Traditional process data historians have been in use now for decades – before the time of the internet of things! Originally designed to collect data from industrial sites they formed a representation of the ‘process’ undertaken on the site, keeping a time-series record in a database locally (sometimes referred to as a site historian).
Eventually businesses became networked, and data was shared with head-office (aka the rise of the enterprise historian). Today industrial businesses still need to retain a record of their ever-increasing volume of data, and thanks to modern technology this data can be viewed and analysed in real-time.
Most time-series database solutions are now accessible on the cloud, however not all solutions are equal. It’s important to understand the design limitations and opportunities so you can make an informed decision when choosing how to store time-series data. After all, history shows us that once a customer stores their data in one solution, they are some-what tied to that solution for an extended period of time – flaws and all.
HOW DOES A TRADITIONAL PROCESS HISTORIAN FUNCTION?
- System of record that allows you to access, store and share real time data within its ecosystem without duplicating the data
- Display real-time data streams for live monitoring
- Can be used to identify anomalies and send alerts (condition monitoring)
- Allows you to contextualise data stored
- Traditional historians can integrate with other 3rd party business systems, software and apps, which extract the data to use for their own purposes, such as analytics.
- Not designed to support exponential increase in data, such as millions of tags
- Traditional historians may have limited capability in terms of speed of data storage, storage efficiency, speed of data retrieval and access methods can vary by product
- The customer is often charged for each person that uses the system, along with individual data inputs or additional features required (such as visualisations)
- Access to data for analytics can be difficult and time consuming, with many stakeholders owning their individual data (data silos)
- No inbuilt AI or machine learning analytics, data can be sent to a 3rd party systems for manipulation
HOW DOES DATAHUB+ FUNCTION?
- DataHUB+ is system agnostic, it can ingest real-time data from any source, including historians, SCADA systems, as well as existing equipment sensors, process information, lab data and CMMS
- As an enterprise distributed historian, DataHUB+ is a scalable reliable solution, not reliant on costly infrastructure. This allows businesses to store all their data, without having to choose to delete historic data or exclude tags
- With a microservices approach to storage, data can be accessed at lightning speed, with speeds able to be scaled up and down based on a customer’s requirements
- Data is always owned by the customer and can be extracted or integrated with other apps or platforms based on a customer’s requirements
- DataHUB+ automatically checks the data’s integrity, sending alerts if there are data gaps or errors which need attention. This ensures that data analysis is highly accurate, as the data source is reliable
- Customers can visualise data within the platform, using the simple drag and drop functionality to build dashboards to monitor their operations or analyse aspects of their data
- As an enterprise solution, it allows multiple sites or plants to have their data ingested into the single platform, which means that analytics can be conducted at an enterprise level, across all departments and operations, eliminating data silos.
- DataHUB+ automatically pre-processes the data, eliminating data wrangling, freeing up valuable resources
- It is built for ease of access, allowing multiple stakeholders throughout the organisation to access and utilise the data without duplicating it or jeopardising its integrity. A key to allowing organisations to scale data analytics
- When combined with OPUS end users (process engineers and subject matter experts) can use advanced analytics tool and build no-code AI models to predict future outcomes and forecast future events, which lead to process optimization.
When looking for a time-series process historian it’s important to consider cost, speed and reliability as well as future business requirements for in-house advanced analytics and AI. As computing power and technology have advanced, many traditional process historians are limited by complex hardware infrastructure, which limits storage, capacity and speed.
Ready to discover the power of DataHUB+, get in touch for a demo.
