Manufacturers are long on data and short on insights. According to research from IBM, just 28% of organizations “are using data from equipment, processes, and systems to draw insights for continuous process improvement.”
The problem? Big data alone rarely drives meaningful value without context and intent. To optimize current operations and stay competitive in a digital-first market, manufacturers need a way to convert big data into smart data. That is, information that is relevant, accurate and actionable.
Keep reading to learn more about big data and smart data, as well as how companies can bridge the gap.
What is big data in manufacturing?
Big data is comprised of large and complex datasets generated across manufacturing systems, but volume alone does not guarantee insight or operational improvement.
Common big data sources include IIoT systems, production system software and programmable logic controllers (PLCs), maintenance logs, quality control tools and computerized maintenance management systems (CMMS).
Big data focuses on quantity rather than quality, in turn creating challenges with usability. While more data can help companies see the big picture, it becomes harder to see the details.
Four characteristics are common for big data:
- Volume: Volume refers to the amount of data generated. Many companies now generate terabytes (TB) of data every day.
- Velocity: Velocity refers to the speed at which data is created and updated. With many production lines running 24/7/365, data never stops.
- Variety: Variety speaks to variation. Manufacturers now collect everything from material data to performance efficiency information, connected IoT sensor data and quality control results.
- Veracity: Veracity is truth: Is data accurate and reliable? Big data isn’t useful if data sources can’t be trusted.
What is smart data?
Smart data is curated, contextualized and purpose‑driven information produced through big data analytics. While big data is often collected and stored at scale, smart data is intentionally shaped to support informed decision‑making.
Three characteristics set smart data apart from its big data counterpart:
- Quality: Quality refers to data that is clear, concise and properly formatted.
- Relevance: Relevance refers to timeliness. While historical data plays a role in long-term planning, timely data is required to ensure actions address current conditions.
- Accuracy: Accuracy speaks precision. For example, high-temperature machines may require calibration of just a few tenths of a degree. Any more or less and outputs may not be usable. Smart data is accurate data.
Not sure if you’ve got big data or smart data? Start with a simple question: Is data simply stored, or does it help answer business and operational questions?
Key differences between big data and smart data
Both big data and smart data play a role in manufacturing operations. Big data lays the groundwork for large-scale trends analysis and can support alignment with regulatory expectations and guidelines such as good manufacturing practices (GMPs). Smart data, meanwhile, helps manufacturers pinpoint problems, define solutions and take targeted action.
There are four key differences between big and smart data:
1. Big data emphasizes collection; smart data emphasizes use.
2. Big data is general; smart data is specific.
3. Big data prioritizes content; smart data prioritizes context.
4. Big data is steady; smart data is fast.
Why big data alone often fails in manufacturing
Companies can’t afford to ignore the role of big data in manufacturing.
Historically, enterprises were only equipped to capture high-level data, such as total output volumes, cycle times or rework rates. The advent of small-scale, always-connected systems and sensors, however, enabled machine data collection at scale. Today, manufacturers can track and record every detail of equipment operations, from initial start-up to standard workloads to unexpected downtime. Every process from production line assets, every action from staff and every operation from software becomes part of the big data landscape.
The challenge? Data volumes can lead to tunnel vision; manufacturers assume that simply collecting data is enough to drive insight and prompt action. In practice, however, big data initiatives often fail to deliver value. Five failure causes are common:
- Data collection without clear objectives
- Poor data quality and consistency
- Lack of standardization across systems
- Limited ability to interpret and act on data
- Too many dashboards, too little insight
How smart data improves manufacturing performance
Smart data helps improve manufacturing performance because it provides actionable insights. Consider four large sets of structured data collected from multiple sources, including equipment sensors, controllers, user reports and operational benchmarks. Usable insight is contained in the data, but is only visible when data is validated, curated and analyzed.
These processes turn big data in manufacturing into smart data, which offers multiple benefits for performance. First is faster root cause analysis. Equipped with contextual data about how, when and why equipment failed, teams can resolve sources rather than symptoms.
Smart data also improves asset reliability and uptime. By combining current and historical performance data, teams can identify possible failure points that could lead to unplanned downtime and take steps to remediate these issues. For example, if analysis reveals that a high-workload asset experiences regular electrical faults, companies can schedule more frequent maintenance to solve the immediate problem while simultaneously searching for the root cause.
Other benefits of smart data include better quality and yield tied to accurate and current information about scrap and rework rates, along with more improved workload planning and forecasting based on both operational needs and equipment efficiency.
Finally, smart data fosters stronger alignment between teams. This is because smart data helps maintenance teams, operators, managers and C-suites speak the same language, reducing the risk of redundant work or missed opportunities.
The role of maintenance and reliability in smart data strategies
Maintenance and reliability data play key roles in smart data strategies. This is a reciprocal process. Tracking maintenance and reliability improves the impact of smart data, and smart data helps drive the continual improvement of maintenance and reliability processes.
Here, four benefits are common:
- Maintenance history provides context for failures: Maintenance histories offer meaningful insight to help uncover the context of machine failures and can support prescriptive analytics.
- Asset conditions can be linked to performance outcomes: Asset conditions are often comprised of both structured and unstructured data. Along with sensor outputs, companies may also incorporate user reports that contain recommendations based on years of experience. Combining these data sources drives better performance outcomes.
- Actionable data reduces reactive maintenance and risks of unplanned downtime: Reactive maintenance is expensive and time-consuming because it doesn’t start until failures occur. Using smart data to create heat maps, risk reports and data visualizations helps identify potential causes of unplanned downtime.
- Smart data underpins predictive maintenance analytics: Predictive analytics enable maintenance teams to stay ahead of potential problems. They also play a role in more advanced analytics that provide end-to-end insight around machine performance and potential improvements.
Smart data as a foundation for Manufacturing 4.0
Digital transformation enables the adoption of Manufacturing 4.0 processes, which rely on always-connected, always-on devices and equipment.
Smart data, meanwhile, is the foundation of digital transformation.
Consider artificial intelligence (AI) and machine learning (ML). These technologies drive the creation of smart factories that use current and historical data to “learn” over time and improve operations. The model training performed by ML algorithms and the data analytics performed by AI both require clean and contextual data. Simply turning these tools loose on big data sources will limit their efficacy. Leveraging smart data shortens the distance between inquiry and insight.
Smart data also supports the real-time machine health monitoring and optimization required by Manufacturing 4.0. In many manufacturing environments, extended equipment downtime is increasingly costly and disruptive. With access to smart data, teams can make immediate decisions that drive improved performance.
In addition, the use of smart data enables scalable and sustainable digital initiatives. Businesses can determine where money is best spent to expand production operations and identify ways to reduce costs, limit emissions and support new green technologies.
There’s also a case for using smart technology to stay ahead of the technology curve. As new AI-enabled assets and autonomous agents become commonplace, it’s easy for teams to get left behind. Smart data management helps pinpoint areas for improvement and suggest ways to ensure operational readiness.
Best practices for creating smart data in manufacturing
Laying the groundwork for smart data in manufacturing starts with five best practices:
1. Define clear business and operational goals: Smart data offers clarity, but only if you know what you’re looking at. Start with clear business intelligence and operational goals. Are you looking to improve machine throughput? Enhance output quality? Reduce maintenance response times? Understanding the end goal helps define the starting point.
2. Standardize data definitions and metrics: Create consistent metrics and definitions for data processing. This means connecting the dots on KPIs such as mean time to repair (MTTR), mean time between failure (MTBF) and overall equipment effectiveness (OEE) to ensure data is interoperable.
3. Focus on data quality over quantity: More data doesn’t mean better data. Where possible, choose higher-quality data over higher quantities of information. For example, precise temperature readings taken every 10 minutes are more valuable than ballpark estimates collected every 30 seconds.
4. Integrate data across systems: Isolated data isn’t smart. To maximize insight, integrate data across systems such as CMMS, enterprise asset management (EAM) and enterprise resource planning (ERP).
5. Build cross-functional collaboration: Ensure collaborative access to smart data to help build actionable strategies. This includes operators, maintenance staff, production leads, technology professionals and corporate leaders.
Smart data turns information into action
Big data offers volume. Smart data offers value. Both are necessary for data-driven manufacturing operations. Big data sets the stage for large-scale trend analysis and regulatory compliance, while smart data helps companies improve performance, reliability and cost control.
Turn information into action with in-depth analytics. ATS helps manufacturers apply smart data to improve reliability, performance and decision-making. Let’s talk.
References
IBM Institute for Business Value. (2022). Manufacturing 4.0: From data to decisions. IBM. https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/manufacturing-4-0
