-fits-all method for dealing with the constant churn of the changing compliance landscape.

For example, the Basel Committee on Banking Supervision (BCBS) 239 regulation governs data aggregation and risk reporting. It stipulates that bank risk reports include a veritable mountain of risk data analysis for which financial institutions must be compliant. The list includes a wide array of risk types with varying datasets and requirements. To comply with each and every different scenario is a meticulous, and often costly, process.

With this in mind, organizations need to consider solutions that can consolidate different data types. To do so, they need to select a big data architecture designed for variety, flexibility, and efficiency.

Choosing the Right Boat

A big data solution must be able to gather and analyze both structured and unstructured data from a variety of sources. The need to apply schemes to unstructured data before completing analysis can be more of a headache than a solution. Flexible data integration will help streamline the analysis process, without requiring organizations to know exactly what questions it wants to answer beforehand.

The infrastructure must also provide enterprise-level governance including a permissions hierarchy to maintain data consistency. For example, teams across an organization should have access to the specific sets of data that they need to analyze. In an ideal solution, data analysis should be easily completed by one group and then passed on to other groups who rely on the data as part of a system of downstream analysis, all while maintaining the 'true' data source.

For financial institutions, the data lake is a growing concern. Financial companies need to harness the potential of big data for business growth, while also meeting compliance requirements – this is no small task.

There isn’t a big data panacea floating around atop this data lake. But when striving for quick, actionable and compliant insights, a financial institution should look into a proper balance of architectural flexibility and robust governance to ride out the waves.