DataOS enables frictionless access and sharing of data in a distributed data environment. It enables a single and consistent data management framework, enabling seamless data integration with plug and play and self-serve capabilities for business users. It is a cloud-native platform providing an entire suite of data primitives, powered by a graph-based datanet. Simplified data ingestion, processing, transformation, and syndication allows the DataOS to become the source of truth for all data within your enterprise.
Respond Quickly to Business Needs and Competitive Threats
Enterprises are spending heavily on a range of data products and patching them together to resemble a Data Fabric. These data products require significant integration work and customization resulting in a lack of flexibility. DataOS humanizes data and its access, breaks data silos, and transforms companies as they move towards data democracy and gain business insights in real-time.
Plug and Play Data Products
Use value-driving data products like Snowflake, Google Tensor Flow, Azure ML, C3.ai, etc. in a plug-and-play fashion. Bring on products that will drive value without concerns for architecture changes, implementation challenges, professional services costs, and other deployment challenges.
Control in Your Hands, Instead of Vendors
Our approach of centralized data management, data quality control, governance, etc. ensures that you control the data in your organization and make the front-end products replaceable without big vendor lock-ins.
Understand and respond to changes happening to your business in real-time. Our data infrastructure makes technology transparent while you focus on ways to put data to use and grow your business.
DataOS identifies changes in data patterns and anomalies in data values; all in real-time. It also provides multiple ways to act on these insights by empowering other systems like SAP Hybris and Salesforce.
In this new age of working remotely, the importance of collaboration on data workloads, similar to tools like Slack, Asana, Jira, and Google Docs, becomes essential. DataOS has collaboration built in so teams work on data prep, data blending, queries, dashboarding, charting, data quality, data syndication, and much more.
The Modern approach enables customers to launch multiple vendor PoCs at the same time and share data with vendors in a scalable / compliant fashion. Launch experiments in a matter of days vs. months.