Data Observability

CASE STUDY

$20 Billion Alcohol Distributor Increases Revenue Through Their Digital Channels with Modern Data’s DataOS

Read how the largest wine and spirits distributor in the US leveraged DataOS to optimize sales and marketing operations and to increase the value of their advertising inventory. Within six weeks, Modern Data helped establish a single source of data, centralized governance, data discovery, real-time data and processing, and the ability to securely syndicate.

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CASE STUDY

Global Logistics Company Transforms Shipment Tracking using Modern DataOS

See how Modern Data helped a billion-dollar logistics client make the shift to digital with DataOS, providing a single source of data, data discovery, real-time data, and processing. This transformation enabled the client to showcase real-time status of their shipment to their customer.

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CASE STUDY

Life Sciences Company Reduces Data Product Generation Time and Cost by 63% with Modern’s DataOS

Discover how, within six weeks, Modern Data helped one of the largest multinational pharmaceutical organizations in the world integrate data from multiple data sources, automate data quality and audit trails, and enable researchers to efficiently find and analyze drug development data.

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WHITE PAPER

A Modern Data Strategy for Enterprises

Regardless of industry, size, or product offering, every company has to ask the same question—“Regardless of the amount of data I have, how much of it is actually usable?”

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WHITE PAPER

DataOS Data Evolution & Modern Data Management

The world is rapidly shifting to a digital first model for every organization due to world events. The long-known expansion of the size of data has suddenly kicked up its pace.

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Today's systems have transformed into complex, open-source, and cloud-native services with no signs of stopping — distributed teams continue to develop and deploy at lightspeed. Within these systems, engineers may not always be able to identify broken links in the chain. This has resulted in the idea of "data observability" that includes monitoring, tracking, and triaging incidents to prevent downtime of the systems and around several factors such as freshness, distribution, volume, schema, lineage.

Want to see how DataOS solves your specific integration and data management challenges?

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