In the ever-evolving landscape of industrial process industries, data is king. The ability to collect, store, and analyse vast amounts of data is not only a competitive advantage but also a necessity for optimising operations and ensuring compliance with regulations. Data historians are an essential tool in this context.
This article explores what a data historian is, its importance in industrial process industries, how it differs from SQL databases, the features and components of different types of historians, effective implementation, and the many benefits of their operation. We’ll also delve into the future of data historians, the current market, and key industry players.
What is a data historian?
A data historian is a specialised software system that records, stores and manages vast quantities of time-series data generated by industrial processes. These systems play a pivotal role in industries like manufacturing, energy, oil and gas and mineral processing. Data historians are designed to capture data at high speeds, with precise timestamps, and often with redundant storage to ensure data integrity.
The importance of data historians
Data historians are the unsung heroes of industrial process industries, enabling real-time analysis, historical data retrieval, regulatory compliance, and operational optimisation. They serve as the foundation for data-driven decision-making in the era of Industry 4.0, playing a pivotal role in ensuring businesses can thrive in a data-rich environment.
- Real-time data analysis: Data historians collect and store real-time data, allowing for immediate analysis and decision-making. This is crucial when swift responses are necessary to prevent operational disruptions or safety hazards.
- Historical data retrieval: Data historians store historical data, enabling users to retrieve past process data for troubleshooting, optimisation, and compliance audits. This historical perspective is essential for identifying trends, patterns, and anomalies.
- Regulatory compliance: Industries such as mining and mineral processing have strict data retention and reporting regulatory requirements. Data historians help meet these compliance standards by providing accurate, traceable, and auditable data.
- Operational optimisation: Data historians facilitate predictive maintenance, performance optimisation, and energy efficiency improvements. They provide valuable insights into equipment performance and overall plant operations.
- Data-driven decision-making: In the age of Industry 4.0, data historians are at the core of data-driven decision-making. They provide the necessary data infrastructure for implementing machine learning and AI algorithms for predictive analytics.
Data historian vs. SQL database
While data historians and SQL databases are both designed to store data, they serve different purposes and exhibit distinct characteristics.
- Data structure: Data historians are optimised for time-series data, making them highly efficient for storing and retrieving data points with timestamps. In contrast, SQL databases are more flexible and suitable for relational data.
- Data retrieval speed: Data historians excel at rapid data retrieval due to their specialised storage and indexing mechanisms. SQL databases are generally not as quick in retrieving time-series data.
- Redundancy: Data historians often employ redundancy in storage to ensure data integrity, making them highly reliable for critical operations. SQL databases may offer redundancy options, but they are less tailored for this purpose.
- Scalability: Data historians are designed for scalability and can handle vast amounts of historical data. SQL databases may require significant optimisation to manage the same volume of time-series data.
- Data compression: Data historians often use data compression techniques to store large datasets efficiently. SQL databases tend to store data in its raw form, which can lead to higher storage requirements.
Types of data historians
There are various types of data historians, each with its unique features and components. The choice of historian depends on the industry’s specific needs and the data collection scale. Some of the prominent types include:
- Proprietary data historians: Developed by companies specialising in industrial automation and control, these historians are tightly integrated with their hardware and software systems. Examples include AVEVA’s PI System and Honeywell’s Uniformance.
- Open source historians: Open-source historians, such as InfluxDB and OpenHistorian, provide flexibility and cost-effectiveness. They can be customised to meet specific industry requirements.
- Cloud-based historians: Cloud-based historians, like Azure Time Series Insights and AWS IoT SiteWise, offer the advantage of scalability, accessibility, and seamless integration with cloud services.
- Distributed data historians: These historians are designed to operate across multiple locations or sites. They provide centralised data storage and monitoring for large-scale enterprises.
- Edge data historians: Edge historians, like Canary Labs’ Canary Historian, are optimised for edge computing, allowing for data collection and processing at the source, reducing latency.
Effective implementation of data historians
Implementing a data historian effectively is crucial to maximising its benefits. Here are key steps to ensure a successful deployment:
Define objectives
Clearly define your goals and objectives for implementing a data historian. Understand what data you need to collect, why you need it, and how it will be used.
Data source identification
Identify the data sources within your industrial process. This includes sensors, PLCs (Programmable Logic Controllers), SCADA (Supervisory Control and Data Acquisition) systems, and other data-generating devices.
Select the right historian
Choose the type of data historian that aligns with your industry’s needs. Consider scalability, data retention requirements, and integration with existing systems.
Data tagging and naming conventions
Develop a standardised data tagging and naming convention to ensure consistency in data collection. This makes it easier to search and retrieve data.
Data quality assurance
Implement data quality assurance measures to minimise errors and inconsistencies in data collection. This includes data validation, cleansing, and redundancy checks.
Scalability planning
Plan for the future by designing your historian architecture for scalability. As data volume grows, the historian should seamlessly expand to accommodate it.
Security measures
Ensure data security and access controls to protect sensitive information. Implement encryption, role-based access, and data audit trails to maintain data integrity.
User training
Provide adequate training for users to maximise the benefits of the historian. Users should know how to retrieve and analyse data effectively.
6 benefits of running a data historian
The operation of a data historian yields numerous benefits, contributing to the overall efficiency and success of industrial process industries. Here are some key advantages:
- Data-driven decision-making: Historians provide real-time and historical data, enabling informed decisions for process optimisation, maintenance scheduling, and resource allocation.
- Improved product quality: Accurate and historical data helps maintain product quality standards by identifying deviations and allowing for quick corrective actions.
- Predictive maintenance: Data historians support predictive maintenance by monitoring equipment performance and identifying issues before they lead to costly breakdowns.
- Energy efficiency: Tracking energy consumption and optimising processes based on historical data can result in significant energy savings and reduced operational costs.
- Compliance and Reporting: Historians simplify the process of complying with industry regulations by providing reliable data for audits and reporting.
- Reduced downtime: By enabling early fault detection and efficient troubleshooting, historians help minimise unplanned downtime and production losses.
The current data historian market
The data historian market is vibrant, with numerous players offering a wide range of solutions. According to a report by MarketsandMarkets, the global data historian market size is expected to grow from $1.1 billion in 2020 to $1.3 billion by 2025 at a Compound Annual Growth Rate (CAGR) of 5.0% during this period. Let’s explore some of the key players in the market:
AVEVA
AVEVA’s PI System (previously OSISoft) is one of the most well-known data historian solutions. It is an industry-leading data management solution built specifically to overcome the challenges of industrial environments. With the AVEVA PI System, you can collect and store data from any location and source and rapidly extract the insights you need to optimise your business
AVEVA also now markets Wonderware as AVEVA Historian. It is a Process database integrated with operations control, enabling access to your process, alarm, and event history data.
Siemens
SIMATIC PCS 7 Process Historian archives process data, tags, alarms and batch data from SIMATIC BATCH centrally and in real time. Due to the openness of the system, data can be read and written via standard database interfaces such as ODBC, OLE DB and ADO.NET. The Information Server is available for the visualization of information from the database. The reporting system, which works based on Microsoft Reporting Services, ensures the simple, individual and target group-specific creation of reports.
Honeywell
Honeywell offers the Uniformance Suite, which includes data historian solutions designed for process industries. It focuses on data analysis and predictive maintenance.
GE
Proficy Historian is a top-of-the-line historian software solution that collects industrial time-series data and A&E data at a high speed, stores it securely and efficiently, distributes it, and allows fast retrieval and analysis.
Rockwell Automation
Rockwell Automation’s FactoryTalk Historian is designed to capture and analyse process data for manufacturing industries. While it is a direct competitor to PI System, some of the restrictions in tagging limit its ability to be vendor agnostic in its data collection.
InfluxData
InfluxDB is an open-source time-series database that has gained popularity in IoT and DevOps applications. It’s known for its scalability and ease of integration and can run any environment in the cloud, on-premises, or at the edge.
Factry
Factry Historian is a powerful and easy-to-use data management platform for collecting, storing and visualising industrial process data. Our historian software will enable your business to transform raw production data into actionable visual insights, drastically reduce downtime, avoid costs and drive a better overall plant performance
Ignition
Ignition’s data historian functionality is a robust set of features that are built into modules, providing data acquisition, storage, retrieval, and visualization. Several modules can be used together to provide a complete system. Often, users combine Ignition’s data historian functionality with SCADA, HMI, IIoT, ERP, or MES functionality.
Canary Labs
The Canary Historian is an example of an edge data historian solution tailored for efficient data collection and analysis at the edge of industrial networks.
Other players include:
Microsoft Azure Time Series Insights: This cloud-based solution by Microsoft offers a highly scalable platform for managing time-series data in IoT and industrial applications.
Amazon AWS IoT SiteWise: AWS IoT SiteWise provides a managed service for collecting, storing, and analysing data from industrial equipment at scale.
All these providers cater to various industries and offer different features and capabilities. The choice of a data historian depends on an industrial operation’s specific needs and goals.
The future of data historians
The future of data historians in industrial process industries is promising. Several trends and developments are shaping the industry’s trajectory:
Integration with IoT
The Internet of Things (IoT) is increasingly integrating with data historians. This integration allows for more comprehensive data collection and analysis, enhancing operational efficiency and predictive analytics.
Machine Learning and AI
Data historians will play a vital role in implementing machine learning and artificial intelligence (AI) algorithms for predictive analytics. This means that data historians will not only store and retrieve data but also analyse it in real-time, providing actionable insights for process optimisation and efficiency improvement.
Cloud-based solutions
The adoption of cloud-based data historians continues to rise. These solutions offer scalability, reduced infrastructure costs, and easy access to data from anywhere. Many industrial organisations are transitioning from on-premises data historians to cloud-based platforms.
Edge computing
Edge data historians, optimised for processing data at the source, are gaining popularity. This is particularly beneficial for applications where real-time data analysis is crucial, such as in manufacturing, energy, and remote industrial sites.
Cybersecurity
As data historians store critical operational data, ensuring cybersecurity is paramount. The future will witness increased emphasis on securing data historians from cyber threats, including ransomware and data breaches.
Interoperability
The ability of data historians to seamlessly integrate with other industrial systems, such as SCADA and MES (Manufacturing Execution Systems), will become a fundamental requirement. This interoperability allows for a holistic view of industrial processes.
AI-Driven anomaly detection
Data historians will increasingly incorporate AI algorithms for anomaly detection. These algorithms will automatically identify deviations from expected patterns, allowing quick response to abnormalities.
Enhanced user experience
User-friendly interfaces and advanced data visualisation tools will be crucial. These improvements will empower users to extract valuable insights from data historians without requiring extensive technical expertise.
Evolving data storage technologies
As data volumes grow, data historians may adopt new storage technologies like distributed databases, in-memory databases, and data lakes to handle the immense data flow efficiently.
Summing it up: Data historians and the future of data-driven operations
The operation of data historians delivers numerous benefits, including data-driven decision-making, improved product quality, predictive maintenance, energy efficiency, compliance, and reduced downtime. As data historians continue to evolve, their integration with IoT, AI-driven analytics, and enhanced cybersecurity will play pivotal roles in shaping the future of these systems.
The current data historian market is dynamic, with several key players providing various solutions. As the demand for data historians grows, these players continually improve their offerings to meet the evolving needs of industrial process industries. The future of data historians holds promise as they become increasingly integrated, secure, and capable of providing real-time actionable insights.
In an era where data is king, data historians will remain a cornerstone of industrial process industries, driving efficiency, productivity, and innovation for years.
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