TL;DR;

DataOps is a data-driven approach that helps with rapid insights and automation, reducing the cycle time on data analytics.
DataOps also helps you build a more agile digital supply chain by enabling analytic teams to automate their processes, which in turn reduces cycle time on data analytics. Getting this data infrastructure right is critical for helping Operational Technology (OT) get the most out of real-time data to optimise processes. DataOps is part of your ability to move quickly in the digital space. It should be part of your overall approach to your developer velocity and can help staff to discover the data they need to drive insightful improvements in your organisation.

Manufacturers, data is your new best friend! You need to take data and turn it into insight. DataOps is a data-driven approach that has been seen as an essential element of supporting operational technology for gaining insights more quickly. DataOps also helps you build a more agile digital supply chain by enabling analytic teams to automate their processes, which in turn reduces cycle time on data analytics. Manufacturers can take the lessons learnt from lean manufacturing and continuous improvement and apply them to their data with DataOps.

Common goals for DataOps are:

  • Reducing data integration latency to ensure faster decision making
  • Reduce data quality defects to make fewer faulty decisions
  • Reduce process implementation and maintenance times to lead to more informed decisions
  • Reduce complexity of individual operations to lower processing and storage costs

The current best practice implementation of a data tier to support these goals is a “modern data warehouse”, combining a file-based data lake as an integration point and curated relational datasets to surface data for analysis and reporting.

Getting this data infrastructure right is critical for helping Operational Technology (OT) get the most out of real-time data to optimise processes. Building this robust capability enables a more effective digital supply chain. Currently only 13% of EU manufacturers have a data infrastructure that could support prescriptive analytics 1 where the platform can suggest what ought to be done in inventory, scheduling, and workforce management.

Over the long term, developing an effective DataOps capability involves looking at how you load, track, verify, consume, and discover data in your organisation. It typically involves a mix of business decision makers as the primary stakeholders and a varying mix of IT people.

At a small company with limited data, the IT person might even be a proactive finance team member, a Data Champion who can start using something like Power BI to integrate data and surface it. As the company size grows, you’re more likely to need dedicated data engineers who can develop and manage your data solutions. To help the company embrace data driven decision making and enabling staff to start developing data integrations or solutions to meet their needs, your data engineers might act as a Centre of Excellence, fostering change within the company.

DataOps is part of your ability to move quickly in the digital space. It should be part of your overall approach to your developer velocity and can help staff to discover the data they need to drive insightful improvements in your organisation.

Further reading & watching

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