Leaders in manufacturing are becoming more optimistic about AI, but integrating it into their systems remains a distant prospect. Data quality issues persist as a significant barrier to adopting and deploying AI technology. Without clean and accurate underlying data, AI insights—even those produced by advanced models—can go wildly askew, rendering them unreliable for use.
A Riverbed report, based on a survey of more than 1,000 decision-makers across the IT, business, and public sector industries in the U.S., UK, Spain, Germany, France, Australia, and Saudi Arabia, highlighted crucial statistics that sum up how AI and internal data are closely intertwined. Here are some compelling points from the report that underscore the value of clean data when leveraging AI:
- Nine in 10 or 92% of manufacturing leaders believe AI will give them a competitive edge. 65% of these leaders even predict it to be a key growth driver by 2027.
- Despite the enthusiastic projections, only 32% feel equipped to implement AI into their systems. The fact that this is 5% lower than the industry average signals that manufacturing businesses still have a long way to go to fix data issues and adopt AI.
- Data issues are also holding back manufacturers from reaching their full potential. For example, nearly one in four AI projects (23%) are underperforming in relation to company goals due to data issues.
- The simple fact is that companies need high-quality and accurate internal data to train intelligence systems. However, 42% of decision-makers cite organizational data issues – measured by accuracy and completeness – as a major roadblock to investing in AI.
The message is clear: if businesses intend to ride the AI wave, they need to fix their internal data—and fix it now.
High-Quality Data Is the Missing Piece in the AI Puzzle
Experts cite the “garbage in = garbage out” theory when describing the interaction between AI and data. AI systems aren’t trained to think; they run solely on the data they receive. When they receive clean, relevant, and accurate data, they learn meaningful patterns and deliver smarter outputs and insights that companies can use. The opposite is also true. If intelligence systems are fed “dirty” or duplicate data, they will be unable to deliver actionable insights.
For example, consider a company that manufactures automotive parts with an AI-powered predictive maintenance system. The system analyzes sensor data from machines on the factory floor. It then predicts when a machine is likely to fail and alerts the manufacturer accordingly.
If the AI system receives well-labeled machine logs and high-resolution sensor readings, it will be able to accurately predict failures before they happen. However, if it receives suboptimal or incomplete data, such as inconsistent timestamps or outdated logs, it might trigger false alarms or, worse, miss the early warning signs of failure.
Such data quality issues, also referred to as “data smells,” can be problematic because they infiltrate the system with errors, causing subsequent issues that cost time and resources to fix.
In an interview with Stack Overflow, Satish Jayanthi, CTO and co-founder of Coalesce, noted, “Data is crucial for AI, but I believe the quality of the data is even more important. To train the AI system, you need to feed it clean input, which will determine the quality of the output. There’s a direct relationship between the two. Whether it's a Large Language Model (LLM) where you're interfacing with natural language, or it's just an AI model trained for a particular function, like fraud detection, it has to be trained on high-quality real-world data.”
Informal data cleaning approaches, where teams reshape or fix dirty data on the fly, won’t suffice. Fixing data issues calls for automated integrity checks that ensure the data fed into intelligence systems is reliable right from the start, not patched later.
Why You Need Alpha Software to Fix Your Data Problems
Concerned about not being ready to implement AI?
To address the readiness gap between AI and data, you first need to ensure your company’s internal data is:
- Organized
- Precise
- Easily Available
The above is possible when businesses embrace a digital-first approach—this is where Alpha Software can help you bridge the gap. It can help you transform into a digitally mature, data-driven company, so you’re fully equipped to leverage AI when the time comes.
Here’s how Alpha Software helps you tackle data issue concerns:
Provides a Centralized Data Pipeline
Alpha Software helps you gather all of your business data, whether from legacy systems, manual logs, or spreadsheets, into a unified system. This means teams and LLMs will no longer have to deal with fragmented data from various departments and platforms.
With Alpha’s low-code features, you can bring all data sources under one app without having to rebuild older systems. A centralized app helps diverse teams enter data in a verified and structured manner, eliminating duplicate inputs and errors caused by manual entries.
Gives Access and Visibility to Real-Time Data
The success of AI systems and their output, whether spotting patterns or making predictions, hinges on accurate, real-time data. Alpha Software helps you build live integrations that keep data current and continuously streaming in, unlike batch updates.
Whether you’re using cloud services, like Salesforce or IoT devices, on the factory floor, you can access and view actual data through a centralized dashboard. As a result, intelligence systems are only fed clear, real-time data, not conflicting or patchy data.
Automates Governance Checks
What if there were a way to verify data against a checklist as it was entered? Thanks to Alpha Software’s low-code app builder, this is doable. Using automated governance checks, companies can filter out dirty data at the outset so that AI tools only receive clean data. These checks integrate user permissions, field-level validation rules, and audit trails into digital forms and workflows, allowing you to verify every piece of data during the input process.
For example, a manufacturing plant could use an Alpha Software-powered app to log machine performance data during shift handovers. As such, the operator would have to complete a digital checklist that records temperature and pressure, as well as downtime incidents. Whenever an entry exceeds acceptable limits, such as an unusually high pressure reading, the app will flag it.
This ensures AI tools only receive verified and structured data, helping them deliver precise insights related to equipment performance or maintenance needs.
Makes Legacy Systems Compatible With AI
Instead of letting your legacy systems trip your AI plans, you can work with them—simply by digitizing them with Alpha Software.
Companies that integrate AI into their systems without first ensuring compatibility with older legacy systems run the risk of feeding it unreliable data. This is because data stored in such obsolete systems is likely scattered, messy, and non-standardized, making it unusable for AI analyses.
Alpha Software can help standardize and consolidate this data without requiring you to rip out and replace existing systems. It uses custom integrations and APIs to reroute old data from ERP systems or spreadsheets and integrate it into fresh digital workflows, making it more AI-ready.
Helps You Scale
A growing company will generate large volumes of data. However, more data doesn’t automatically mean better insights. If your data isn’t clean or well-formatted, even the most sophisticated AI tools will struggle to make sense of it. Fortunately, Alpha Software helps you stay in control as you scale, making it easier for companies to start small and expand over time. You can gradually incorporate new features into your custom app, add more users, or sync growing systems, all while keeping your data quality intact, even as the volume increases.
Accelerates Your AI Implementation Timeline
Rolling out AI systems without first fixing data issues is like putting the cart before the horse. You’re bound to run into costly errors and spend resources on fixing data patches that could have been cleaned from the start. Backward processes like these greatly jeopardize the AI adoption timeline.
Alpha Software helps you address the most challenging aspects of the AI-data relationship early and head-on. It helps you:
- Set up automated system checks
- Structure and store your data in a unified digital system compatible with AI tools
- Clean and standardize scattered or siloed records early on
These steps mean your business is prepared for AI rollouts whenever they happen. Most importantly, you can trust the results and insights AI delivers from your underlying data. This puts you well ahead of competitors who waited too long to prepare, or worse, jumped in on the AI wave without data checks.
Businesses that are digitally prepared will ride the wave. Will yours be one of them?
Make the smart choice. Adopt a digital-first mindset and fix your data problems today so you can be AI-ready. You don't need developers or IT resources - we can build the solution for your team and tie into your systems of record, based on your workflows and business requirements.
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