Learn about the big changes in the Gartner Magic Quadrant for Data Quality Solutions 2024

The 2024 Gartner Magic Quadrant for Augmented Data Quality Solutions is out, and it's full of surprises.

The name, the market definition, critical capabilities, and vendor placement have all changed.

Join us as we break down the main changes for you.

Market definition

This year's edition is much shorter.

Instead of focusing on detecting and fixing issues, Gartner chose to define the market in terms of how data quality solutions improve insights, make suggestions, and automate data quality use cases. All three rely on using AI and metadata. This is why they've included "augmented" in this year's title.

Here is how Gartner defined the market in 2022:

"Data quality (DQ) solutions are the set of processes and technologies for identifying, understanding, preventing, escalating and correcting issues in data that supports effective decision making and governance across all business processes."

Compared to the previous definition, the emphasis in 2024 has shifted:

"Gartner defines augmented data quality (ADQ) solutions as a set of capabilities for enhanced data quality experience aimed at improving insight discovery, next-best-action suggestions, and process automation by leveraging AI/machine learning (ML) features, graph analysis, and metadata analytics."

It's true that Gartner analysts talked about augmented data quality as the key driver for the market in the past. However, in the 2022 edition of the Quadrant, they referred to it as an "emerging capability." No more. And that's why this year's edition has "Augmented" in its title.

Augmentation is no longer a bonus or a superlative but an essential element of any data-quality product.

Critical capabilities

Without going into too much detail, here are some of the more noteworthy changes in capability requirements:

• Focus on augmentation and automation: The 2024 description emphasizes the integration of AI/ML into DQ solutions to automate profiling, matching, rule discovery, data transformation, and other critical processes. There's an increased focus on reducing manual effort and leveraging LLMs to enhance data quality functions.
• The bundling of data profiling and monitoring: Data profiling and data monitoring used to be presented as separate capabilities. In this edition, they are described together to emphasize the importance of using data profiling results to automate DQ monitoring via "automatic semantic discovery. In other words, the data catalog.
• Extreme business-user centricity: Gartner continues to emphasize the importance of supporting non-technical business users. In this report, the analysts make a particular point about providing suggestions to users, learning from user feedback, and minimizing user involvement in configuration.

It's all about AI and automation.

If you want to go through the capabilities in-depth, you can download the full report here.

Vendor movements

This Magic Quadrant marks not only a change in the name but also a big shakeout in the positioning of vendors.

Five vendors were dropped, three were added, and almost everyone who stayed in the Magic Quadrant was moved to the left. Out of the 6 Leaders on the 2022 Qudrant, only 3 remain in 2024. This means vendors have a lot of work to do to satisfy Gartner's new bar for data quality solutions in terms of completeness of vision.

Here is the 2022 Quadrant for comparison:

New ambitious players and maturing subcategories like data observability provide the market with fresh solutions, so we can expect healthy competition in the coming years.

Ataccama in the 2024 Magic Quadrant

Ataccama is still a Leader in this year's Quadrant.

We have been moved slightly to the left and significantly up on the Ability to Execute. Over the past two years, we have invested in customer success functions, go-to-market strategy, and building scalable operations across multiple functions. Our customer base grew by 10% and revenue by 24% year over year.

Our investment in AI technologies, initiated in 2016, is critical to our success. It allowed us to incorporate the latest GenAI has to offer in our product as early as 2023. We believe this focus on emerging technologies and our clear future roadmap have strongly contributed to our high rating on the Completeness of Vision axis.

The future of data quality

The data quality market has grown by 8.3% in 2022, and we can expect that growth to accelerate.

Leading companies across first-mover industries (like financial services) have been data-driven for a long time. These organizations are now modernizing and expanding the use of data quality tools in their organizations while laggards are catching up.

Besides that, based on conversations with our customers, the following are the main business challenges that drive the demand for data quality solutions. Organizations worldwide are trying to:

• Grow their market share
• Expand their customer footprint
• Reduce costs and improve efficiency
• Mitigate regulatory and security risks
• Become AI-ready and establish trust in data

All of these require high-quality, readily accessible data, while the reality is often very different as companies struggle with data chaos, inefficient use, reactive work, and non-compliant data.

What does all of this mean for data quality solutions?

It's clear from this year's Magic Quadrant that data quality tools are all about ease of use and automation. Connectivity and scalability go without saying.

Where Ataccama is going with augmented data quality

Ataccama has always been eager to integrate cutting-edge technology, and we will continue to do so—review our AI-powered capabilities. We are excited to keep working on making data quality automated and more accessible for end users.

AI will continue to play a huge role in automating and accelerating data quality workflows. As ease of use and democratization of data analytics become more important, we believe end-user interfaces for working with data will be built predominantly around large language models (LLMs). In this paradigm, users will "talk" with their data ecosystem about their requirements and SLAs and delegate the heavy lifting of data analysis and preparation to (Gen) AI.

For some time now, we have been working on integrating LLMs even more in Ataccama ONE, and the next big feature we will unveil is the AI assistant.

Imagine a co-worker who handles on-demand data-related tasks, responds to queries, provides detailed progress updates, and learns from feedback. Without AI, business users would have to log in a request and wait until someone from the data team addresses it. This is a common issue that will become even more prevalent.

With a unified data management platform like Ataccama ONE, which stores vast amounts of metadata and possesses all critical data management capabilities, it only makes sense to have an LLM that would use all that information and skillfully apply the features of the data platform.

If you've enjoyed this breakdown and want to dive deeper, download the latest edition of the Magic Quadrant here.