What is a Data Historian and Is It Still Relevant in 2025?

What is a Data Historian?

A Data Historian is software specifically designed for capturing and storing time series data from industrial operations. This includes sequential measurements from the same source over defined periods—like the temperature of machinery or pressure levels in pipelines, recorded at high frequencies.

These systems are integral to the Industrial IoT ecosystem, handling continuous data streams from numerous devices, enabling real-time process tracking, monitoring, and analysis. The information collected is crucial for enhancing process efficiency, predicting equipment failures, ensuring energy optimization, and maintaining compliance with regulatory standards.

Data historian use cases

Manufacturing

In a manufacturing setting, data historians can monitor machine efficiency and productivity, allowing for identification of bottlenecks and inefficiencies. Predictive maintenance use cases can foresee potential machine breakdowns, minimizing downtime.

Oil and gas

Data historians help track the performance of drilling rigs, pipelines, and other equipment. Predictive analysis can identify potential issues, while historical data can assist in decision-making processes and improve safety measures.

Transportation and logistics

Historians can track and analyze data from various sensors in vehicles and equipment, improving maintenance scheduling, fuel efficiency, and overall fleet management.

Utilities

Data historians track and analyze energy consumption data, helping identify inefficiencies and opportunities for cost savings. Additionally, they can monitor equipment status in real-time, predict potential outages, and enable rapid response.

The shift in data management needs

As the overwhelming flow of new data accelerates, the limitations of traditional data historians are becoming more apparent. They often operate in isolation, struggle with scalability issues, are costly, and lack the agility to integrate with modern data analytics tools. In 2025, organizations require not only to store and retrieve data but also to analyze and act upon it in real-time to drive decisions that enhance operational efficiency and competitive advantage.

Benefits of traditional data historians

Traditional data historians are essential in industrial settings. The mission-critical nature of the tool is why organizations have agreed to their high price in the past. Traditional historians are tailored for high-stakes environments to deliver:

Reliability and stability

Designed for continuous, long-term operation, traditional data historians provide dependable performance crucial for industries where downtime can lead to significant disruptions and losses.

Data management

These systems excel in storing and retrieving large volumes of time series data.

Seamless integration

Data historians integrate smoothly with industrial control systems, allowing for direct data collection from various sources such as PLCs, SCADA systems, and DCS, maintaining unified data oversight.

Compliance and security

With stringent compliance requirements in many sectors, traditional data historians offer secure data management, comprehensive audit trails, and detailed logging to ensure regulatory adherence.

Predictive maintenance

They support predictive maintenance by analyzing historical data to foresee and prevent potential equipment failures, thereby reducing downtime and extending machinery lifespan.

Downsides of traditional data historians

As industries evolve and data demands increase, traditional data historians can sometimes have trouble keeping up. Let’s look at why these older systems might not be the best choice anymore, especially as companies need faster and smarter tools like InfluxDB.

Scalability issues

Traditional data historians were designed for a certain scale of operations, which can make scaling up challenging as a business grows and data volume increases. This is akin to trying to fit more content onto a single page when what you really need is to add more pages to your notebook. As data inflow expands, these systems can become overloaded, leading to increased costs and complexity in infrastructure expansion.

Integration challenges with new technologies

While these historians are well-adapted to older industrial systems, they often lag in integrating seamlessly with newer technologies. This can be problematic as businesses strive to adopt advanced analytics tools and IoT technologies. It’s like trying to play a new video game on an old computer; the system may not have the necessary capabilities to support the latest software efficiently.

High cost of ownership

Maintaining traditional data historians involves significant investment in specialized hardware and ongoing maintenance. As the volume and velocity of data increase, the infrastructure required to support these systems grows, which can escalate costs quickly. It’s similar to maintaining an old car that needs increasingly expensive repairs instead of switching to a more efficient, newer model.

Limited flexibility

Customizing traditional data historians to meet specific business needs can be cumbersome and resource-intensive. These systems often require expert intervention to modify, making them less adaptable to rapidly changing business environments. It’s like having a rigid, fixed menu at a restaurant when diners are looking for customizable meal options.

Delayed insights

The architecture of traditional data historians typically does not support real-time data processing, which can delay the generation of insights. In industries where timely information is critical, this delay can be a significant handicap, akin to receiving a weather report long after a storm has passed.

Data silos

Often, traditional data historians create isolated pools of data that are not easily accessible across different departments or for integrated analytics. This can inhibit collaborative data usage and holistic analysis, much like having team members working in separate rooms without the ability to communicate effectively.

Is the traditional data historian still relevant?

While traditional data historians still have a role in specific industries and use cases, their relevance in the face of emerging technologies like InfluxDB is diminishing. For businesses looking towards future-proofing their operations, adopting a more flexible, scalable, and integrated system like InfluxDB offers a clear advantage.

Modernizing your data historian with InfluxDB

InfluxDB is a time series database built to address the shortcomings of traditional data historians.

InfluxDB: Why is it a powerful, modern alternative?

Many companies across different industries are using InfluxDB as their data historian. As a time series database, InfluxDB provides the performance required in addition to scalability and data compression. InfluxDB excels in areas where conventional historians lag, particularly in its ability to scale dynamically, process data in real-time, and integrate seamlessly with other tools in the analytics stack. This makes InfluxDB not just an alternative but a superior choice for modern data workloads.

• Enhanced scalability and flexibility: Unlike traditional data historians that often struggle with large-scale data environments, InfluxDB is built to scale effortlessly with the needs of modern enterprises. It can handle millions of data points per second, supporting high-availability setups without compromising on performance.
• Real-time queries that scale: InfluxDB provides capabilities beyond mere data storage. It supports high-speed ingest and real-time queries for recent data, which is essential for organizations that rely on immediate insights to optimize operations and respond to market changes swiftly. Compared with InfluxDB 1.x and 2.x OSS, InfluxDB 3 delivers 50-400% faster query performance for high-cardinality data.
• Cost-effective implementation: Transitioning to InfluxDB from a traditional data historian can significantly reduce costs. Terega implemented InfluxDB to lower total cost of ownership (TCO) by 50%.
• Easy integration: InfluxData also offers Telegraf, our popular open source connector with 5B+ downloads, which makes integrating your systems with InfluxDB easy. Telegraf has over 300 different plugins which allow you to connect to different devices or protocols and forward the data to InfluxDB or other data stores. Telegraf also has the ability to transform or filter data before sending it to storage.

The future of data management

While traditional data historians have served industries well in the past, the dynamic nature of today’s business and technological landscapes calls for more advanced solutions. InfluxDB represents the next step in the evolution of data management, offering enhancements that are not just improvements but essential for staying relevant in a data-centric world. As we move into 2026 and beyond, embracing these modern solutions will be key to unlocking new levels of operational intelligence and efficiency.

FAQs

What are the requirements for process manufacturing?

Process manufacturing refers to the production of goods that are produced in large quantities by following a specific formula or recipe. Requirements for process manufacturing include accurate measurement and monitoring of production data. The right data historian should, therefore, be able to capture and store plant data in real-time, support OPC interfaces, and provide advanced features such as data compression and retrieval.

What is an enterprise data historian?

An enterprise data historian is a data historian that focuses on providing data management and data analysis across different levels of an organization. It enables managers and other stakeholders to access data from multiple sources in real-time and make informed decisions based on accurate and reliable data.

What is the difference between a data historian and a time series database?

A data historian is a type of database that is specifically designed to store and manage time-stamped data from industrial and process manufacturing environments. A time series database, on the other hand, is designed to store time-stamped data from a wide variety of sources, including Internet of Things (IoT) sensors, financial markets, and more. While the two technologies share some similarities, data historians typically have features that are tailored for industrial data and process manufacturing.

What is the purpose of a database in the industrial process?

Databases in industrial processes are used to store and manage data that is generated by equipment, sensors, and other sources. The data stored in these databases is used to monitor the performance of industrial processes, identify potential issues or bottlenecks, and optimize processes for maximum efficiency. Industrial databases allow manufacturing and process engineers to gather detailed data about their operations, helping them to improve safety, quality, and productivity.

What is an operational historian?

An operational historian is a type of database that is used to store and manage real-time data from industrial processes as it is generated. Operational historians are designed for use in applications where fast access to data is critical, such as in control systems and other real-time applications.

What is the difference between a data historian and a traditional relational database?

Data historians are designed specifically for time-stamped and event-based data, while relational databases are better suited for structured data such as that found in financial applications or CRM software. Data historians provide additional features such as advanced data storage and compression optimized for time series data, support for analog data, and visual presentations of the data, among others.

What types of data are stored in a data historian?

Data historians are most commonly used to store time-series data, such as data that changes over time and is generated by industrial or manufacturing processes. This data can include analog data, digital data, and event data. Historians are used to store data that is generated by machines and processes, such as temperature readings, process variables, and other metrics.

How is data generated by equipment and sensors collected and stored in a historian?

Data from equipment and sensors is typically collected and sent to the historian using a variety of protocols and methods, such as OPC UA, Modbus, or a proprietary protocol. Once the data is received, the historian stores it in a raw format, either in memory or on disk. Data is then compressed and archived to save space and improve retrieval times. Data management software is typically used to interact with the historian, allowing users to retrieve and analyze data directly.