The speedy progress of the world’s industrial output has inspired ongoing advancements in operational technology. Digital transformation and Industry 4.0. are accelerating exponential demands for Industrial systems like DCS, SCADA, RTU’s, PLC’s, sensors, edge devices, data historian, industrial robots, 3D printers. All these systems are now generating unprecedented and escalating production volumes, velocities, and efficiencies. The data captured by these systems need to be properly managed, cleaned, processed, stored, routed, secured, and leveraged, and so on...
In the past, data historians, which are time-series databases, were located on the premise, next to the industrial system. They captured and stored all the sensor data. However, to take advantage of artificial intelligence and big data analytics applications, which are mostly available in the cloud environments, the data now needs to be moved, stored, and searchable in a cloud-based database.
The whole IIoT evolution is not new to the market. The concept of IIoT has been around in the industry for many years, however, the demand for the convergence of IT and OT is getting louder and louder. The time-series data historian plays a major role here in the context of IIoT. Industrial time series data will give way to complex adaptive systems and multi-processing. The future belongs to nanotech, cloud computing, wireless everything, artificial intelligence (AI)-based machine learning (ML), Big Data, and complex adaptive systems.
Time-series data is the data that changes with time (e.g., digital sensor readings). A time-series database keeps data values and timestamps which were collected over time with the unique ability to consistently store (ingest) large amount of data that is coming in with time. Time-Series data and related technologies are the fasted growing segment in the market. As a result, we have seen lots of investments and acquisitions recently e.g. AVEVA Acquire OSIsoft in $5 Billion Deal on August 25, 2020. Industrial time-series data has gravity and researchers anticipate it to grow with a healthy growth rate of more than 6.90% over the forecast period 2020-2025. Leading public and private cloud platforms, software startups, data lake vendors, control systems, SCADA companies, top tier visionary investors and venture capital firms are all rushing to be the vendor/partner/investor of record for this time-series data storage business of the world. In the coming year's competition will fuels more innovation and will grows Time-Series data historian market as a whole.
The Data historian has evolved from being just a place for storing data to becoming a data infrastructure. This means data collection or storage or visualization by themselves alone or even together doesn’t make a complete industrial data management system valuable. Industry4.0 evolution needs more with a complete infrastructure solution with the capability of integration, archiving, asset modeling, notifications, visualizing, analysis, and many more analyzing features. Data historian, MES, and ERP all might become part of DataLake (everything stored) which we can call the unified namespace, however, DataLake will still not be able to supply data at the right time and with the right context for time-stamped process data with proper data integrity.
The future of data historian is much more than a traditional operational data historian. The data historian capability is just a sub-function. A better name would be an “Industrial Data Management System” or something along those lines. Operational data historians are expensive, challenging to work with, and typically behind the times with limited analysis and visualization capability. Not all data historians are horizontally scalable and during a large amount of archive, data retrieval needs to face a performance penalty. It is also very difficult to contextualize sensors data to other metadata for data historians which is very lengthy and costly for customers to work with. Operational data historian provides the benefits of interfacing that sit with data collection points together with buffer capability (store and forward) with industrial system compatibility such as DCS, SCADA, OPC, etc.
Key technologies for the future of the Industrial Data Management System:
And last but not least to have a solution for an extremely important challenge to solve human talent availability issues to help customers. There are multiple players in this race but still, no one has a complete solution for the future of the Industrial Data Management System.
Major market players in 2020 for time series data management market:
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