Operational Historians/Data Platforms Selection

This Selection Guide includes extensive selection criteria and strategies to help in choosing the Operational Historian/Industrial Data Platform technology and supplier that best meets your specific current and future needs.

Operational Historians and Industrial Data Platforms Are Revolutionizing Digital Transformation Across the Enterprise 

​Eliminating data silos and integrating data sources and databases seamlessly across the enterprise using newer Operational Historians and Industrial Data Platforms are key to collaboration, performance improvements, and optimizing value. Today, this is particularly important as some databases are being deployed to different environments with both real time tag data and other types of data. Easily integrating and connecting to data sources allows quick access to information, enabling data-driven insights. This highlights the importance of integrating IT and OT data, along with the use of newer data management tools for process optimization.
    
Process complexity and digital transformation have always made process data storage and management complex. We live in a world where more devices and things are connected, and this requires interoperability, accessibility, searchability and ease of use. The digital transformation and Industry 4.0 are all about data; how quickly one can get the data, how quickly one can access and understand the data in context and get the data to the right user no matter where they are in the world, and how quickly and securely one can take actionable insights. Speed, performance, flexibility, cybersecurity, accessibility, and context are important because users need to be able to understand what is happening based on enormous amounts of data and react quickly, no matter where it is located – on-premise, cloud or edge.

AI, new Industrial Enterprise Historians and new Industrial Data Platforms are a game changer for digital transformation by connecting the data and enabling an organization’s digital transformation. The software sets a foundation for workers to be able to use data more effectively. The technology is helping to drive digital transformation and a cultural shift to a more end-to-end data-based culture.

It is also important for companies to make this data more accessible using intuitive dashboards or reports that enhance the user experience and enable remote workers to make sense of the data and make operational data-driven decisions faster. Companies should strive to create a data mindset as well as the ability to collaborate throughout the organization so that workers can make well-reasoned, fact-based decisions that embraces information that is already available in the data they are collecting.

Operational Historian/Industrial Data Platform Selection Strategic Issues

Eliminate Data Silos for Collaboration and Optimization

Eliminating data silos and integrating data sources and databases across the enterprise can enhance collaboration, performance, and value optimization. This is especially relevant as some databases and data sources are deployed on different infrastructures. The ability to integrate and connect to data sources easily for quick information access can reduce deployment costs, highlighting the importance of data connectors, integration standards, and data management tools for success.

Use Solutions that Simplify Complex Processes and Elevate the Worker’s Skillset

Implementing solutions that are straightforward to deploy, simplified, intuitive, and visual—easy to learn and use—encourages users to collaborate more frequently and effectively utilize the tools. When employees can comprehend data and make data-driven decisions swiftly, they are more likely to consistently engage with the tools. These tools can also be leveraged to enhance the employees‘ skillsets.

Consider Hyperscalers for Storage Infrastructure

Hyperscalers use large, distributed infrastructure that can quickly accommodate an increased demand for computing resources without requiring additional physical space, cooling, or electrical power. Hyperscale computing is characterized by standardization, automation, redundancy, high-performance computing (HPC) and high availability (HA). To accommodate such demand, cloud providers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) have developed new infrastructures that maximize hardware density, while minimizing the cost of cooling and administrative overhead.

Hyperscalers can accommodate data volume fluctuations with scalable server infrastructures. The term “hyperscale” refers to scalable cloud computing systems in which many servers are networked together. The number of servers used at any time can increase or decrease to respond to changing requirements. This means the network can efficiently manage both large and small volumes of data traffic. Hyperscale servers are small systems that are purpose built. To achieve scalability, the servers should be networked together “horizontally” or scaled out to increase the computing power of the system. By having a system with a load balancer, the load can be monitored and balanced, enabling scalability. Most servers in data centers will consist of these scalable, cost-effective systems.