Claravine, a leader in Data Integrity, today announces The Data Standards Cloud™ allowing data owners and data engineers to finally be able to deliver on enterprise data standards. No longer do teams need to rely solely on their data pipelines to be responsible for making sense of data. Now, organizations can leverage Claravine to manage their data standards and create data integrity globally, providing consistent and quality information to optimize business outcomes.
According to Forbes, data engineers continue to spend nearly 80% of their time doing activities that are manual, extensive and least enjoyable; cleaning, preparing and organizing data — causing critical delays in time to insight and results. Enterprises must enable data collaboration, but a lack of data standards makes this impossible to do across teams, geographies and partners. The pandemic has only increased the demand for data collaboration. In fact, according to a Winterberry Group report titled: “Collaborative Data Solutions: Data and Identity in the Era of Permission,” 81% of brand marketers in the US are either currently sharing data or intend to share data. Despite the growing demand for data collaboration, there has not been an end-to-end solution in the market to fix data quality before it becomes a problem.
After launching a partnership with Adobe and an integration into Adobe Experience Manager to control content data standards, Claravine now partners with the industry-leading mobile measurement and deep linking platform, Branch, to bring data standardization to mobile and enable the enterprise to create, deliver and optimize mobile experiences at scale.
“Content and mobile have been major focuses for customer experiences, and Branch’s leadership in mobile experiences makes them the ideal partner to help define these standards,” said Chris Comstock, chief product officer of Claravine.
The Claravine and Branch integration helps mobile marketers and data teams align on the data requirements for cross-screen experiences. Marketers can now quickly create the right links and ensure all data is defined and connected so customer experiences work the first time with data automation occurring from day one.
“Our customers need to optimize for cross-channel, but the complexity of launching multi-screen experiences slows down marketers. Claravine solves this by helping marketers and data teams achieve automation and optimize their business decisions,” added Eric Stein, EVP & GM of Branch.
Too many teams rely on spreadsheets and ETL in an attempt to achieve data quality, which means there is no global data standard. This means data teams are left wondering if they are receiving the data or spending a majority of their time trying to make sense of it.
“So much of the investment going into data systems and data teams is reactive. At Claravine, we believe in a proactive approach that supports a true enterprise data strategy. We support our customers’ efforts to create data integrity by helping them own their data standards through our technology and collaboration. More ETL isn’t the answer. ETL is a time-consuming and soul-crushing process that occupies far too much of data engineers’ and data scientists’ time, and it keeps them from focusing on data analysis and optimizing business outcomes. The only way to eliminate ETL is by empowering data owners to standardize, connect, and control their data standards in a platform like Claravine,” said Verl Allen, chief executive officer of Claravine.
Data-driven market leaders are reinventing their data strategies at a time when a single data lake is no longer possible with the changes Google, Amazon, and Facebook are requiring marketers to access performance data as well as the ongoing challenge of customer and product use data being generated throughout the enterprise, often in legacy systems. The marketing tactics that once worked in mobile are changing with the Apple IDFA changes. As these transformations occur, Claravine’s Data Standards Cloud provides a way for data strategies to move into the cloud for a dynamic view of data.
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