Quest Software has built its reputation on protecting digital identities, assisting companies’ data migrations within the Microsoft ecosystem. But the Austin, Texas-based firm also has a data management business that has been addressing both the database and metadata management ecosystems.
As artificial intelligence begins to take a dominant role in data management and among financial institutions’ processes, Quest is gearing its capabilities to leverage the new technology to meet the demands of the new data landscape.
The most recent addition to its data management armoury is the Quest Trusted Data Management Platform, which offers clients an end-to-end capabilities of data modeling, cataloguing, governance and quality management. All of these deliver integrated data products that can be created within the platform’s own Automated Data Product Factory (ADPF).The package is the company’s “biggest launch in some time”, said Bharath Vasudevan, Quest vice president of product and GTM.
“What we’ve done with the data management platform is we’ve taken a lot of the – call it individual capabilities – and really built the bridges between the products and tied it into a unified interface,” Vasudevan told Data Management Insight.
Nine-Factor Score Underpins Trust
At the heart of Quest’s updated SaaS-native platform is trust, Vasudevan said, which is provided via an inbuilt nine-factor trust scoring framework. This gives every data asset a score based on “customisable dimensions”, which include data quality, governance completeness, timeliness, lifecycle, popularity and user ratings. Each of the metrics can be weighted according to the risk tolerances of individual organisations.
“The fact that it’s trusted helps organisations with things they aren’t always going to be able to do – data assets underpinned with modeling, integrated data quality, full lineage…,” he said, adding that it acts as a checklist for the essentials of data management.
“Do I have lineage? Do I have completeness of data with my data products or with my data quality? Do I have all of these pieces in place to build up my own confidence that the trust score is good? Is all the data that I’m putting in a data product, has it been modelled? Can I track it all the way down to the physical model, logical model? So if I have all of these things in place, then I can go back and I can say, look, this is why we believe it’s trusted and transparent.”
‘Killer Application’ at Heart of Trustworthiness Proposition
The key plank of the Quest platform’s trustworthiness – or as Vasudevan puts it, the “killer application” – is its Automated Data Product Factory. Touted as more than just a marketplace or catalogues, the ADPF is integral to the efficiency of Quest’s latest offering.This factory enables users to describe what they need using natural language prompts to create a suitable production-ready, governed data product. Those products, once created, will be stored for reuse or adaptation should any other user – whether in IT or business – requests a similar product.
Clients can self-service and tune data products (which can eventually become dashboards) with new entities and attributes after initial creation, reducing reliance on analysts, Vasudevan said.
Vasudevan said the reusability of the products reduces the amount time spent building the components over and over again and improves the fidelity and governance of data.
“The first thing it’s going to do is it’s going to check the existing data products in the organisation and it’ll come back and say, hey, we hit a 96 per cent match for what you just typed in – do you want me to go recreate the wheel or do you just want to use this?” he said.
“Then I can pull that up and I can start customising that or I can pull it up and use it off the rack. I don’t have to go through this iterative exercise over and over again.”
Mapping Metadata Back to Actual Data
The system is streamlined by Quest’s platform building the products using only the metadata and then mapping that to the actual data. This also helps firms navigate data sovereignty issues when creating data products.
While the factory’s own data products eliminate the need to bolt on third-party point solutions, the platform is technology agnostic and can work through the data warehouse likes of Snowflake and Databricks and for governance can connect into tools such as Horizon, Unity Catalog and Microsoft Purview.
The time saved by replacing manual processes with Quest’s automated platform can be measured in months, he added. And it can done to scale.
“By leveraging AI, natural language and automation, I can now solve significantly larger challenges for organisations that the individual products by themselves couldn’t,” he said.
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