How to Choose the Best Data Historian

Jeff Tao
Jeff Tao
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With the progress of digital transformation across industries, data is becoming an increasingly important asset for enterprises. To benefit from the advantages offered by digitalization, it is necessary to have a foundation of operational data collected from all sites at an enterprise. However, the scale of this data can quickly become difficult to manage — devices are constantly generating time-series data that needs to be centralized and stored.

In the past, simply purchasing any well-supported historian solution from an established systems provider may have been sufficient for pre-IIoT data operations. However, with the advent of Industry 4.0, traditional solutions are not necessarily suited for the new scenarios that industrial enterprises are facing. With this in mind, it has become essential to consider a number of criteria when choosing the best data historian for your use case.

Compatibility and Integration

Being responsible for data collection across devices and sites, your data historian needs to support your existing infrastructure, including your SCADA, PLCs, and HMI systems, in addition to all of the industrial data protocols that you are running, such as OPC or MQTT. But integration with equipment at your sites is only the beginning — looking forward, it’s also critical that your data historian can integrate with the visualization and analytics tools that your teams want to use, and also with systems that may be located at other sites or in the cloud. Therefore a future-ready data historian supports not only the industrial protocols needed for existing sites, but also provides standard interfaces to work with business intelligence and other tools.

Data Storage and Retrieval

Considering that your data historian is the source of truth for data in your environment, its ability to store data efficiently and securely is paramount. High performance ingestion is critical to processing the massive scale of data coming in from your devices, and not every system is built to handle the speed at which data points arrive — new records every 15 seconds is no longer a fast pace when compared with IIoT devices. After the data is ingested, the capabilities related to storing data are also important factors to consider: in particular, efficient compression algorithms are needed to reduce storage costs, encryption is essential for data security and confidentiality, and high availability through clustering or replication is a must for ensuring that critical data is never lost or inaccessible.

For modern analytics and data science applications, being able to query data quickly is also a major benefit. Apart from query latency itself, integration must be possible with third-party applications and algorithms such that manual transmission of data is avoided and systems can work together on the latest data to provide the most up-to-date insights into business operations.

User-Friendliness and Pricing

It’s important to keep in mind that many end users of data historians are not IT engineers, but frontline personnel and data scientists. For that reason, ease of use is extremely important — if your key users cannot work with your systems, their effectiveness will greatly decrease and they will be reluctant to get on board with digital transformation. Standard query languages like SQL and user-friendly interfaces are therefore a critical element of modern data historians.

Finally, as IIoT datasets grow exponentially in scale, a modern pricing model that adapts to the size of your data is a hard requirement for deploying a new data historian. Legacy systems that charge based on number of tags will quickly incur astronomical costs if implemented directly in an IIoT environment — a fact that vendors may only reveal after licenses have been created and contracts signed.

Conclusion

The best data historian may be different for each enterprise or even for each site, but there are criteria that can be used to help choose the most suitable option for a particular use case or situation. When selecting products for your data infrastructure, remember that you need to support not only your business today, but also your future business needs as your enterprise continues to grow and technology continues to develop. All in all, a user-friendly data historian with a predictable and affordable pricing scheme that is highly compatible with data protocols and third-party applications and offers high performance ingestion and querying is an ideal choice for most traditional industries.

  • Jeff Tao
    Jeff Tao

    Jeff Tao is the founder and CEO of TDengine. He has a background as a technologist and serial entrepreneur, having previously conducted research and development on mobile Internet at Motorola and 3Com and established two successful tech startups. Foreseeing the explosive growth of time-series data generated by machines and sensors now taking place, he founded TDengine in May 2017 to develop a next generation data historian purpose-built for modern IoT and IIoT businesses.