– Introducing-
ADQ 1.7
The latest ADQ, built for peak performance and control.
ADQ v1.7 turns up the dial on speed, scalability and access control, so your data quality programme can handle real-world loads and real-world governance.
From distributed rule execution and faster rule execution and profiling, to a brand‑new Access Control area for managing users and data permissions, ADQ v1.7 is packed with improvements that make life easier for data stewards, rule authors and platform teams.
Pick a button below to get ADQ or plan your upgrade, or read on for the highlights.
What's New in ADQ
Here are the big changes you'll notice when you move to ADQ 1.7:
Built for Peak-Load Performance
A new distributed architecture option for ADQ means rule executions, workflow tests and data extractions can be spread across multiple worker nodes, while DQC handles parallel exception imports. The result: faster runs in both development and production, even at month‑end.
Centralised Access & Governance
A simplified, much improved and centralised Access Control feature inside ADQ lets you manage users, groups, roles and data access from one place, with granular permissions and nested user groups for complex organisations.
More Flexible Rule Management
Rules can now belong to more than one execution pipeline, giving you more options for scheduling checks (for example, daily monitoring plus heavier end‑of‑month runs) without duplicating rules.
Smoother Day-to-Day Operation
Small, high‑impact improvements – from automatic log housekeeping to better navigation and accessibility – reduce friction for both end users and platform teams.
Performance and Scale: 'Month-End Safe'
Handle your busiest DQ runs with ease
ADQ v1.7 introduces a powerful distributed architecture option designed for organisations with large data volumes and tight reporting windows. Under the hood, ADQ 1.7 introduces a controller/worker deployment option for our orchestrator and workload executor, Data Quality Manager (DQM), allowing you to scale out rule execution and data extraction horizontally across multiple worker nodes, and parallelise exception loading into our remediation engine, Data Quality Clinic (DQC). This gives platform teams a clear path to scaling ADQ for enterprise‑grade workloads.
Distributed rule processing with DQM
The Data Quality Manager can now run as a controller with multiple worker nodes. Rule executions, workflow tests and input data extractions can be processed concurrently, reducing bottlenecks and improving stability under load.Parallel exception loading in DQC
The Data Quality Clinic now supports parallel dataset imports. Even when you’re working with millions of exceptions, stewards can get to the data they need quickly.Faster profiling extractions
The data extraction step in profiling has been refactored and optimised, so you can understand new or changing datasets faster and decide where to focus your rules.
Outcome: ADQ v1.7 is more than ready for month‑end, quarter‑end and any other peak‑load data quality activity you throw at it.
Access Control and Governance
All your access controls in one place
Governance teams have been asking for a simpler way to manage ‘who can see what’. ADQ v1.7 delivers a dedicated Access Control area in the UI.
Centralise user and role management
Configure users, user groups and roles without hopping between different tools. Everything lives in one place inside ADQ.Granular, real‑world permissions
Set fine‑grained permissions that match real job roles – for example, allow someone to view data source connections without editing them, or grant the ability to edit views while restricting underlying connections.Nested user groups with clear visualisation
Model complex organisational structures with nested groups. A tree view makes it easy to see who belongs where, and what access they have.
This makes it easier to align ADQ with internal access policies and to demonstrate robust control to auditors and regulators.
Rule Management and Library Improvements
Rules that fit your schedule, not the other way round
As data quality programmes grow, so does the need for flexible scheduling and clear rule catalogues. ADQ v1.7 adds improvements that help here:
Rules in multiple execution groups
A single DQ rule can now be part of more than one execution group. That means you can reuse the same logic across different schedules (for example, intraday vs. overnight) without cloning or maintaining duplicates.Refined reusable rule library
Metadata in the reusable rules library has been reviewed and improved. The default view now shows Rule Categories, making it easier to find and apply the right kind of check across your datasets.
Together with the rule building features introduced in earlier versions, this helps teams roll out and maintain DQ coverage more efficiently.
Day-to-Day UX and Operational Improvements
Smaller changes that make a big difference
ADQ v1.7 includes a number of quality‑of‑life improvements for both end users and administrators.
Better UX for rule authors
When you cancel out of the rule view/edit/create wizard, ADQ now remembers where you were in the rule library rather than taking you back to the top – a small change that saves clicks and makes for a smoother experience.Accessibility improvements
An inactivity warning appears before automatic logout, giving users a chance to stay signed in if they’re still working.Automatic log file housekeeping
A daily housekeeping process clears out ADQ server log files based on configurable settings, reducing manual maintenance and helping keep environments clean.
These enhancements don’t change what ADQ does – they just make it nicer to live with every day.
Progress never sleeps. Here's what you can expect in the near future from ADQ:
Coming Next in ADQ
Container-Ready Deployments across the Platform
We’re continuing to extend containerisation across the Datactics stack – including ADQ, DQM, FlowDesigner and related services – so it’s easier to deploy on modern platforms such as Kubernetes or managed container services. That means simpler architecture, faster provisioning of environments, and smoother upgrades for your platform teams.
AI + Automation to Shift Work Left
Building on ADQ’s existing machine‑learning and rule‑suggestion features, we’re investing in more AI‑assisted experiences so non‑technical and semi‑technical users can define and refine data quality checks themselves. The goal is to move away from central bottleneck teams and enable more meaningful self‑service across the business.
Even Faster Execution + Responsiveness at scale
Following the distributed architecture and profiling improvements in v1.7, the next releases continue to focus on performance: better utilisation of worker nodes, snappier UI interactions, and more efficient handling of large rule sets and exception volumes. This will help keep ADQ “month‑end safe” even as your data grows.
richer remediation workflows in ADQ Data Issues
We’re enhancing the Data Issues experience in ADQ so more remediation can be managed directly there, rather than relying on older tooling. This includes better performance for large exception sets, clearer ownership and status tracking, and improved support for collaborative remediation across teams.
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Technical Details and Documentation
To see under the hood of ADQ v1.7 - configuration, deployment, capacity - we've documented everything in our Public Knowledge Base.
Your Datactics contact can also walk you through recommended deployment options for your environment. Drop us a direct line or use the contact form below.
Feedback, Ideas, Questions?
We’d love to hear your ideas on how to make ADQ even better.
If you’re an existing customer, you can simply contact your account manager or submit a request through the Portal.
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