“Every organization — no matter how big or how small — needs data quality,” says Gartner in its newly published Magic Quadrant for Data Quality Solutions. However, with more and more data coming from more and more sources, it’s increasingly harder for data professionals to transform the growing data chaos into trusted and valuable data assets. Data pipelines may carry incomplete and inaccurate data, making data practitioners’ jobs difficult and preventing data-driven initiatives from delivering on their expected business outcomes.
We all aspire to transform our organizations with data-driven insights, but we can’t do that if we don’t trust our data. A recent Opinion Matters survey shows that only 31% of data specialists have a high level of confidence in their organizations’ ability to deliver trusted data at speed.
Working without reliable data becomes costly, risky, and chaotic. Whether you’re unifying product and customer data in a single 360° view to transform the customer experience, or you need to comply with data privacy regulations, data quality can make the difference between the success and failure of your data-driven initiatives.
No data management initiative is complete without a solid data quality strategy
Data quality can dramatically impact your bottom line. Gartner stated that its “Magic Quadrant customer survey shows that organizations estimate the average cost of poor data quality at $12.9 million every year.” Another Gartner report also positions data governance and data quality as the most important initiatives for data management strategies.
As data quality is becoming a linchpin of data management, we’re proud that Talend was recognized by Gartner as a Leader for the third time in a row in the 2020 edition of Gartner’s Magic Quadrant for Data Quality Solutions.
We believe data quality shouldn’t be managed by a standalone solution. Rather, data quality is a core discipline within data management. It should span out everywhere, and this requires integration and extensibility.
Talend Data Fabric delivers data quality as a pervasive capability that spans across our platform and related applications, and that includes self-service data preparation, data integration, real-time integration, metadata management, and a data catalog.
We believe, being recognized as a Leader in the Magic Quadrant for Data Quality Solutions not only validates our capacity to build a vision for data quality, but also validates our ability to help organizations succeed in their digital transformation journeys.
4 innovations that make the biggest impact on data quality
The research also considers the technologies and innovations in the data quality market. Let’s review those key ingredients and see how Talend addresses them.
Ubiquity: horizontal, not vertical data quality
Talend has made data quality a key component of its data management vision for a decade; we have been positioned in this Gartner Magic Quadrant since 2011. Talend has always considered data quality the key to making any data management project a success.
We embed data quality into every step of the data pipeline by making Talend Data Quality an integrated part of Talend Data Fabric instead of a standalone application, so that customers can get data they trust at every stage of the data lifecycle.
Simplicity: democratizing data quality with simple, efficient, collaborative data systems
Data practitioners need simple, intelligent, automated data quality tools to transform data chaos into valuable, reusable data assets.
Talend was among the first contenders to cover that need. Talend introduced self-service data preparation tools in 2016, bridging the gap between IT capabilities and business needs. The following year, Talend entered the Magic Quadrant for Data Quality Solutions as a Leader. Today, Talend Data Fabric provides a unified, collaborative platform in the cloud on which nontechnical users can profile, contribute, and improve data, removing the hassle of legacy on-premises systems.
Automation: data quality made intelligent
Amplifying data quality with machine learning has become a key differentiator. “By 2022,” Gartner predicts, “60% of organizations will leverage machine-learning-enabled data quality technology for suggestions to reduce manual tasks for data quality improvement.” Business users need help to accelerate preparation for better data.
To that end, Talend recently introduced more machine learning-driven features, such as Magic Fill to accelerate data preparation and let users process data quicker and better.
Collaboration: bring the people expertise back into the data
Still, while automation is important, it’s not the answer to everything. Data quality success often stems from the right alliance of people, technology, and processes aligning with each other to make an impact. People must remain in control, and human expertise must be captured and employed in the data chain. To capture that knowledge, another component of Talend Data Fabric, Talend Data Stewardship, helps organizations assign data validation to appointed experts across the organization and track and audit progress.
Talend’s stewardship capabilities were highlighted by our customers in the previous Magic Quadrant, and continue to provide value to customers. That’s why we made Talend Data Stewardship a key part of our Talend Data Fabric, letting organizations not only offer that functionality, but also engage users in a virtuous circle with their data.
Companies rely on data quality to deliver successful data strategies
We’re witnessing these innovations and new needs firsthand and are proud to support our customers on their journey to data quality.
Take the example of Seacoast Bank, which created a data quality index for all their financial services. Seacoast Bank relies on data to be able to provide customers the best solutions for their needs, and to develop a deeper understanding of who their customers are and how they want to work with the bank. And being heavily regulated, Seacoast Bank also understands the need for trusted data. Seacoast Bank is banking on a data quality index to measure data quality across six dimensions and track how it improves or degrades as the bank acquires other banks, and as data sources, processes, and the technical environment change.
“It’s our duty to make sure each customer’s data accurately reflects who they are in our community, and what their relationship is with our community-based bank.”
Talend is working with a renowned telco operator that serves more than 90 million mobile subscribers. Our customer was facing huge data quality challenges that led to underperforming customer communications. They used Talend Data Quality to convert bad data into a steady stream of clean and reliable source data to power advanced analytics. This happens automatically every day, allowing data analysts, the operations team, and even business users to know if the data they are using is accurate and valid. Results were impressive: The company went from a 40% to a 90%+ trust score that saw better efficiency, cost reduction, risk protection, and higher ROI of marketing campaigns.
Everyone should know what’s inside their data, score it, and improve it over time
Gartner predicts that “by 2022, 70% of organizations will rigorously track data quality levels via metrics, increasing data quality by 60% to significantly reduce operational risks and costs.”
Talend brought data profiling into the hands of data engineers. Now that everyone wants to use data, it’s equally important to let data workers understand the data, endorse it, score it, and improve it.
|Talend Trust Score does just that. The Trust Score helps anyone to answer at a glance the question “How trustworthy is my dataset?” It’s based not only on data quality indicators, but also on popularity and certification, so that reliable and authoritative datasets can be shared and populated across the organization.|
We’re still in the early stages of the data quality journey. Data management practices are constantly evolving, and we’re seeing capabilities converging into a unified platform that can meet the needs of both business departments and IT.
We’re happy to help. We thank all the customers who have placed their trust in Talend. And to anyone who wants to bring clarity to their data chaos, we invite you to discover Talend, try our data quality stack, and become part of our growing user community.
Gartner, Magic Quadrant for Data Quality Solutions, Melody Chien, Ankush Jain, 27 July 2020
Gartner, Survey Analysis: Data Management Struggles to Balance Innovation and Control, Melody Chien, Nick Heudecker, 19 March 2020
Gartner, Build a Data Quality Operating Model to Drive Data Quality Assurance, Melody Chien, Saul Judah, Ankush Jain, 29 January 2020
Gartner, Magic Quadrant for Data Quality Solutions, “Melody Chien, Ankush Jain”, “27 July 2020”
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