Data is an indispensable part of any business process. It is simply impossible to imagine a process, which operates without certain input as a starting condition and without an output representing its results. In one or another form, these inputs and outputs are always associated with certain business data. A process is a flow (in business terms, a workflow) transforming the data.
On another hand, data is amorphous and shapeless without a process behind. Data is always a result of certain process and always have a reservation of consumption by one or another process sooner or later. Rarely somebody stores the data, especially, business data, entirely without an intent for subsequent usage.
Business process is a fundamental foundation, which shapes the data and gives it a business sense. Data with an undefined or lost business context become a silos. Synthesis of the data and its associated processes in a business model is the cornerstone for consistent storage of the data and its effective usage in successful process automation.
There always exists a discrepancy between a model of business process, however well designed and accurate, and real execution of this process in a business environment. The reason for this gap is an unforeseen depth and hidden details inherent to any real process. Real business model of organization is ultimately unlimited in its depth. Going from highest management levels, it descends to individual departments, client relations, production units, technical code of equipment and controllers etc. In vast majority of cases, it is impossible and senseless to build a complete model covering all and every fine detail of the business. Omitted lower layers of the model create (pseudo) random fluctuations during execution of the model. Real execution paths of a process never follow its model exactly. However, in case of the correct model, we can expect to see that an ensemble of execution paths statistically converges to the model as to its average path over a significant set of observation...
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