Distribution / Supply Chain

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

$1 Billion Logistics Company Improves their Customer’s Shipment Tracking Experience using The Modern Data Company’s 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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How Data Fabrics Can Help Prepare Today’s Supply Chains for Uncertainty
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How Data Fabrics Can Help Prepare Today’s Supply Chains for Uncertainty

Supply chains are complex webs that are composed of people, products, and processes. When governed by bad data strategy, each component functions as a siloed node in a linear system operating in a relay. When disasters hit one node, entire systems across the globe can crumble.

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The multitude of functions that manufacturers must balance don't simply end at their doorstep. Product innovation, engineering, forecasting, production, and logistics all affect their partners, clients, and vendors. This creates a complex network filled with gaps in data, latency, and access barriers that complicate operations at every step of the way.

The pandemic's massive disruptions tested global supply chains and pushed many to their breaking point. Bad data strategies are largly to blame as they treated data systems like a relay race when they realistically function more like a complex network that needs to be in constant sync to respond to changes. During peak demand across the course of the pandemic, data latency between linear handoffs from source systems prevented suppliers from reacting to changing marketing demands.

Traditional data management systems were built for when each node in the chain had time to react and the enterprise data landscape was much more simple. Today's data landscape is much more different and supply chain data needs to be flexible and reusable. Manufacturers need to be able to switch from a reactive data strategy to a responsive one, and we already see many making the effort to adopt new data innovations like data fabrics.

Enterprise data fabrics weave together data from data from disparate sources to create a network of information to power real-time business applications, artificial intelligence, and analytics. Teams outside of core tech departments can ask questions and better understand real-world scenarios and their consequences.

Read through our resources for an in-depth look!

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