(303c) Using Data Effectively to Enhance Decision Support


The common complaint from process industry plants is that there is plenty of data, but not enough information. How can that be? Why doesn't every data point collected in or produced by process control systems, data historians, and company personnel working in their Excel spreadsheets, provide information that decision-makers can put to good use right away? Or, to put the question in even simpler terms, how does data become information?

Computers have greatly expanded our ability to collect and evaluate data, but how data that is collected used to improve facility manager's decisions is rarely considered in the design of computer systems except from a very narrow perspective, such as producing a specific report. In some cases, that is all that the data is capable of being used for, and hence, it is sufficient that the data not be evaluated further. To elevate data to the level where it can contribute to our understanding of an issue or a fact, it must be viewed in the context of a human goal or objective. For example, the amount of money spent on office supplies this month constitutes data. However, the total funds spent on all overhead items (including supplies) may be the information that a decision-maker really needs, because the company's overhead costs cannot be passed on to customers in the same way that materials and labor can be allocated. Information ties data to a specific human endeavor and helps us better understand it.

In some cases, data is only collected to furnish minimal information and need not be processed further. However, if we take the next step forward in the distillation of data beyond producing information, we are using it to produce or increase knowledge. For example, while total overhead costs may be useful information, decision-makers may need to index overhead costs to an operating parameter, such as total revenues or the number of employees, to discern patterns in the data that will produce better decisions about budgets and spending policies. Knowledge is the recognition of what the information means to a person or an organization. When knowledge is further studied or assessed in the light of experience, or if multiple underlying patterns are elucidated to reveal previously unknown truths, then knowledge can approach wisdom.

In most industrial sector companies, the data collected at plants is culled and assessed to produce usable information to facility managers, but much of the knowledge-building is understood to occur at other levels of management, such as in a corporate planning group. The effectiveness of corporate knowledge-building is directly related to the quality of the data collected as well as its inherent ability to be ?rolled up? to higher levels of the organization. All of the basic principals of generating knowledge from data apply to this process. Companies may find that their data cannot be readily transformed into knowledge because it cannot be ?rolled up? without extensive processing of the data, or for other reasons such as accuracy or completeness. Consequently, looking ahead at how data will be utilized for knowledge-building can improve the manner in which ?raw? data is collected. This paper will discuss this challenging aspect of knowledge management.


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