Augmented Data Quality: the what, why, when and how

What is Augmented Data Quality?

Augmented Data Quality (ADQ) leverages advanced technologies such as AI and ML to enhance traditional data management practices. It automates the detection and correction of data issues, enabling data leaders, data stewards, and domain experts to ensure higher data accuracy, consistency, and reliability. This approach is vital in today's fast-paced data environments, where manual oversight is impractical due to the sheer volume and complexity of data. ADQ represents a shift towards more dynamic, responsive data governance models, aligning closely with strategic business objectives by providing a cleaner, more trustworthy data foundation.

When is Augmented Data Quality needed?

ADQ becomes crucial in various scenarios, such as when organizations face rapid data growth, deal with data from multiple, disparate sources, or require real-time data analysis for decision-making. It's also essential when businesses undertake digital transformation projects that necessitate clean, reliable data for new applications. In addition, industries regulated by strict data compliance standards need augmented solutions to ensure accuracy and adherence to regulations. Essentially, any situation where the volume, velocity, or variety of data overwhelms traditional management approaches calls for augmented data quality solutions.

Why Augmented Data Quality Matters

ADQ is pivotal in today's data-centric business environment. It underpins the integrity of data-driven decision-making by ensuring data is accurate, consistent, and trustworthy. This reliability enhances strategic initiatives, operational efficiency, and customer experiences. With AI and ML at its core, ADQ allows businesses to scale their data governance efforts, adapt to changing data landscapes rapidly, and maintain compliance with evolving regulations, securing a competitive edge in a fast-paced digital economy.

How to Implement Augmented Data Quality

The main motive of ADQ is to reduce the manual tasks related to improving data quality, thereby saving time and resources. Implementing ADQ requires organizations to establish a process for data cleaning, creating criteria for determining accurate data, and determining how data accuracy will be validated. The role of the data steward remains pivotal, as they may not need to perform the data management tasks themselves but they will still need to oversee and endorse the outcomes. This is particularly important in maintaining an ADQ endeavour that is consistent with the overall data governance program, and in ensuring compliance with security and privacy requirements.

Augmented Data Quality and Master Data Management

As a standalone discipline, ADQ offers many benefits. However, when combined with the capabilities of modern Master Data Management, the results are amplified many times over. MDM goes beyond ADQ and encompasses data governance, standardization, integration, and stewardship capabilities that are not inherently part of ADQ. While ADQ automates the cleansing and enrichment of data, MDM establishes the policies, processes, and standards needed to maintain high-quality master data over time.

The CluedIn platform is a great example of this, as it combines low code data integration, augmented data modeling, and dynamic hierarchy management – amongst other things – with AI-assisted data quality features such as deduplication, standardization, and enrichment. CluedIn also has the advantage of being based on a Graph database, which means that natural data models can emerge according to the relationships uncovered by the graph, therefore negating the need to model data upfront and providing much-needed context for AI and ML-assisted data stewardship.

Not only this, but CluedIn was designed to allow anyone – regardless of their technical ability – to directly manage and use data. From its inception, it has been a low-code platform, and as a result of its integration with Azure OpenAI, is now even more intuitive for business users.

The future of Augmented Data Quality

ADQ is not a new discipline, but it has gained recent prominence amongst the technology, business, and analyst communities. For organizations that are battling against the rising tide of organizational data, ADQ looks set to become part of their data management arsenal soon – if it hasn’t already. For optimum results, ADQ needs to be considered in the context of your wider data management strategy, rather than as an individual initiative. Many of the goals of ADQ – such as empowering non-technical users and reducing manual intervention - apply across the data management spectrum, and should be treated as your guiding principles when assessing potential solutions.