Overcoming industrial data literacy challenges
Posted: December 4, 2024
Data literacy is increasingly critical for industrial businesses to compete in a data-driven world. According to Forbes, 76% of business leaders find it difficult to understand their data.[1] Similarly, a Control Engineering survey found 60% of industrial decision-makers identified challenges with visualization and analysis.
Harvard Business School Online defines data literacy as “an individual’s ability to read, understand, and utilize data in different ways. It doesn’t require an individual to be an expert—as a data scientist or analyst might be considered—but rather, to show an understanding of basic concepts.” It goes on to say, “Data literacy can help non-data professionals read and understand data and use it to inform their decision-making. As such, data literacy is increasingly important not just for executive leadership, but for managers and employees who want to increase the value they bring to their organization.”[2]
There are two major factors businesses should consider to establish effective data literacy among their industrial personnel. The first is that, given the complexity and variability of industrial data, extracting actionable insights from data can be difficult if you don’t give proper consideration to governance and context. For example, if time-series data doesn’t have a standardized model or structure, and if it hasn’t been prepared to account for late data, engineering units, and data resolution, analysis will frequently require trained experts to parse it into an understandable format for others. This can be made even more complex when other data types are introduced such as 3D models, metadata, and documentation.
The second significant factor to consider in establishing effective data literacy is how personnel access and visualize this industrial data. The diversity in roles and tasks across an industrial organization means that each stakeholder often needs different things from industrial data visualization. It can be challenging to align these requirements in a single tool or application that was likely never meant to serve everyone’s needs.
There are many different types of data visualization available, but not all are intended for the complexities of industrial data. General-purpose business intelligence and analytical software lacks the feature sets and context-awareness you need to create visualizations that explain industrial data effectively. That’s why it’s important to use data visualization purpose-built for industry. Here are the four most common categories of industrial data visualization:
Enterprise visualization
Enables role-based visualizations to rapidly analyze available information and make key business decisions by converging engineering, operations, and business data in context. This transparency across the enterprise’s operational landscape can quickly align teams and create pathways for uncovering new efficiencies.
Operations data visualization
Focuses on understanding real-time operations data in context to quickly spot trends, identify anomalies, and integrate data with the relevant analytic tools to solve immediate problems. Organizing data streams on the basis of assets and process events enables users to easily troubleshoot process challenges and increase operational awareness.
Engineering data visualization
Securely provides access, visualization, and validation of trusted engineering data in 1D, 2D, or 3D formats. Aggregating engineering data from multiple sources and formats helps to unlock new ways of understanding as-designed and as-built variations across the design, build, operation, and decommissioning phases of industrial assets and facilities.
Operations control and HMI
Modern operations control software can do more than just basic process execution. It should encapsulate best practices to reduce knowledge gaps and promote situational awareness. These visualization tools proactively identify actions that positively affect performance and business outcomes in operations processes.
However, knowing which data visualization software best applies in any given situation presents another challenge. It can be helpful to take a step back and break down where and how data visualization needs to be applied in your organization. There are five key aspects that can aid your decision-making when considering data visualization software. Read this article to learn more about choosing the right data visualization software for the job.
Read how to choose the right data visualization software.
[1] Segal, Edward. (2022, August 18). Data Management Poses Major Challenges and Issues for Companies: New Study. Forbes. https://www.forbes.com/sites/edwardsegal/2022/08/18/data-management-poses-major-challenges-and-issues-for-companies-new-study/
[2] Stobierski, Tim. (2021, February 23). Data Literacy: An Introduction for Business. Harvard Business School Online. https://online.hbs.edu/blog/post/data-literacy.
