A 2021 study found that, despite the availability of data in the sector, only 39% of manufacturing executives had managed to scale data driven use cases like automation or asset analytics. Data and asset information just isn’t where it should be for many manufacturers, and we know that data quality is holding these enterprises back from fulfilling their next stage of digital transformation.
We also know what they need to do to get over these obstacles and secure good data that can fuel their ambitions to become leaner, stronger and smarter manufacturers – having helped others in the sector to tackle their data quality and build a stronger data strategy as a result.
So, as a manufacturer, how can you replicate the stringent quality control you apply to your production processes, to the data that flows through your business? In this blog, we outline what you need to do to get the accurate information you need to drive your business.
The data quality conveyor belt
How to construct a data assembly line to tackle your data quality and manage your assets.
Stage 1. Define what ‘good data’ looks like
What are you trying to achieve with your data? The first step is to identify your commercial goals and figure out how data aligns with them. It could be an obviously data-led objective, like working to an industry standard such as ETIM. It could be ESG related, like needing to implement Scope 3 reporting. Or it could a broader organisational goal, like wanting to improve profitability. Identifying your goal for data will influence what ‘good data’ should look like: for instance, if your focus is on an ESG initiative like Scope 3, you’re going to need available emissions data across your supply chain. If your goal is ETIM, you’ll need to understand what that data standardisation entails.
