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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Year after year, the volume of data being generated is increasing at an unparalleled pace. For businesses, data is critical to inform business strategy, facilitate decision-making, and create opportunities for competitive advantage.

However, leveraging this data is only as good as its quality, and traditional methods for measuring and improving data quality are struggling to scale.

This is where Augmented Data Quality comes in. The term describes an approach that leverages automation to enable systems to learn from data and continually improve processes. Augmented data quality has led to the recent emergence of automated tools for monitoring and improving data quality. In this post, we’ll explain what exactly is augmented data quality, where it can be applied, and its positive impact on data management. 

Why Are Traditional Approaches Struggling? 

First, let’s set the scene. With an ever-growing reliance on data-driven decision-making, businesses are looking for ways to gain accurate insights, deep business intelligence, and maintain data integrity in an increasingly complex business environment.

However, measuring data quality is challenging for enterprises, due to the high volume, variety, and velocity of data. Enterprises grapple with ensuring the reliability of data that has originated from multiple sources in different formats, which can often lead to inconsistencies and duplication within the data.

The complexity of data quality management procedures, which involve data cleansing, integration, validation, and remediation, further increases the challenge. Traditionally, these have been manual tasks carried out by data stewards, and/or using a deterministic-based approach, both of which are not scalable as the volume and veracity of data grows.  Now, enterprises are turning to highly automated solutions to effectively handle vast amounts of data and accelerate their data management journey and overall data management strategy.

What Is Augmented Data Quality? 

Augmented Data Quality is an approach that implements advanced algorithms, machine learning (ML), and artificial intelligence (AI) to automate data quality management. The goal is to correct data, learn from this, and automatically adapt and improve its quality over time, making data assets more valuable. 

Augmented data quality promotes self-service data quality management, empowering business users to execute tasks without requiring deep technical expertise. Moreover, it offers many benefits, from improved data accuracy to increased efficiency, and reduced costs, making it a valuable asset for enterprises dealing with large volumes of data. 

Although AI/ML solutions 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, where human expertise is combined with advanced algorithms to ensure the highest levels of data accuracy and quality.

“Modern data quality solutions offer augmented data quality capabilities to disrupt how we solve data quality issues. This disruption – fueled by metadata, artificial intelligence/machine learning (Al/ML) and knowledge graphs – is progressing and bringing new practices through automation to simplify data quality processes.”

-Gartner^®, ‘The State of Data Quality Solutions: Augment, Automate and Simplify; By Melody Chien, Ankush Jain, 15 March 2022.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

How Can Augmented Data Quality Help A Data Quality Process?

Routine data quality tasks, such as profiling and rule building, can be time-consuming and error-prone. Fortunately, the emergence of augmented data quality has revolutionized the way routine data quality tasks as performed, reducing manual effort and saving time for users. Below are some examples of where automation can add value as part of a data quality process:

Data profiling and monitoring

ML algorithms excel at recognizing patterns. For example, ML can enhance a system’s capability to manage data quality proactively, by identifying and learning patterns in data errors and corrections. Using these learnings, ML can be applied to automate routine tasks like data cleaning, validation, and deduplication.

Data Deduplication

ML can be used to identify and remove duplicate entities. Rather than simply looking for exact matches, ML algorithms such as  natural language processing can identify duplicates even with minor variations, such as spelling mistakes or different formats.

Automated Validation

ML can be used to automate the data validation process. For a feature such as  automated rule suggestion, the system applies ML to understand underlying data and match relevant rules to this data. The process can be further enhanced by automatically deploying suggested rules using a human-in-the-loop approach, making the process faster and more efficient. 

Why Enterprises Are Embracing Augmented Data Quality

Augmented data quality is useful for any organization wanting to streamline its data quality management. Whether it’s for digital transformation or risk management, augmented data quality holds immense value. Here are a few examples of where our clients are seeing the value of augmented data quality:

Regulation and Compliance: Industries like healthcare and financial services are confronted with increasing regulatory changes. Yet, organizations often struggle to meet the demands of these regulations and must adapt quickly. By leveraging AI/ML methods to help identify data errors and ensure compliance with regulatory requirements, enterprises can efficiently minimize the potential risks associated with poor data quality. 
Use Cases: Single Customer View, Sanctions matching.

Business analytics: With complete, and consistent data, organizations can leverage analytics to generate accurate insights and gain a competitive edge in the market. Through AI/ML, data quality processes can be automated to quickly produce analytics and predict future trends within the data.
 Use Cases: Data preparation & Enrichment, Data & Analytics Governance.

Modern Data Strategy: Data quality is a foundational component of any modern data strategy, as data sources and business use cases expand. By leveraging augmented data quality within a modern data strategy, organizations can experience greater automation of manual processes, such as rule building and data profiling. 
Use Cases: Data Quality Monitoring & Remediation, Data Observability

Digital Transformation: Enterprise-wide digital transformation is taking place across all industries to generate more value from data assets. Automation plays a crucial role in enabling scalability, reducing costs, and optimizing efficiencies. 
Use Cases: Data Harmonization, Data Quality Firewall

Adopting augmented data quality within an organization represents a transformative step towards establishing a data-driven culture, where data becomes a trusted asset that drives innovation, growth, and success. The automation of process workflows reduces dependence on manual intervention, saving time and resources while enhancing efficiency and productivity. Moreover, augmented data quality increases accuracy, reliability, and compliance, enhancing customer experiences and improving an organization’s competitive advantage.

In conclusion, the seamless integration of augmented data quality within essential business areas offers significant benefits to organizations seeking to maximize the value of their data.

Find out more about Datactics Augmented Data Quality platform in the latest news from A-Team Data Management Insight.

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