Beyond the dashboard: Why data analysis demands personalization
Conventional data systems are designed for automated analysis, predefined workflows and dashboards for routine monitoring and reporting. They are good, but not sufficient for real-life troubleshooting, which is complex and nonlinear in nature.
When a process engineer must tackle new phenomena or unexpected issues, ad hoc data analysis is required. This exploratory work rarely follows a strict, system-driven sequence; rather, it proceeds in undefined steps of investigation, requiring analysts to adapt their approach as insights continually evolve.
For this essential, creative problem-solving to flourish, the data analysis platform must empower individual users to easily and intuitively create and fine-tune their environment for specific tasks.
Customization: The key to agile troubleshooting and process optimization
A highly effective data analysis tool must serve the user’s “mindflow,” not compel them to adhere to a rigid theoretical workflow. This flexibility is realized through deep personalization capabilities:
- On-the-Fly Customization: The capability to define one’s own workspaces, customizing the layout and selecting visualization options via intuitive drag-and-drop functionality, is essential. This allows process experts to tailor dashboards and create ad-hoc diagrams in seconds to contextualize a specific issue.
- Intuitive Data Refinement: Raw data rarely offers reliable analysis results immediately. Domain expertise is needed to judge which values are irrelevant, necessitating tools that support immediate data cleansing, filtering, and refinement. This includes the crucial ability to compensate for complex process delays (lags) to ensure reliable correlation findings.
- Ad Hoc Calculation Engine: To derive truly useful measurements, users frequently need to combine information from several sources, performing unit conversions or creating new calculated measurements (soft sensors). Systems must include an embedded calculation engine that allows users to easily create and utilize these custom measurements in diagrams and analyses as if they were real physical measurements.
- Accessible Causal Analysis: Understanding the “why” is the ultimate goal. The system must empower users to intuitively apply advanced analysis methods to uncover hidden root cause-and-consequence relationships. This functionality must be intuitive, enabling process experts to diagnose the source of anomalies without requiring data science intervention.
The best data analytics tools combine automation and customization capabilities
For effective data analysis and utilization, the best data analysis systems enable both automated analysis and flexible customization.
Standard views and automated reports fulfill the need for consistency and knowledge dissemination, but they are insufficient for the agile, spontaneous demands of modern troubleshooting and process optimization.
The competitive edge belongs to organizations that deploy systems that allow users to seamlessly modify datasets, views, and analytic parameters, ensuring that the system is always precisely tuned to the singular task—no matter how novel—that the user is trying to solve.
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