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Big Data Technology for Regulatory Reporting Is Getting Lots of Publicity: It Might Have Real Star Quality

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By Vlad Etkin, CTO, AxiomSL.

It has only been a few years since the concept of big data was first heard in association with regulatory reporting, and while there are still questions about what it really is and its applicability to regulatory reporting, it’s obviously here to stay. However, as with all new concepts, it is critical for organizations to assess the technologies that promise they can process large volumes of data at speed. To do so, organizations implementing big data strategies must understand and adopt new technologies and platforms such as Hadoop, Spark, and the myriad of cloud-vendor provided big data solutions, including Redshift.

Fundamentally, big data technologies enable high volume data ingestion from multiple sources, subsequently processing it and making it available for analytics and regulatory reporting submissions. The aim is to increase performance speed of data processing and glean information that will inform business decision making and strategy. But as organizations assess how best to utilize and implement big data technology, they may have concerns about its security and viability. The bottom line is that the big data market is thriving, so how can organizations ensure transparency, easy access to data and scalable processes when adopting this technology?

Big Data Technology Stars Are Ready For The Big Screen

To mitigate potential pitfalls, firms should examine best practices for the following:

  • Accommodating increased data and avoiding silos– end-to-end transparency across disparate sources of data enables strong business decision making and confident regulatory compliance.
  • Streamlining the processing of usable data – leveraging technology to avoid repeat data processing in the case of data lakes and data warehouses maximizes resources and enables accurate slicing and dicing of data for business insights.
  • Automation – organizations should orchestrate scalable big data workflows, especially given increased reporting requirements in an ever-changing risk and regulatory landscape.

Like an actor graduating from acting class to a role on the big screen, big data technology and its capacities have been put to the test in recent years, and the technology is now considered mainstream. If organizations avoid the potential pitfalls of sacrificing transparency and accuracy, harnessing the power of big data can enable organizations to improve processing times, deliver business insights and enable confident risk and regulatory reporting.

Compatible Casting: Large Volume And Incremental Execution

There are two approaches to the execution of big data: processing large volumes and incremental execution. While each one is based on the same foundation of processing data, incremental execution offers a flexible process for re-executing certain data if desired. In effect, it is a complementary approach to large volume processing. For example, to do a stress test or other granular analysis, small batches of data from the larger ingestion can be re-processed in small batches with relevant calculations applied.

Rather like an iconic onscreen couple who bring out the best in one another with compatible acting techniques, an appropriate approach to big data means that the most granular data is being processed by a technology-driven system to increase efficiencies and glean meaningful business insights, while not compromising accuracy. This leads to an interesting question for organizations to consider:

Will implementing big data not only optimize resources and create scalability, but could it improve data granularity?

An Oscar-Worthy Performance

To counterbalance the challenges of an ever-evolving risk and regulatory environment, particularly given COVID-19, organizations are seeking new technologies that can help them manage their risk and regulatory data and better accommodate the effects of the crisis. And high-quality performance is key. Big data technology enables the processing of huge amounts of data, and therefore when executed efficiently, can deliver scalability and save financial institutions valuable time and resources.

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