Imagine losing billions of dollars each year simply because of a lack of visibility. This is a reality for distribution and supply chain organizations thanks to incomplete shipment data. In an industry already running on the thinnest margin, industry leaders are keen to plug drains on their bottom line and improve inefficient processes.
However, industry leaders won't be able to realize those goals without one vital component: data sharing. Although it's in everyone's best interest to share, some obstacles present challenges to the free flow of vital data. Let's explore what stands in the way of unlocking data and how distribution and supply chain leaders can facilitate a shift to data sharing.
A complex distribution and supply chain involves multiple data streams
Past data collection included data lodged in silos from multiple sources and formats. Much of this collection included varying manual processes from pulling reports to sending email requests. This methodology created conditions for flawed insights based on outdated, incorrect, or missing data.
Today, sensor-based trackers are simplifying and automating data collection. They can provide real-time feedback on location and conditions for shipments, flag for potential issues, and help everyone down the supply chain better manage the process. However, there are two downsides to all this new data:
- Companies don't have the data infrastructure in place to ingest and process the amount of data generated by these devices.
- Companies can't access the right data from partners to complete the view of the supply chain.
Together, these obstacles prevent a clear picture of what's happening along the supply chain from forming. Even if data comes in real-time, no one can pivot quickly based on incomplete data. If supply chain leaders can connect these pieces through a comprehensive data platform, these multiple data streams become far more useful together.
The right stakeholders need access and integration tools
Once leaders unite data sources, they need to distribute this data to the right people. A modern supply chain can't wait for top-down insights. Instead, using cloud or hybrid tools, leaders can quickly push relevant data to team members on the ground for rapid response.
This requires a tool that can operate in multiple environments—hybrid, cloud, multi-cloud, and even on-premise. Enterprises may have many different data environments that need connection, so the data platform should accommodate those needs.
This collaboration also requires deep integration capabilities. A data platform should allow leaders to share data with partners and other organizations involved in their supply chain to complete the data picture. Where fragmented, proprietary data silos used to be the norm, companies must shift to an open flow of data while balancing security and privacy.
This is a significant obstacle. Companies require a feasible governance model that operates enterprise-wide as third and even fourth-party data come into play. Even more, they need something that will scale and adapt as the supply chain evolves.
Reducing costs and risks will outweigh concerns over data sharing
Data sharing allows companies greater precision in setting smart timelines and plans and managing all resources. Companies might be able to reduce unnecessary inventory, for example. In other cases, data sharing allows everyone along the supply chain to predict and prepare for things like seasonal items or global disruptions like the pandemic.
Data sharing also helps mitigate the natural risks of moving goods through the supply chain. Operators can use shared data to monitor the conditions of shipments to ensure goods arrive unspoiled. The more data is shared and available, the better everyone along the supply chain can identify potential threats and either adjust to avoid them or notify customers of the delay with more specific timelines.
Creating a data-sharing culture requires the right data platform
A data operating system is the key to creating the conditions that make data sharing worth it. Some of the features of a data operating system include:
- Comprehensive integration: A data operating system provides the connective tissue to all the necessary tools and data sources.
- Comprehensive governance: Organizations will need to rethink governance, shifting to a model that frees data without creating loopholes from successive iterations of governance settings.
- Customizable dashboards: Dashboards provide important visualization tools and should make those insights available based on what stakeholders actually need to see.
DataOS provides the right foundation for successful data sharing
DataOS offers a next-generation data platform designed to integrate existing tools and operate under multiple hosting configurations. It's designed for business use with prebuilt algorithms and a right-to-left approach to data production. Essentially, business users can search for data using a search engine-like program and run queries without knowing code. For more technical users, a command-line interface is available.
Companies can approach governance using a revolutionary approach, as well. DataOS uses tag-based permissions to grant access to data. This prevents mistakes and delays in granting permission to use certain tables. Businesses can grant approval across the board based on user attributes, freeing up data for real-time use.
As data sharing becomes more common, DataOS provides the necessary capabilities to ingest data, collaborate, and scale. It takes data-driven decisionmaking to the next level and enables supply chain and distribution leaders to harness a complete, transparent view of data.
