Making Sense of the 2019 Gartner Magic Quadrant for Data Quality Tools
By now, you know that data is the lifeblood of digital transformation. But the true digital leaders have taken a step beyond by starting to understand the need to preserve this lifeblood with people, process and tools. That’s why data quality is so important in its ability to take control of the health of your data assets from diagnostic to treatment and monitoring with whistleblowers.
With this respect, we are especially proud that Talend was recognized by Gartner as a Leader for the second time in a row in the 2019 edition of Gartner’s Magic Quadrant for Data Quality Tools.
The Definitive Guide to Data Quality now.
Strong Dynamics in the Data Quality Market
This ability for organizations to deliver trusted data at speed across the organization and beyond has become imperative. In Gartner’s hype cycle for data management, "Data Quality Tools" was removed for its 2018 version compared to the previous year because it reached the plateau.
However, Gartner pinpoints that: "This market is still among the fastest-growing in the infrastructure software subsector of the enterprise software market. We forecast compound annual revenue growth of 8.1% in this market for the period 2017 through 2022.”
Today’s Outlook in the Gartner Magic Quadrant
With that respect, the Gartner analysts, Melody Chien and Ankush Jain have brought their deep subject matter expertise and thought leadership to examine the Data Quality Tools vendors.
“Data quality vendors are competing to address these requirements by introducing an array of new technologies, such as machine learning, interactive visualization, and predictive/prescriptive analytics, all of which they are embedding in data quality tools. ”.
But succeeding in data quality is also a matter of adopting data quality principles at speed, in a flexible way, and across the organization. This is where Gartner analysts, Melody Chien and Ankush Jain pinpoint the ways to delivering data quality tools to a wider range of organizations and audience through cloud deployment models saying “They also are offering new pricing models, based on open source and subscriptions.”
Talend’s Named in the 2019 Gartner Magic Quadrant for Data Quality Tools
For the last 10+ years, we’ve been on a mission to help our customers to deliver trusted data at speed. Today, data must be timely, because digital transformation is all about speed and accelerating time to market— whether that’s providing real-time answers to business teams or delivering personalized customer experiences. While speed is critical, it’s not enough. For data to enable effective decision-making and deliver remarkable customer experiences, organizations need data they can trust.
With respect to our data quality vision, this means that from the very beginning, when we introduced our data quality products back in early 2010, we decided not to deliver Data Quality as a stand-alone tool, but rather as a capability that spans across each and every component within our unified platform. We were recognized for the first time in the Magic Quadrant for Data Quality Tools in 2011.
We believe that this pervasive approach to data quality is unique and that it had an important impact on Talend being recognized in the 2019 Gartner Magic Quadrant for Data Quality Tools.
Pervasiveness allows Talend Data Quality to run anywhere and enables users to apply the same data quality sensors, controls, and metrics just in time and consistently across the data chain. And, now, thanks to the cloud, our data quality capabilities can be fully ubiquitous and access any data, anywhere in the data chain and make it trustworthy. This applies to streaming data, real-time or data at rest no matter if it is stored in an enterprise application, a cloud data warehouse, or in a data lake cluster.
Start to Think Data Quality Before You Think Data Quantity
Many data quality projects fail because data quality is an afterthought. Data issues are remediated downstream, but the root cause is not addressed. This is why thinking “quality”, as well as “quantity” is needed.
Let’s take the example of correcting your Salesforce data in your data warehouse because it is where you find the issue. You might have better analytics for a while, but the problem will come back for other use cases or when you modernize your data warehouse into a new environment. Using a pervasive tool allow you to remediate to data quality issue upstream in the data chain. For example, directly into your Salesforce application, or while your capture a stream from the IoT; or when a data engineer turns raw data into something that is more structured and ready to share ; or as an API that can be pushed to a third party so that they can remediate to the data quality at its roots.
Pervasiveness also allows users to democratize data quality and make it relevant for any audience, not just the IT department or CDO. Within the Talend platform, the same data quality capabilities can empower a developer or data engineer using our development framework. It can also be used by a data steward through the Talend Data Stewardship App or by a business analyst with Talend Data Preparation.
“Data and analytics leaders face a challenge to enable business users as the primary audience for new data quality tools and to adopt a more flexible trust-based data governance model", as stated in Gartner’s new Magic Quadrant for Data Quality report. Operationalization and orchestration remain essential to deliver business friendly apps under IT supervision. Talend also strongly believes that data must become a team sport for businesses to win, which is why governed self-service data access tools like Talend Data Preparation and Talend Data Stewardship are such important investments for Talend.
And now, with Talend Data Catalog, this same data quality stack can be applied to a whole data landscape in a systematic and automatic way, making data more meaningful and searchable by anyone through automatic sampling, profiling, categorization, masking, and relationship discovery. Data Quality used to be a discipline, one that only a happy few can really turn into business value. Now, thanks to the advancements in pattern recognition, IA, and machine learning, data quality can get into anyone’s hands. This encourages data literacy now that profiling morphs into data discovery, but also engages a wider audience to collaborate for better data.
As I said at the beginning of the blog, our evolution has been a journey and we invite you to come along with us, try Talend for yourself and become part of this fantastic growing user community.
The Definitive Guide to Data Governance now.
1) Gartner Magic Quadrant for Data Quality Tools, Melody Chien, Ankush Jain, 27 March 2019.
2) Gartner Magic Quadrant for Data Quality Tools, (Authors), Date of Publication, 2011.
3) Gartner Press Release, Magic Quadrant for Data Quality Tools, 20 August 2018. https://www.gartner.com/en/newsroom/press-releases/2018-08-20-gartner-identifies-five-emerging-technology-trends-that-will-blur-the-lines-between-human-and-machine
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. 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.
GARTNER is a federally 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.