Author: Laurie Schnidman
The data quality market is evolving. Increasingly, automation, metadata management, and governance are combining with traditional data quality tools to evolve the space and bring about a new generation of solutions. The main goal is to automate data quality efforts using AI and machine learning (ML) to make data more accessible and available to all.
At Experian, we are seeing this shift as well. Quality data is critical to many organisations. In fact, recent Experian research shows that 87% of respondents believe that high quality data plays a critical role in responding to market pressures. These market pressures include technology advancements, increased costs, skill shortages, supply chain issues, and shifting consumer expectations.
Even with all this importance, most organisations still suffer from a high degree of inaccurate information. While there has been investment in some data quality features, these features are often not used in combination with other data management capabilities or they are built for only a few data practitioners within an enterprise, making it difficult for wide adoption. In addition, there is still a misconception that data quality projects can be executed on a one-off basis. Data is constantly changing and evolving, therefore it needs to be continuously monitored and cleaned for ongoing improvement.
The shift and demand for new features from data quality tools aims to fix this. Automation brings a lot of promise within many fields, but automation done well within data quality means that organisations can better understand the challenges with their data and fix those challenges at a much faster rate. In addition, data can be continuously monitored and staff notified when data falls outside of certain guidelines.
At Experian, we are updating our solutions to better take advantage of these changes within the market. While we have always been strong with our data profiling, in the past year we have invested heavily in features like data observability, automated rule creation, automated data transformation and more. These are all designed to help users better identify issues with their data, point them to problems they didn’t know they have and provide suggestions for how to fix those issues in an automated fashion.
In addition, we are investing in metadata and data governance to better connect data policies with data quality and improve data visualisation capabilities so organisations can better tell the story around why data matters to the business and the impact of poor data quality.
Recently, Gartner® released the 2024 Magic Quadrant™ for Augmented Data Quality Solutions. According to Gartner, “Augmented data quality, driven by AI, metadata and knowledge graph, is dominating the data quality market. Traditional data quality vendors face fierce competition due to outdated technologies. Data and analytics leaders should understand market trends and vendor landscapes to make the best choices.”
Experian is excited to announce that we have been placed as a Challenger. We continue to invest in the growth of our products and, in turn, the growth of our customers.
At Experian, we believe trusted information facilitates better and faster responses to changing consumer needs, and that’s why we empower our clients to keep data quality at the core of their business. If you haven’t taken a look at our solutions lately, we encourage you to see what we have been up to. Feel free to contact us to set-up a demonstration.
Gartner, Magic Quadrant for Augmented Data Quality Solutions, 6 March 2024. Gartner and Magic Quadrant are registered trademarks of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.