Businesses spend more time trying to manage their data rather than using it for business decisions. DataOS® is an empathy-driven, modular architecture data management platform that replaces the multiple point solutions commonly used to manage data. DataOS is a true, purpose-built data fabric.
Turn data into information and information into insight.
Bring data from anywhere and turn it into high-quality, auto-profiled, managed assets.
Democratize data access with ABAC-based policies to control connectivity at the most granular level.
Curate and maintain universal domain and tribal knowledge bases, then layer it up with semantic information. Don’t just work with rows-and-columns; understand the meaning of every data element.
DataOS enables enterprises to ingest, process, transform, govern, and orchestrate data from disparate data sources to deliver a trusted and real-time view of customer and business data. Our DataOS data fabric solution embodies seven core principles:
Our approach to data allows customers to use value-driving data products like Snowflake, Google Tensor Flow, Azure ML, C3.ai, etc. in a plug-and-play fashion without the need for extensive integrations.
This approach of centralized data management, data quality control, and governance ensures that customers control the data in their organization and makes front-end products replaceable without big vendor lock-ins.
In order for businesses to succeed in the new post-Covid normal, having the ability to understand changes happening to your business in real-time and being able to respond to those changes and innovate becomes essential.
In this new age where many teams work remotely, having the ability to collaborate on data workloads in the same fashion that teams commonly collaborate using tools like Slack, Asana, Jira, and Google Docs becomes essential.
The business agility that the Modern approach brings to data management creates an environment that makes it easy for our customers to launch multiple vendor PoCs and share data at the same time.
Our system not only identifies changes in data patterns and anomalies in data values in a real-time fashion but it also provides multiple ways to act on those insights by empowering other systems like SAP Hybris and Salesforce to react.