What is Contextual Data?
Contextual data, or context data, is information about the background surrounding other big data points. It’s the context within which all your other data observations take place.
Process plants produce a vast amount of time series data every day, gathered from sensors, monitors, and smart devices. Contextual data makes the flood of data larger, but also more manageable.
Big data is too much for human intelligence to cope with, but even advanced analytics struggle with large time-series datasets collected by plant devices. That’s because each data point appears to take place in a vacuum, without any relationship between the datasets. Without context data, machine learning (ML) tools have difficulty finding the patterns within the data that reveal cause and effect and enable identification of trends.
A busy process plant generates so much noise that it obscures those patterns, so data scientists need to invest a lot of time cleaning up data, creating asset models and hierarchies to define relationships, and assigning different data points to different asset lines and items of equipment.
Why Does Contextual Data Matter for Process Manufacturers?
Contextual data saves data science teams time and effort by cutting the amount of pre-processing work that lies ahead of them. It speeds up root cause analysis by helping process engineers make accurate connections between cause and effect.
With context data and time-series data, ML algorithms and models can detect true anomalies, instead of being overwhelmed by noise and producing constant meaningless alerts. Predictive analytics solutions use contextual data to identify true anomalies that indicate process inefficiencies or impending part failure.
Plants that apply context data can:
- Use predictive analytics to cut costs and achieve OEE
- Spot minor issues before they snowball to major catastrophes
- Forecast market trends to meet customer demand in advance
- Plan ahead to seize opportunities and mitigate risks while they are still on the horizon
- Reduce unplanned downtime
- Improve product quality
- Raise operational efficiency
How Can Process Plants Implement Contextual Data?
Improve data gathering
You can’t use contextual data if you aren’t collecting it. Ensure that your data collection strategy is integrated, including protocols for gathering context data and storing it in an accessible repository for ML tools to tap into.
Identify domain experts
Subject matter experts, or domain experts, can speed up the process of organizing context data by helping create a knowledge map of the plant, guiding ML tools to recognize connections and relationships between equipment, processes, and networks.
Ensure data access
Remove silos between your data sources so that every advanced analytics tool can draw on data from anywhere in the plant. It’s important to make sure that your data repository is source-agnostic, and all your data is stored using the same formatting.
Open up data insights
Once you’ve set up your data systems to draw on and apply contextual data, you need to make sure that the insights they produce are put to good use. Invest in user-friendly, intuitive data visualizations and business intelligence dashboards that are simple for any stakeholder to use.
What are the Benefits of Context Data for Process Plants?
Context data is the key for process plants to unlock the value hiding within their time series and general plant data. With context data, process manufacturing companies can track fluctuations in the market, understand their plant processes, spot inefficiencies, and performance gaps before they become serious, reduce waste, improve business planning, and enhance their bottom line.