DataOps in a Manufacturing Company – Anomaly or Solution?

Knauf Insulation is one of the world’s largest manufacturers of insulation products and solutions. We are present in more than 40 countries and have 27 manufacturing sites in 15 countries. The challenge we faced was quite simple on paper: We had to centralize all data from production sites and start creating value from that data, either by basic analytics or complex optimizations. A key driver was to optimize the production process by decreasing downtime, reducing energy consumption and improving the quality of our product.

As with most companies, our initial plan was to set up a data science platform and get all the data in the cloud data lake, providing a team of data scientists with everything they need. The premise was that a data lake is just an enormous disk and accessing the data later on is simple for a reasonably skilled data scientist. Well, not exactly. The former is only true when the data quantity is low. Our data quantities are not extreme, but we are not in the realms of classical one-machine SQL anymore.

So how do users actually access the data in an efficient and fast enough manner? We created a skilled DataOps team that sits between the Central IT and our end users, data scientists and engineers in the plants. The end user has to know more about the business than about data science, that is why at a manufacturing company he usually comes from the business. How do we make work easier for him?

Removing complexities from the end user is what we have in mind every day. Working with reasonably sized data is easy, so we decided to do distributed computation in the background when possible, by leveraging a subset of tools that is small and manageable but it still covers most of the needs that we have.In the early days, advanced analytics was only feasible in large software companies with almost unlimited investment capabilities and an abundance of domain knowledge.

Today, non-software companies can also be successful when using the right strategy. We at Knauf Insulation strongly believe that a key part of that strategy is a DataOps team that works as a bridge between existing IT-based teams and on-site engineering teams.

Topics Covered

Azure Data Lake Storage - Dremio
Business Intelligence
Data Lake Engines
Dremio Subsurface: Advanced Storage Solutions
Subsurface: Dremio Insights
Subsurface: Tableau Insights

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