How Retail Can Build a Security-First Data Architecture

March 30, 2022 - 4 minutes to read

How to Unlock Data for Operational Reporting

E. Wallace By E. Wallace

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.

Why DataOps can transform how companies use operational data

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:

  • Unified, single data view: Silos will be the death of any data initiative. Outdated, missing, or poor-quality data affects operational reporting in particular because it needs more granular data in real-time.
  • Customized dashboard: Everyone needs access to data, but not everyone will need the same view. The right tool allows the user to view information relevant to their position in operations.
  • Self-service: Operational reporting shouldn’t go through IT, which creates bottlenecks for response times and regulatory reporting. Instead, a self-service platform ensures that data is put to use now.
  • Automation capability: Documentation and reporting can derail because of human oversight or error. The fewer manual processes in reporting, the better.
  • Transparency: Users need to be able to drill down on data to understand the context. A tool with filters and rules can help offer these deeper views.
  • Security: Releasing data to stakeholders requires a new way to think about permissions and security.

DataOps is often the step to managing data complexity, but companies can move one step further to manage data.

Building a data fabric to implement a responsive DataOps data strategy

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:

  • Unstructured data: Relational data systems have a more difficult time managing data that doesn’t fit neatly into columns and rows.
  • Reusable data: Traditional operational data tools relied on single data extractions to determine the cause of an issue rather than reusing data to form predictive insights.
  • IoT devices: The sheer volume of data offered by connected devices along the supply chain, distribution chain, and logistics requires a different architecture.
  • External data sources: Distribution and supply chain needs a window into vendor data, partner data, and other external data sources previously hidden from traditional operational data.

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.

DataOS enables companies to build their data fabric

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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