Data Historian Uses and Benefits Executive Summary

In today's world of industrial operations, data is generated at an unprecedented rate. It is essential to capture, store, and analyze this data to gain insights into the operations and make informed decisions in real-time. This is where real-time data analysis comes into play, and data historians are a key tool for capturing, storing, and analyzing this data.

Definition of real-time data analysis

Real-time data analysis is the process of analyzing data as soon as it is generated. It involves the collection and analysis of data in real-time, enabling users to respond to changes and make informed decisions quickly.

Advantages of using data historians for real-time data analysis

Data historians are designed to capture and store time-series data at high speeds, making them an ideal solution for real-time data analysis. Some advantages of using data historians for real-time data analysis include:

• Real-time data capture and storage: Data historians capture and store data in real-time, making it available for analysis immediately.

• Historical data analysis: Data historians store data over time, enabling historical analysis and trend identification.

• Scalability: Data historians can handle large volumes of data, making them ideal for industrial operations with large amounts of data.

• Security: Data historians provide a secure environment for storing and managing data, ensuring data integrity and preventing unauthorized access.

Examples of source systems that can be tapped into to capture real-time time-series data such as DCS, SCADA, and MES systems

Real-time data can come from a variety of sources, including Distributed Control Systems (DCS), Supervisory Control and Data Acquisition (SCADA) systems, and Manufacturing Execution Systems (MES). These systems capture data from various sensors and instruments in real-time and provide a continuous stream of time-series data.

Real-time data analysis use cases

Real-time data analysis has numerous use cases across various industries. Some examples include:

• Predictive maintenance: Real-time data analysis can help identify potential equipment failures and perform predictive maintenance to avoid downtime.

• Quality control: Real-time data analysis can identify quality issues in the production process and allow for immediate corrective action.

• Energy management: Real-time data analysis can optimize energy usage and reduce waste.

• Process optimization: Real-time data analysis can identify process inefficiencies and enable process optimization.

The role of data historians in enabling real-time data analysis

Data historians are a key tool for enabling real-time data analysis. They capture and store time-series data in real-time, making it available for analysis immediately. They also provide historical data analysis capabilities, enabling trend identification and performance analysis.

Benefits of real-time data analysis for industrial operations

Real-time data analysis can provide numerous benefits for industrial operations, including:

• Improved efficiency: Real-time data analysis can optimize operations and reduce waste, leading to improved efficiency.

• Cost savings: Real-time data analysis can identify opportunities for cost savings, such as reducing energy usage or avoiding equipment downtime.

• Improved product quality: Real-time data analysis can identify quality issues in real-time, enabling immediate corrective action and improving product quality.

Challenges to be overcome when implementing data historian solutions:

• Cost: Implementing a data historian solution can be expensive, and companies may have budget constraints. In some cases, there may be additional costs associated with hardware upgrades, software licenses, and personnel training.

• Data quality: Poor data quality can significantly impact the accuracy and effectiveness of real-time data analysis. Data historian solutions must have measures in place to ensure data integrity and accuracy.

• Data integration: Companies often have multiple data sources, and integrating them into a data historian can be challenging. The data must be standardized, and different formats must be converted to a common format.

• Scalability: As the amount of data collected grows, the data historian solution must be able to scale efficiently without causing any system performance issues.

• Security: Data historian solutions must be secure to ensure the confidentiality and integrity of sensitive data.

Best practices for implementing real-time data analysis using data historians:

• Define requirements: Before implementing a data historian solution, it is essential to define the business requirements, goals, and objectives. This helps in selecting the right data historian solution that meets the company's specific needs.

• Data integration: Companies should start by identifying the data sources they want to integrate and work towards developing a standard format. They should also develop a data quality assurance process to ensure data accuracy.

• Scalability: It is crucial to choose a data historian solution that can scale to meet the growing data demands of the company. Additionally, companies should regularly review their data storage and processing requirements to ensure that the system can handle it.

• Security: Security is a critical consideration when implementing data historian solutions. Companies should implement security measures such as access control, encryption, and firewalls.

• Training: Personnel training is essential to ensure that employees can use the data historian solution effectively. Companies should provide training to employees to ensure they understand how to use the system and interpret the data.

Future trends in real-time data analysis and data historians:

• Artificial intelligence: Artificial intelligence (AI) has the potential to revolutionize real-time data analysis by enabling predictive and prescriptive analytics. Data historians that integrate AI algorithms will provide advanced insights that help industries make better decisions.

• Cloud computing: Cloud computing will enable industries to store and process large amounts of data cost-effectively. Cloud-based data historians will enable companies to access real-time data from anywhere at any time, enabling remote monitoring and decision-making.

• Edge computing: Edge computing is a trend that involves processing data locally instead of sending it to the cloud or a centralized server. Data historians that incorporate edge computing will enable companies to process and analyze data faster, improving the responsiveness of real-time data analysis.

Conclusion: Real-time data analysis is a critical requirement for industrial operations. Data historians have become the go-to solution for real-time data analysis due to their ability to collect, store and organize large amounts of time-series data. However, implementing data historian solutions and real-time data analysis comes with its challenges. To overcome these challenges, companies must define their requirements, integrate their data sources, ensure scalability and security, and provide personnel training. Additionally, future trends such as artificial intelligence and OT-IT convergence will place high demands on historian system throughput and data coverage which will be challenging to satisfy cost-effectively.