The Path from Data to Actionable Information as a Driver for the Industrial Ecosystem
were used, such as R to find optimal solutions).
CONCLUSIONS
The process of taking data from a raw, proprietary format to prescriptive wisdom requires a set of transformations and analytics that adds meaning and context at every step. As with any good architecture, the process splits the responsibilities into multiple composable stages, as illustrated in Figure 1: Data and Meaning, where each stage adds capabilities and value to the business and helps achieve measurable outcomes.
In many implementations, some of these stages will be combined into a single step, but even when that is done, the intermediary transformations and analytics are hidden in a common component. We have found that in most cases, these stages of data analysis must occur and, when put into a framework, it is easier to reason on each step in the process separately and create a more flexible system.
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