Redgate, the end-to-end Database DevOps solution provider, announced today it is introducing two new machine learning (ML) and artificial intelligence (AI) powered capabilities in its test data management and database monitoring solutions. Data professionals today are faced with increasingly complex daily workloads, with a pressing need to improve efficiency and streamline workflows. With these new offerings, Redgate continues to provide ingeniously simple solutions, while maintaining data protection across the database management process.
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The use of AI is growing – in McKinsey’s 2024 State of AI survey, 65% of respondents stated their organizations are regularly using AI in at least one business function – and Redgate research shows that database professionals are benefitting from improved efficiency, automation and standardization. However, people are concerned about the risks of AI with the McKinsey report revealing concerns around inaccuracies (63%), intellectual property (52%), and cybersecurity (51%).
As David Gummer, Redgate CPO, comments: “AI has the potential to bring real value to every business, but when we introduce AI and ML into database management, we must also counter any risks it introduces. At Redgate, we’ve taken an approach to introduce AI innovation in a way that delivers value without lowering standards, particularly around how data is used and shared. With the introduction of the AI capabilities in Redgate Monitor and Redgate Test Data Manager, we’re removing the bottlenecks and errors that come with manual processes, freeing up time for teams to create new value, and keeping data even more secure.”
Synthetic Data Generation
When wanting to use test data, many development teams have no access to production data due to the risk of exposing sensitive customer information and compliance concerns. Often teams need to generate data which is as similar to production data as possible, whether recreations of existing projects, tricky corner cases or even greenfield projects where data doesn’t yet exist in any form. To address this, an AI synthetic data generation capability is being added to Redgate Test Data Manager.
In Redgate’s offering, the data a user inputs and the data it generates is only ever used by their local version of the capability and stays in their own data environments, addressing customer concerns about data being used to train AI/ML models, or any proprietary data leaving their environments.
Using ML algorithms to understand patterns, relationships and distribution characteristics within data, Redgate Test Data Manager will generate new data that mirrors these properties, so that users can create intricate datasets that closely mimic real-world data patterns. This provides developers and testers the accurate, representative data they need without any data being copied from or leaving production, satisfying data privacy and maintaining data integrity.
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Further automating Database Monitoring
Similarly, a new ML capability will be introduced to Redgate’s long established and popular monitoring solution, Redgate Monitor, which already offers real-time performance monitoring for SQL Server and PostgreSQL.
The customizable alerts and diagnostics for databases will be enhanced further by using ML to identify which operational and performance alerts are normal background noise, and which are critical and need to be prioritized. With every organization placing different demands on its database estate, this is a standout capability that tailors Redgate Monitor to each customer’s particular requirements, reducing the time teams spend manually configuring and maintaining alerts.
With database estates becoming too large and complicated for a one-size-fits-all monitoring approach, Redgate Monitor will also use the ML capability to raise dynamic alerts based on patterns in metric data. By matching alerts to the real usage seen on monitored databases, it will improve uptime and make alerts far more relevant, avoiding alert fatigue.
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