DataOps for manufacturing: A 4-stage maturity model

Posted on 24 May 2022 by The Manufacturer

The promise of Industry 4.0 has many manufacturing leaders thinking big. They envision a future in which real time access to data opens the door to unprecedented levels of operational flexibility, predictability, and business improvement.

For many, early-stage wins often lead to larger projects that stall or fail to scale because their data infrastructure couldn’t support the increasing project complexity.

Enter Industrial DataOps.

DataOps (data operations) is the orchestration of people, processes, and technology to securely deliver trusted, ready-to-use data to all the systems and people who require it. The first known mention of the term “DataOps” came from technology consultant and InformationWeek contributing editor, Lenny Liebmann, in a 2014 blog post titled: ‘DataOps: Why Big Data Infrastructure Matters‘.

According to Leibmann: “You can’t simply throw data science over the wall and expect operations to deliver the performance you need in the production environment—any more than you can do the same with application code. That’s why DataOps – the discipline that ensures alignment between data science and infrastructure – is as important to Big Data success as DevOps is to application success.”

As Leibmann argues, many organisations approach Big Data primarily from the standpoint of data science; they know that the massive data they now have access to contain high-value insights, and they’re trying to determine what kind of analytics they need to extract that insight. However, you must also consider your infrastructure.DataOps for Manufacturing

DataOps for Manufacturing

DataOps solutions are necessary in manufacturing environments where data must be aggregated from industrial automation assets and systems and then leveraged by business users throughout the company and its supply chain.

Intelligence Hub is a DataOps solution specifically designed for the manufacturing industry.

This maturity model will help readers like you understand where you are on your own data journey – and where you need to go to achieve the results you expect. It’s a four-stage process that includes data access, data contextualisation, site visibility, and enterprise visibility.

Complete the form below to download the full whitepaper.