March 30, 2022 - 4 minutes to read
Supply chain and distribution complexity requires big data, but big data introduces its own set of challenges. Without the right tools, companies can set up a vicious catch-22: gather plentiful data to unravel operational bottlenecks, but get swamped in data and create new ones.
Companies need a new data paradigm—one that puts data into production rather than storing it in siloed containers. Organizations may focus on digital transformation in terms of analyzing customer behavior or determining market trends, but analyzing operational data in real-time holds much potential for improving a company’s bottom line. The key is implementing a unified image of operational and process data to remediate issues at the system level.
DataOps takes a process-oriented perspective of data. It puts data into constant motion by automating manual processes and adopting continuous improvement as a rule. Optimizing operational reporting ensures that only real-time data informs reports and that companies can quickly pivot in response to feedback.
What should tools look like in this data environment? Here are a few key features that facilitate DataOps for operational reporting:
DataOps is often the step to managing data complexity, but companies can move one step further to manage data.
The purpose of leveraging DataOps for a unified view of operational processes is to move away from reactive policy. It doesn’t do companies any good to understand why a bottleneck happens if they can’t keep it from happening again. This requires a semantic layer that integrates all data sources, APIs, and storage containers—no matter where they originate.
Data fabric offers new solutions for challenges traditional data management can’t cover:
Gartner calls data fabric key to modernizing data management and integration. Companies can ask real-world questions, receive answers for how certain changes or variables will affect operations, and get actionable insight into mitigating risks like downtime or inventory bottlenecks.
Data fabrics can connect operations to the overall picture, offering an unprecedented look into the entire organization. Companies can understand how demand volatility affects operations and how inefficiencies cost the company lost revenue or customers. A data fabric enables companies to pivot quickly and spot challenges before they happen.
Operations teams can leverage a data operating system to configure a data fabric for operational visibility. With DataOS, companies can successfully stitch together existing tools and management systems. With an increasingly complex data environment, DataOS can unlock data from silos to offer unprecedented visibility into operations.
This is operational visibility at its best. With a data fabric configured through DataOS, operational reporting becomes easier and more straightforward in just a matter of weeks without getting rid of the tools that teams already use. It is the fastest path from data to insight and can move companies from reactive to proactive operations.
Discover how an operating system can revitalize a complex distribution chain with our case study: “$20 Billion Alcohol Distributor Increases Revenue Through Their Digital Channels with Modern Data’s DataOS.”
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