Historically, there have been significant barriers for firms to gain meaningful insights and value from cloud data content because of disparate datasets that are unstructured in nature, according to Jeremy Katzeff, head of buy-side solutions, GoldenSource.
He said that managing disparate data sets in a data lake eliminates data silos that hold firms back and create issues with data governance and access.
“Data lakes help to democratize data access and governance within an organization,” he told Traders Magazine.
According to Katzeff, managing data sets in a data lake enables firms to fuse the data together in a common data model, and provide tailored views and access to various personas while preserving a centralized governance structure.
Currently, market participants must manage many disparate datasets within data lakes, making it difficult for users to digest and subsequently apply analytics.
As a result, one significant challenge faced by cloud data warehouse users in financial services is the absence of a comprehensive data model.
Katzeff said that Cloud Data Warehouses are open platforms—they do not come with any governance structures out of the box. Firms need to think about everything, from how queries drive cost, to how personas access the data, and also what data types and how the data is ingested into the cloud data warehouse, he explained.
“They also need to understand cybersecurity, data privacy and protection regulations that exist globally,” he added.
On Tuesday, June 27, GoldenSource launched the GoldenSource Snowflake Native App to accelerate data onboarding and analytics in Snowflake.
From now, the GoldenSource schema – which provides firms with a way to identify, structure, describe and link data that is of interest to an organization, independent of how the organization uses and processes that data – can be moved into the Snowflake cloud data platform.
Previously, the process of cleaning and transforming raw data had to be handled externally by cloud computing platforms.
The GoldenSource Snowflake Native App will help accelerate adoption of Snowflake use across the financial services industry, according to Katzeff.
Commenting on the wider industry trends within cloud data pipelines and storage, Katzeff said there has been wider adoption of these platforms, and the platforms are expanding or launching their data sharing and data marketplace frameworks. There is also a trend of integrating AI/ML into the use cases and the technology stack.
“Leveraging AI/ML requires clean data to have well trained models. This requirement will lead to more data quality and mastering tools that are built into the pipelines and storage workflows,” Katzeff said.
Finally, he mentioned the demand for ESG data from clients across financial services and broader industries, which is rapidly growing.
Katzeff thinks there will continue to be a need for faster data onboarding, and there are additional tools and processes needed to ensure the highest level of data quality when onboarding data into the cloud data warehouses.
“In addition, governance, access, and data privacy are all issues that market participants must consider when onboarding new data sets into their warehouse,” he said.
