Glossary term

Data Historian

The amount of data that’s created every day is impossible for the human mind to comprehend—and that number continues to grow. In fact, it’s estimated that the amount of data generated daily is expected to hit 463 exabytes by 2025. With such enormous amounts of data present, businesses (and especially process manufacturers) are looking for ways to use that data to extract actionable insights.  

To do so, they’ll need database tools that collect, store, and analyze data from a variety of sensors and IoT devices. This is where data historian solutions come into the picture. 

What Is a Data Historian? 

Think of data historians as a bridge that connects data to insights—they’re a type of time series database specifically designed for industrial and process manufacturing that gather and store time series data from industrial automation systems such as SCADA (supervisory control and data acquisition). Most often used in data and industrial control systems, data historians automatically log production and process data and compress it for more efficient storage and faster retrieval when needed. 

The stored data can be used to present process data trends on charts, generate reports, or execute data analysis. Modern process data historians are widely used across different industries, serving as an important tool for supervisory control, performance monitoring, advanced analytics, and quality assurance. Organizations using data historians can gather information about program operations to diagnose failures when reliability and uptime are critical. 

The Importance of a Data Historian 

Data historians automate the collection of data from numerous sensors across the organization so that data analysts and engineers can draw actionable insights from them. 

For instance, in manufacturing plants, systems, processes, and equipment are constantly running, and it’s essential that they communicate and work together well. Data historians can analyze real-time data and provide insights on all parts operating together to check for defects, possible causes of inefficiencies, and maintenance plans. Using the resulting insights, organizations can suggest predictive maintenance, identify areas that need to be monitored, and improve business decision-making. 

The Benefits of a Data Historian for Business 

Data historians help businesses analyze data to identify ways to improve the production process. Here are a few benefits of using data historians in manufacturing: 

Streamline Insights 

Storing critical information from different databases makes data collection and extraction slow. A data historian gathers all data from a variety of databases and makes it more accessible. 

For example, once data is acquired from temperature sensors, it’s linked to contextual data like the beginning and end production time of a specific batch. If the temperature is too hot and the production took three minutes longer than normal, engineers can flag that batch as an anomaly and discard it, rather than risk it makes its way further along the supply chain. 

With a data historian, sensor data from across the plant is stored in one place, making it easier for users to make quick decisions, identify harmful patterns, and take decisive actions quickly. 

Improve Plant Productivity 

If the factory line is producing a high ratio of defective products, operators can use the data historian to conduct a thorough analysis of the conditions in which a specific batch was made—this helps determine where issues happened and how the product quality was compromised. 

By identifying the exact production issues, organizations can encourage a model of continuous improvement (CI) to constantly improve their operations. They can identify and prevent anomalies, improving their overall plant productivity, cutting down on extraneous costs, and preventing downtime. 

Reduce Energy Consumption 

For many companies, especially those in high emissions industries like manufacturing, environmental concerns and regulations are top of mind. Among other things, data historians can collect and analyze data related to energy production and consumption, such as the amount of fuel or kilowatts used per hour, which can help users identify waste and areas that can be made more efficient. 

Take natural gas providers for example. They often use flare stacks to monitor surges in pressure, manage emissions, and track burn-off. Monitoring these with traditional A/V sensors is time-consuming and may fail to provide the correct insights for the operator to swiftly identify emissions and respond to them promptly. 

By utilizing time-series data from data historians, machine learning models study and assess flares automatically. This allows them to take appropriate action in real-time and prevent future incidents from occurring. 

Secure Data 

Data historian software comes with enhanced security features such as multi-factor authentication, case-sensitive passwords, and built-in alarm management systems, that significantly reduce the risks of data breaches. 

Data historians also make it easy to keep data secure. Only authorized personnel have access to all information in the database, and the primary operator can give specific permissions to individuals depending on their title and job function, limiting exactly what data they have access to, and limiting the risk of sensitive information leaking. 

What Sets Data Historians Apart 

In general, time-series databases are built to process and analyze time series data, like reading digital sensors. They’re open source and can handle any non-relational data (or NoSQL)—for instance, tracking the comments, likes, scrolls, and uploads of billions of Facebook users in real-time within a certain timeframe. 

Data historians go one step further, combining the functionality of time-series databases with improved business intelligence solutions to help businesses leverage the value of the data collected from their manufacturing plants. They can handle more data than typical time-series databases and they also feature process management control systems, built-in quality control, and anomaly detection to help connect data to insights. They can utilize BI, analytics, and reporting to gather information about a program to identify failures and provide solutions in real-time. 

To Wrap Up

By utilizing data historians, manufacturers can separate themselves from their top competitors. Rather than just having data at your disposal, you can use that data to analyze business operations, improve performance, and increase profitability. 

Want to learn more about how data science could level up your operations? Check out our manufacturing solutions page to see how you can use the shop floor data you’re already collecting to improve product quality and bring more goods to market faster. 

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