Both established operational data historians and newer open-source platforms continue to evolve and add new value to business, but the significant domain expertise now embedded within data historian platforms should not be overlooked.
Time-series databases specialize in collecting, contextualizing, and making sensor-based data available. In general, two classes of time-series databases have emerged: well-established operational data infrastructures (operational, or data historians), and newer open source time-series databases.
Enterprise data historian functionality at a fraction of the cost. Industrial time series data collection & analytics tools.
Data Historian vs. Time Series Database
Functionally, at a high level, both classes of time-series databases perform the same task of capturing and serving up machine and operational data. The differences revolve around types of data, features, capabilities, and relative ease of use.
Benefits of a Data Historian
Most established data historian solutions can be integrated into operations relatively quickly. The industrial world’s versions of commercial off-the-shelf (COTS) software, such as established data historian platforms, are designed to make it easier to access, store, and share real-time operational data securely within a company or across an ecosystem.
While, in the past, industrial data was primarily consumed by engineers and maintenance crews, this data is increasingly being used by IT due to companies accelerating their IT/OT convergence initiatives, as well as financial departments, insurance companies, downstream and upstream suppliers, equipment providers selling add-on monitoring services, and others. While the associated security mechanisms were already relatively sophisticated, they are evolving to become even more secure.
Another major strength of established data historians is that they were purpose-built and have evolved to be able to efficiently store and manage time-series data from industrial operations. As a result, they are better equipped to optimize production, reduce energy consumption, implement predictive maintenance strategies to prevent unscheduled downtime, and enhance safety. The shift from using the term “data historian” to “data infrastructure” is intended to convey the value of compatibility and ease-of-use.
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What about Time Series Databases?
In contrast, flexibility and a lower upfront purchase cost are the strong suits for the newer open source products. Not surprisingly, these newer tools were initially adopted by financial companies (which often have sophisticated in-house development teams) or for specific projects where scalability, ease-of-use, and the ability to handle real-time data are not as critical.
Since these new systems were somewhat less proven in terms of performance, security, and applications, users were likely to experiment with them for tasks in which safety, lost production, or quality are less critical.
While some of the newer open source time series databases are starting to build the kind of data management capabilities already typically available in a mature operational historian, they are not likely to completely replace operational data infrastructures in the foreseeable future.
Industrial organizations should use caution before leaping into newer open source technologies. They should carefully evaluate the potential consequences in terms of development time for applications, security, costs to maintain and update, and their ability to align, integrate or co-exist with other technologies. It is important to understand operational processes and the domain expertise and applications that are already built-into an established operational data infrastructure.
Why use a Data Historian?
When choosing between data historians and open source time-series databases, many issues need to be considered and carefully evaluated within a company’s overall digital transformation process. These include type of data, speed of data, industry- and application-specific requirements, legacy systems, and potential compatibility with newly emerging technologies.
According to the process industry consulting organization ARC Advisory Group, modern data historians and data infrastructures will be key enablers for the digital transformation of industry. Industrial organizations should give serious consideration when investing in modern operational historians and data platforms designed for industrial processes.
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11 Things to Consider When Selecting a Data Historian for Manufacturing Operations:
1. Data Quality
The ability to ingest, cleanse, and validate data. For example, are you really obtaining an average, such as if someone calibrates a sensor, will the average include the calibration data? If an operator or maintenance worker puts a controller in manual, has an instrument that failed, or is overriding alarms, does the historian or database still record the data? Will the average include the manual calibration setpoint?
2. Contextualized Data
When dealing with asset and process models based on years of experience integrating, storing, and accessing industrial process data and its metadata, it’s important to be able to contextualize data easily. A key attribute is the ability to combine different data types and different data sources. Can the historian combine data from spreadsheets and different databases or data sources, precisely synchronize time stamps and be able to make sense of it?
3. High Frequency/High Volume Data
It’s also important to be able to manage high-frequency, high-volume data based on the process requirements, and expand and scale as needed. Increasingly, this includes edge and cloud capabilities.
4. Real-Time Accessibility
Data must be accessible in real time so the information can be used immediately to run the process better or can be used to prevent abnormal behavior. This alone can bring enormous insights and value to organizations?
5. Data Compression
Deep compression based on specialized algorithms that compress data, but enables users to reproduce a trend, if needed.
6. Sequence of Events
SOE capability enables user to reproduce precisely what happened in operations or a production process.
7. Statistical Analytics
Built in analytics capabilities for statistical spreadsheet-like calculations to perform more complex regression analysis. Additionally, time series systems should be able to stream data to third party applications for advanced analytics, machine learning (ML) or artificial intelligence (AI).
The ability to easily design and customize digital dashboards with situational awareness that enable workers to easily visualize and understand what is going on.
Ability to connect to data sources from operational and plant equipment, instruments, etc. While often time-consuming to build, special connectors can help. OPC is a good standard but may not work for all applications.
10. Time Stamp Synchronization
Ability to synchronize time stamps based on the time the instrument is read wherever the data is stored – on-premises, in the cloud, etc. These time stamps align with the data and metadata associated with the application.
11. Partner Ecosphere
Can make it easy to layer purpose-built vertical applications onto the infrastructure for added value.
Rather than compete head on, it’s likely that the established historian/data infrastructures and open-source time-series databases will continue to co-exist in the coming years. As the open-source time series database companies progressively add distinguishing features to their products over time, it will be interesting to observe whether they lose some of their open-source characteristics. To a certain extent, we previously saw this dynamic play out in the Linux world.
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