About a-team Marketing Services
The knowledge platform for the financial technology industry

A-Team Insight Blogs

Datactics Enhances Augmented Data Quality Solution with Magic Wand and Rule Wizard

Subscribe to our newsletter

Datactics has enhanced the Augmented Data Quality Solution (ADQ) it brought to market in November 2023 with the addition of an AI magic wand, Snowflake connectivity and an SQL rule wizard in ADQ v1.4. The company is also working towards the release of ADQ v1.5 that will include generative AI (GenAI) rules and predictive remediation suggestions based on machine learning (ML).

ADQ was born out of market interest in a data quality solution designed for not only dedicated data quality experts and data stewards, but also non-tech users who could write their own data quality roles. A user-friendly experience, automation and a reduction in manual processes were also top of mind.

Kieran Seaward, head of sales and business development at Datactics, explains: “Customers said their challenges with data quality were the time it took to stand up solutions and enable users to manage data quality across various use cases. There were also motivational challenges around tasks associated with data ownership and data quality. We took all this on board and built ADQ.”

ADQ v1.4

ADQ made a strong start with v1.4 also a response to customer interests, this time in automation, reduced manual intervention, improved data profiling and exception management, increased connectivity, predictive data quality analytics, and more.

Accelerating automation, ADQ v1.4 offers enhanced out-of-the box data quality rules that ease the burden for non-tech users. The AI magic wand includes reworked AI and ML features and an icon showing where users can benefit from Datactics ML in ADQ. Data quality process automation also accelerates the assignment of issues to nominated data users.

Increased connectivity features the ability to configure a Snowflake connection straight through the ADQ user interface, eliminating the need to set this up in the backend. The company is working on additional integrations as it moves towards v1.5.

Predictive data quality analytics monitor data quality and alert data stewards of breaks and other issues. Stewards can then view the problems and ADQ v1.4 will suggest solutions. Based on a breakage table of historical data from data quality rules, ADQ v1.4 can also predict why data quality will fail in the future. Seaward comments: “Data quality is usually reactive but now we can put preventative processes in place. Predictive data quality is very safe to use as the ML does not change the data, instead providing helpful suggestions based on pattern recognition.”

The SQL rule wizard allows data quality authors to build SQL rules in ADQ, performing data quality checks in-situ to optimise processing time.

ADQ v1.5

Moving on to ADQ v1.5 and the integration of GenAI, users will be able to query the model, write a rule for business logic specific to their domain and test the rule to see if it produces desired results. Datactics is currently using OpenAI ChatGPT to look at the potential of GenAI, but acknowledges that financial institutuiosn are likely to have their own take on LLMs and will point its solution to these internal models.

Other developments include a data readiness solution including preconfigured rules that can check data quality and allow any remedial action before regulatory data submissions are made for regulations including EMIR Refit, MiFID III and MiFIR II, and the US Data Transparency Act and SEC rule 10c-1.

Criticality rules that will help data stewards prioritise data problems and solutions are also being prototyped, along with improved dashboards and permissioning, and as it started, next stage development will continue to make ADQ more friendly for business users.

Subscribe to our newsletter

Related content

WEBINAR

Upcoming Webinar: Building a Semantic Layer for Your Enterprise Data Estate

Date: 8 September 2026 Time: 10:00am ET / 3:00pm London / 4:00pm CET Duration: 50 minutes The democratisation of data has encouraged engineers to think about how to make their data estates more accessible and useable for non-technical business end-users. Translating intention into data action requires careful configuration that enables consumers to mine insight, analytics...

BLOG

MCPs in Data Management: Bringing New Order to Private Markets

Financial institutions have begun deploying Model Context Protocols (MCPs) as they have expanded the use of artificial intelligence applications and agents. The technology developed by Anthropic is an open-source contextual layer that helps coordinate models and data, enabling AI applications to connect with a multitude of other platforms and processes. In the first of a...

EVENT

AI in Data Management Summit New York City

Following the success of the 15th Data Management Summit NYC, A-Team Group are excited to announce our new event: AI in Data Management Summit NYC!

GUIDE

AI in Capital Markets Handbook 2026

AI adoption in capital markets has moved into a more disciplined phase. The priority is now controlled deployment: where AI can be used safely, where it can deliver measurable value, and how outputs can be governed, monitored and evidenced. The 2026 edition of the AI in Capital Markets Handbook examines how AI is being applied...