As context adds value to data, analytics takes that contextualized data to the next level, helping you see relationships you couldn’t see on your own.
For some time, we said that “data is the new oil” to indicate how much value and power there is in having data available. Having data means having knowledge of what’s happening and being able to objectively evaluate phenomena that otherwise could only be guessed. Then we started to say that having the data wasn’t enough—it’s more important to apply context to data so that it can be transformed into information. Data is important, but providing context makes it much more meaningful, and the information can then be used to make better informed decisions.
But today, even information is not enough. Transforming Big Data into Big Information is powerful, but it can still be difficult to interpret and understand. Moreover, when you apply context to data, you are basically applying a model that combines variables you know are correlated in some way. But is that the only existing correlation? Or are some variables correlated to others in way you do not know and maybe are not so evident? The exponential growth of available data and information makes it difficult, if not impossible, to evaluate all the possible relationships, especially when you start to consider data coming from different domains (e.g. process and business data) or data coming from different stages of the value chain.
In manufacturing, analytics often refers to a system that can analyze a set of data and automatically identify relationships between variables. In this way, the system builds a mathematical model that can be used to predict the state or value of a single variable based on the behavior of the others. One of the most used examples is predictive maintenance where, based on the data collected from several sensors installed on an asset, the system can predict if the asset will fail in the near future—optimizing the maintenance process, and minimizing the maintenance costs and possible impact of a failure on production at the same time.
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