Data quality is too important to your company’s success to trust the process to a solution that can’t execute on critical tasks. This is especially true in the world of the empowered customer, where having the wrong data can result in misaligned moments of engagement, lost revenue, and customer attrition.
These are powerful consequences of poor data quality, and we believe strongly in making it easy for companies to ensure they don’t fall victim to them. That’s why we’re proud to announce that we received the highest product scores in two of the six use cases featured in the 2016 Gartner Critical Capabilities for Data Quality Tools report – Operational/Transactional Data Quality and Data Integration. RedPoint Data Management™ also received the second highest product scores in Big Data and Analytics, Master Data Management, and Data Migration Use Cases.
Download the full report at: www.redpoint.net/dq-critical-capabilities.
Gartner defines operational/transactional data quality as “capabilities applied to controlling quality of data created by, maintained and housed within transactional applications. As data quality controls are increasingly applied upstream, close to the source of data, the ability to embed data quality capabilities closely with operational applications is key. This use case emphasizes the core data quality operations as well as the need for strong scalability and performance in the face of ever-increasing transaction volumes.”
Data integration, as it is identified in this report, refers to “capabilities applied within data integration processes and architectures (i.e., nondomain specific), in support of both analytics and operational integration.”
Our goal with RedPoint Data Management is to streamline data quality processes, allowing companies to spend more time garnering insights and less time ensuring their data is accurate. We do this through a code-free approach that lets business users access data from any source, including Hadoop, without requiring specialized programming skills or knowledge of Hadoop technologies such as MapReduce or Spark. This approach reduces costs and speeds time to insight, which lets marketing and other users gain the intelligence they need to react at the speed of the customer.
RedPoint Data Management’s data quality routines can execute in an automated production flow with traditional databases or a big data/Hadoop environment. Since RedPoint Data Management is a native Hadoop YARN application, data preparation tasks can be performed without the need to move the data from the Hadoop cluster, rather processing inside the cluster where the data lives. This cuts down on latency between aggregating data and leveraging it in analytics tasks, driving better and faster decisioning and leading to improved results
High data quality should be a priority on every business leader’s mind, especially as the business world becomes more and more data-driven all the time. Our goal with RedPoint Data Management is to streamline data quality processes as much as possible, removing the barriers inherent in the data management skills gap many companies now face. Because, after all, data quality is too vital to the modern enterprise to not do well.
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.
 Gartner, Critical Capabilities for Data Quality Tools, Mei Yang Selvage, Saul Judah, Ankush Jain, 08 December 2016