What are the best ways to use cloud-based tools for data visualization in distributed control systems?

1 Cloud-based tools for data visualization

Cloud-based tools are web applications that run on remote servers and can be accessed through a browser or a mobile device. They allow you to upload, store, process, and visualize data from various sources, such as sensors, controllers, databases, or APIs. Some examples of cloud-based tools for data visualization are Google Data Studio, Microsoft Power BI, Tableau Online, and Plotly Dash. These tools offer different features, such as drag-and-drop interfaces, pre-built templates, customizability, collaboration, and sharing options.

3 Challenges of cloud-based tools for data visualization

However, cloud-based tools for data visualization also pose some challenges, especially for control engineering applications. One of the main challenges is security. You need to ensure that your data is protected from unauthorized access, modification, or loss, and that you comply with the relevant regulations and standards. You also need to consider the reliability and availability of the cloud service provider, and the potential impact of network latency, outages, or disruptions on your data and visualizations. Another challenge is compatibility. You need to ensure that your cloud-based tools can communicate and interact with your DCS hardware and software, and that they support the protocols, formats, and standards that you use.

4 Best practices for cloud-based tools for data visualization

To overcome these challenges and make the most of cloud-based tools for data visualization, you need to follow some best practices. First, you need to select the right tool for your purpose and audience. You need to consider the features, functionality, usability, and cost of the tool, as well as the type, size, and complexity of your data and visualizations. You also need to consider the feedback and expectations of your stakeholders, such as managers, engineers, operators, or clients. Second, you need to design your visualizations with clarity, simplicity, and relevance. You need to choose the appropriate chart types, colors, fonts, labels, and filters that convey your message effectively and accurately. You also need to avoid clutter, confusion, and distortion that may mislead or overwhelm your viewers. Third, you need to test and optimize your visualizations for performance, compatibility, and security. You need to check that your visualizations load fast, display correctly, and respond to user inputs on different devices and browsers. You also need to ensure that your data and visualizations are encrypted, backed up, and updated regularly.

5 Examples of cloud-based tools for data visualization in DCS

To illustrate how cloud-based tools for data visualization can be used in DCS, here are some examples of real-world applications. One example is the use of Google Data Studio to create a dashboard that monitors and analyzes the energy consumption and efficiency of a DCS for a wastewater treatment plant. The dashboard shows the key performance indicators, such as power, flow, temperature, and pressure, and allows the user to filter, drill down, and compare the data by time, location, or process. Another example is the use of Microsoft Power BI to create a report that shows the status and trends of a DCS for a chemical plant. The report shows the operational data, such as production, quality, and safety, and allows the user to interact, explore, and share the data with different views, charts, and maps.

6 Future trends of cloud-based tools for data visualization in DCS

As cloud computing and data visualization technologies continue to develop, so do the possibilities and issues for control engineering applications. In the future, edge computing may influence the use of cloud-based tools for data visualization in DCS. Edge computing allows data processing and visualization at the edge of the network, closer to the sources and devices, reducing latency, bandwidth, and security risks. Additionally, artificial intelligence and machine learning integration can automate, enhance, and personalize data analysis and visualization to provide insights, predictions, and recommendations. Augmented reality and virtual reality adoption can also make data visualization immersive, interactive, and engaging. Ultimately, these advancements create realistic and immersive experiences.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?