Our quick ADQ in three minutes guide can be downloaded from the button below the graphic. Happy reading! As always, don’t hesitate to get in touch if you’re looking for an answer that you can’t find here. Simply hit ‘Contact us’ with your query and let us do the rest.

Wherever you are on your data journey, we have the expertise, the tooling and the guidance to help accelerate your data quality initiatives. From connecting to data sources, through rule building, measuring and into improving the quality of data your business relies on, let ADQ be your trusted partner.

If you would like to read some customer stories of how we’ve already achieved this, head on over to our Resources page where you’ll find a wide range of customer case studies, white papers, blogs and testimonials.

To get hold of this infographic, simply hit Download this! below.

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In the digital era, data is essential for every organisation, meaning good data management is needed to empower businesses to make well-informed decisions and operate efficiently. However, this can be a challenging landscape, encompassing catalogs, lineage, observability, master data management, and data quality. 

We’re at a point now where institutions’ data estates are rapidly expanding. Stretching from legacy systems to cloud migrations and data warehouses, and spanning relational databases to unstructured documents, the importance of data quality has never been greater. This, coupled with the decentralisation of organisational data, has made it difficult for organisations to maintain good data quality. 

From traditional to transformative Data Quality Solutions 

Addressing data quality issues within a business has typically involved very labour-heavy, manual processes. The nature of the modern data landscape, with its complex and ever-growing data sets, is demanding much more in the way of transformative solutions. Consequently, data quality systems must now adapt to automate processes like data profiling, rule suggestion, and time-series analysis of data issues. This is where the revolutionary concept of ‘augmented data quality’ comes into play. 

Augmented Data Quality- What is it? 

In short, augmented data quality is an approach that uses machine learning (ML) and artificial intelligence (AI) to automate and enhance data quality management. The aim is to automatically improve data quality by analyzing data, identifying and fixing issues, and providing clear, transparent metrics on data quality and improvement actions across your entire data estate. As a result, our users have found that an augmented data quality approach makes their data assets more valuable, allowing them to maximise the value of their data at a low cost with minimal manual effort. 

 Augmented data quality promotes self-service data quality management, making it easier for business users to carry out tasks without the need for deep technical expertise and knowledge of data science techniques. Moreover, it offers many benefits, from improved data accuracy to increased efficiency, and reduced costs. Rather than needing to carry out many specific tasks when assessing the quality of a set of data, augmented data quality automates this process, making it a valuable resource for enterprises dealing with big data. 

 Whilst AI and machine learning models can speed up routine DQ tasks, they cannot fully automate the whole process. In other words, augmented data quality does not eliminate the need for human oversight, decision-making, and intervention; instead, it complements it by leveraging human-in-the-loop technology, using advanced algorithms to perform large amounts of checks and fixes while making use of human expertise to review and tackle only the most difficult of issues, ensuring the highest levels of accuracy. 

Datactics Augmented Data Quality Platform 

Responding to these challenges, Datactics has developed the Augmented Data Quality platform (ADQ), which streamlines the data quality journey through a user-friendly interface. Our technology team has pioneered the use of AI/ML capabilities to make it easier for businesses to improve data quality. This includes: 

• Automated Data Profiling: Enabling you to efficiently onboard new sources of data or analyse existing ones, this feature allows the user to quickly understand their data, identify trends and outliers, and, when errors are found, automatically suggest and apply data quality rules. 
• DQ Insights Hub: Making use of a wide range of our machine learning capabilities, this feature provides a summarised view of data quality across many sources, allowing you to create interactive and fully customizable dashboards. These dashboards highlight and track many DQ metrics, from the number of issues found with each data element to the average time it takes for these issues to be remediated and then re-occur again.
• Predictive Features:  We’ve developed a bespoke machine learning algorithm that learns from your data quality issues, allowing you to gain a deeper understanding of the root causes of the problems and empowering you to take preventative measures to ensure they don’t reoccur. By training this exclusively on your data, you get the most accurate predictions whilst also ensuring your data is fully secure. 

Benefits of the Datactics ADQ platform

These represent tangible benefits for our users. At the heart of ADQ’s success is the new user layer that simplifies all the key components of a good data quality solution, such as connectivity, integrations, rule authoring, remediation, and insights. Essentially providing a pragmatic and practical real-world understanding of data quality

The Datactics platform is designed with all levels of users in mind. ADQ’s interface is intuitive and user-friendly, ensuring that users, regardless of their technical proficiency, can easily navigate and utilise the platform to its full potential. With support for a spectrum of different technologies, ADQ is the perfect platform for any user, from a non-technical business user to expert data scientists. This approach democratises data quality management, making it accessible and manageable for a wider range of professionals within an organisation. 

The practical benefits of ADQ are evident in our client testimonials, with users reporting significant reductions in cost and time associated with building data quality projects. Specifically, the rule suggestion feature has been a game-changer for many, identifying a substantial portion of business rules which results in considerable time savings. Essentially, it provides a pragmatic and practical real-world understanding of data quality. 

Empowering Organisations with Data 

In the future, we plan to enhance ADQ with more automated features, better insights, and additional integrations. Some of the new features upcoming this year include incorporating generative AI into the platform, allowing non-technical users to create data quality checks using natural language prompts. Suggestions for remediations, generated using historical fixes and our bespoke machine learning algorithm, will vastly boost the number of issues that can be automatically resolved, decreasing the likelihood of human error and leaving your data stewards free to tackle the most critical and problematic cases. Additionally, by enhancing our predictive capabilities, we will allow you to pre-emptively act before data quality issues occur, ensuring your organisation is always working with high quality data. 

 The release of ADQ marks a significant milestone at Datactics, in terms of innovation and supporting our customers. It embodies our commitment to providing state-of-the-art data management solutions, enabling organisations to fully leverage their data assets. We are proud of our team’s vision and dedication to delivering a platform that not only addresses current data quality challenges but also paves the way for future innovations. 

For more information about the Datactics ADQ solution, take a look at this piece by A-Team Insight or reach out to us at www.datactics.com. 

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