07 Sep Big Data Technology For Regulatory Reporting Is Getting Lots of Publicity: It Might Have Real Star Quality
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 technology must understand and adopt new processes and platforms such as Hadoop, Spark, and the myriad of cloud-vendor provided big data solutions, including Redshift.
Fundamentally, big data technology enables 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.
And The Winner Is…Faster, More Granular, And Auditable
AxiomSL’s ControllerView® data integrity and control platform provides organizations with a strategic and scalable, end-to-end capability to transparently and efficiently address big data needs. AxiomSL’s use of innovation and bespoke-architected technology makes the most of big data technologies for transparent regulatory reporting. It enables faster and more accurate processing of both granular data and aggregated data as well as offering the flexibility to slice and dice.
- The execution engine gateway manages execution environments and multiple synchronized gateways guarantee high data availability and optimal performance speed.
- The execution engine uses Apache Spark, additional engines may be defined per cluster and execution configuration.
- Redshift is being integrated with the Spark engine on the ControllerView® strategic and RegCloud® deployable platform for even better performance. The result is a massive improvement in performance of information extraction (e.g., a batch that took more than five hours in Oracle, took less than one hour in Redshift).
Thanks to an innovative, transparent, and technology-driven approach that is constantly adapting to new technologies, AxiomSL can provide accuracy, traceability, and transparency for large amounts of data with Oscar-worthy performance metrics. The platform runs report generation directly on the data residing in the big data environment with no need to move data into Relational Data Base Management System (RDBMS) storage. And it can generate reports in-memory, skipping intermediate results when not required.
Performance By Volume
Impact on performance for a project heavily based on statistical calculations
Clients reap the benefits: based on use cases overseen by AxiomSL subject matter experts, clients can get the best performance and scalability out of their investment. Furthermore, the flexible and robust platform transparently delivers efficient processing of large volumes of data, threshold checks, validations and reporting. In addition, dynamic data-lineage capabilities, intuitive user-controlled workflow automation, and end-to-end user-friendly visualization support organizations as they seek a trustworthy and reliable solution to address big data challenges in a quickly changing world.
Fifteen Minutes Of Fame Or Lifetime Achievement Award for Big Data Technology? Talk to us about risk and regulatory reporting, leveraging big data star power.
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