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In the Define phase, the important points are the defining and scoping of the problem/project and the prioritizing based on a cause/effect relationship. Reaching those goals comes with specific analytics needs associated with the Define phase. In order to examine an issue, one important point is to have quick access to various data sources. Thu, solutions in the space of self-service analytics should have live connections to various data sources encountered in the ecosystem of process industry so that all the necessary data can be used as needed.
The first exploration of a problem often is visually driven. This means that visual analytics plays a important role. A good self-service platform will provide the enticing visuals that are easy to interpret, relevant for process industry, and fast to create.
As is known to everyone who works with data in any capacity that analyzing the cause/effect relationship of a symptomatic upset starts with rigorous data preparation. It is of central importance that self-service analytics provides an easy way to provide an answer to the question “Which process conditions are representable of my symptom at hand?”. Only then is an unbiased view on the problem possible. In order to retrieve all relevant symptoms which can be seen as similar, the predefined data space process data needs to be made searchable including context information from the various roles involved in the production. With this can a clear picture of the problem/project emerge based on the complete history of the data.
Since prioritization usually is about business impact, it may be necessary to calculate relevant KPIs if not present in the historian. Here, an easy-to-use formula editor enables engineers and operators to create those KPIs and make them available for any further analytics.