Not Getting the Results You Want from AI? Here’s Why Manufacturers Need to Review Their Data Quality
Is your data the problem-child in your AI strategy? The odds are good that’s the case.
Despite the promises of increased productivity and efficiency, many manufacturers struggle to realize the benefits of AI without data quality control.
As probabilistic systems, LLM-powered AI tools make inferences based on data patterns and can only function if that data is consistent, clean, and accurate. Just like manufacturing finished goods, the final product is only as good as the raw materials.
Thankfully, the process of data cleansing and verification can help organizations discover anomalies, formatting discrepancies, and other data quality problems before that data is used for training. The trick to realizing the significant benefits of AI is setting and maintaining a standard of data quality and validation that mirrors the meticulous quality controls of physical production.
Here are some considerations for manufacturers to create AI data quality they can trust:
Data analysis is a supply chain, too
In the physical world, supply chain bottlenecks, transit inefficiencies, and other logistics issues can limit an organization’s ability to produce and supply products. The movement, storage, and analysis of data for AI are fairly similar. Data gathering, processing, and storage follow many of the same principles, as the collection and analysis of data depends on that “supply” being accurate and consistently available.
Not all data is created equal, and most organizations collect and store much more unstructured data than they will ever process, making their capacity to efficiently manage and utilize this data critical. That’s why it’s important to think of AI systems as a downstream step in a larger data processing infrastructure. Some data needs to be filtered out or corrected before processing.
This is why there needs to be clear data management and data governance standards throughout the enterprise. Whether that’s driven by a data steward or data management as a service (DMaaS), manufacturers need a dedicated focus on maximizing the accuracy and quality of data upstream.
The potential costs of bad data
The power of AI to rapidly react to changes in the market is a powerful tool but can also present a major risk if the stream of data contains errors. It isn’t hard to see how a system empowered to react instantly to datapoints can lead to significant issues.
For example, an AI monitoring system analyzing inaccurate data from an overactive sensor on the factory floor might infer that a key component of the supply chain is beginning to show signs of wear and initiate an accelerated maintenance timeline that increases spend and shuts down the manufacturing process. Because it was based on aberrant or noisy data, this unnecessary maintenance ends up costing valuable production time.
It should come as no surprise, then, that some studies place the cost of bad data in the millions. However, in the above scenario, the issue wasn’t AI, but processing the data and filtering out inaccuracies and noise. These implementation issues have led many organizations to reduce or abandon AI adoption. However, their competitors may still be able to benefit from the speed and processing power of AI, as a more careful implementation can create significant cost- and labor-savings.
Investing in a data supply chain
Proper data quality is part of any AI investment. Successfully deploying AI tools requires certain guardrails and steps to be in place, particularly for data processing. Some of the most common processing steps include:
- Standardization: Different sensors, regional formatting, spelling differences, and other factors can mean the same data type is collected with different values or in a different order. A processing step which handles these problems can help AI systems avoid mistakes.
- Deduplication: Data collection sometimes includes duplicate data which can skew a dataset to over-represent a particular value and taint any downstream AI inference. The same goes for missing datapoints, which can skew the data in the opposite direction.
- Noise: Datapoints which break with the established baseline are known as noise, and can be the result of sensor issues, aberrant behavior, or some other breakdown in an otherwise functional data gathering process. Cleaning a dataset means evaluating these values and separating acceptable noise from data points that will skew AI training and inference.
Assessing AI readiness and impact
Organizations can take the time to ask some important questions before incorporating an AI solution:
- Do we have enough quality data to train this model? Determining this early on can help prevent costly mistakes from poorly trained AI agents.
- What guardrails do we need to put in place to catch mistakes? Depending on the use case, some or all of what an AI tool infers should be double-checked before action is taken, particularly in the early stages after deployment.
- When and where does this AI tool need human input and quality control? AI should be viewed as a tool and not necessarily a replacement for human decision-making.
Use a strategic, iterative approach to AI
Getting the most out of AI tools doesn’t always mean pushing them into manufacturing, procurement, fulfillment, and every other part of the organization. Silicon Valley execs might think otherwise, but there is a balance you need to strike. A successful data cleaning process can point to the areas where the data is most structured, organized, and consistent, and therefore ready for AI tools, predictive analytics, and other automation techniques.
The excitement around AI’s potential can sometimes cause organizations to adopt tools without checking to see if their data is ready for training. An iterative approach, on the other hand, allows organizations to measure, adjust, and improve the performance of AI agents or other tools, setting up easy wins and rewarding their investment in the data pipeline. This approach can also reduce the risk of AI mistakes and inaccuracies, increase trust, and encourage adoption.
At the end of the day, adopting AI in manufacturing isn’t about “rubber stamping” every conceivable use case. It’s about using your knowledge of your data assets to build a cost-effective and trustworthy foundation. Because when your approach to data is disciplined, your AI will behave the way you want.
Are you looking to improve efficiency with AI initiatives? iSphere can help you successfully enhance the data that fuels your manufacturing AI use cases.
