Unreliable information can wreak havoc on your asset management, supply chain resilience, and profitability. Amplifi shares their tips for creating a data-quality conveyor belt.
In manufacturing, quality control is everything.
At every stage of a product’s lifecycle, from resourcing through production to delivery, measures are taken to make sure that products are free from defects, damage, or manufacturing errors. There are procedures in place to make sure mistakes don’t happen, and if they do slip through, there are protocols to spot them and stamp them out – making sure that the customer receives the product they ordered in excellent working order, and poor-quality products don’t see the light of day.
When it comes to quality controlling data in the manufacturing sector, the same stringent measures aren’t taking place across the sector. Yet data quality should be equally as important to manufacturers as the quality of their products: just as sub-par products pose a risk to a manufacturer’s reputation and profits, so too can poor quality information.
On one hand, good quality data has the potential to help manufacturers manage their assets, improve their efficiency, tighten up compliance, boost employee satisfaction, strengthen supply chain resilience, enable automation, AI, and analytics – and ultimately raise their ROCE (Return on Capital Employed).
On the other hand, bad data can impair efficiency, cause accidental non-compliance, dilute analytics (preventing fast-paced adaptation and agility), impact employee morale, generate production errors, and disrupt supply chains, all resulting in reduced profitability.
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 aren’t where they 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 be a broader organizational 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 standardization entails.
Stage 2. Assess your data quality in the here and now
Now you’ve put a pin in where you want to go, where are you now? Is your data largely available and accurate, or are data quality problems rife, with missing, inaccurate, or duplicate data across your systems? A data quality initiative will help you to review your data in full to understand the scope of work that’s needed to get your data from A to B.
Stage 3: Analyze the gap between the data you want, and the data you have.
Unfortunately, there is no magic wand that will turn the data you have into the data you want – but it’s not necessarily as difficult or as lengthy a process as you might think. Working with an external partner will help you quickly flag data quality hot spots and identify what needs to change to bring your data up to standard.
Step 4: Improve your data quality
This is essentially the ‘assembly’ part of the process: putting together the data quality attributes you need and making sure they are applied across all relevant data. You may choose to focus on one aspect or source of data to start with, and build your data quality in increments – this is often a less daunting approach.
Step 5: Control future data quality with Data Governance
A data quality initiative alone can’t ensure that these control measures are met, although it can set the rules and parameters that need to be actioned. To maintain data quality over time, you need Data Governance. Just as your manufacturing processes – from the machinery you use to the training your employees are given – enable you to maintain product quality, Data Governance provides the framework you need to keep consistently high-quality data long into the future. It’s a stepping-stone to a strong data culture, tackling behaviors and processes to ensure that every aspect of your business understands what good quality data is, and why it’s important.
For more advice on using good quality data to boost every aspect of your manufacturing business, download our guide: Culture clash: Creating a data culture in enterprise manufacturing.