Syncari Debuts its Autonomous Data Management Platform to Transform Master Data

Syncari, a leader in data unification and automation, is launching its Autonomous Data Management platform, offering distributed 360-degree data, centralized governance, and democratized access that is essential for powering transformative digital and AI technologies.

According to the company, the platform enables enterprises to automate processes seamlessly across all data domains, using a unified master data model, ensuring robust data consistency and transactional lineage.

Designed to achieve success in today’s digital age and for business agility, the Syncari platform tackles data integrity challenges and optimizes data readiness for enterprise AI applications, delivering substantial business value.

This latest release introduces innovative dataset authoring and activation features, along with an advanced cross-domain control plane. Unlike traditional MDM, these enhancements enable organizations to leverage active master data instantly across multiple domains and ecosystems, boosting operational efficiency and driving strategic business outcomes, according to Syncari.

"With our Autonomous Data Management platform, we're solving more than data challenges; we’re automating the entire data lifecycle,” said Neelesh Shastry, CTO and co-founder at Syncari. "We’re creating and distributing a 360° view across disparate systems and turning data into actionable insights.

Key Capabilities of Syncari Autonomous Data Management include:

  • Continuous unification
  • Continuous data quality
  • Continuous distribution
  • Programmable MDM
  • Comprehensive 360-degree dataset readiness

"Powered by our patented multi-directional synchronization engine, Syncari flips the script on how enterprises activate their valuable data,” said Nick Bonfiglio, CEO and co-founder at Syncari. “For the first time, business and IT teams can work more closely on AI modernization projects and drive increased go-to-market efficiency while significantly reducing the risk of failed data projects.”

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