The concept of data acquisition has changed the way data scientists acquire and convert data to ready-to-use computerized information. Data acquisition involves the digital collection of units of data through various retrieval elements like sensors, transducers and other devices in the field, a science laboratory or factory plant (Patodkar, 2019)…
Azeez Olanrewaju Shoderu
Mental Wealth Module Task 2 Discussion University of East London, UK through UNICAF
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Introduction
The concept of data acquisition has changed the way data scientists acquire and convert data to ready-to-use computerized information. Data acquisition involves the digital collection of units of data through various retrieval elements like sensors, transducers and other devices in the field, a science laboratory or factory plant (Patodkar, 2019).
Data Acquisition Tools
Both the sensor and transducer (though the terms are mostly employed to mean the same thing) are instruments of data acquisition which aids in measuring a certain amount of data retrieved from a part of the human or animal body or even a particular substance (Banica, Seritan, Valahia and Cepisca, 2012). Apart from these devices, other Data Acquisition Systems (DAQs) are used to in collecting data from the environment and users like the data loggers, USB, Ethernet, etc.Data Life Cycle
Unlike general knowledge, data acquired from the different sources are not always useless after the first or intended use. There exists what is known as the data life cycle which is a concept that refers to the whole process of data management. It reiterates that collected data last beyond first usage and can mostly be reapplied, reanalyzed, stored and even shared for more purposes (Fox, 2011).
Take for instance, if I, a business-oriented data scientist, collect data concerning the user experiences of some of my clients to analyze how satisfied about my product or service offerings, the data elicitated can be reused for maybe calculating how much customer retention ratio I have or even sent to another data scientist that perhaps want to study the psychological impact of user experience with my brand or in relation to other companies around.
Art of Data Management
In other words, the notion of data life cycle is so much linked to the art of data management. In fact, some authors like refer to this aspect of data science and ecology as Data Management Life Cycle wherein they emphasize the various stages of Data Management Life Cycle to include: collecting, processing, storing and securing, using, sharing and communicating, archiving, reusing/repurposing, and destroying of data (Miller, Miller, Moran and Dai, 2018).Lastly, one of the most integral parts of data acquisition and management is the storage of the collected data. In that regard, the database system aids the retention of data. In the words of Bhojaraju (2003), “A database system is an integrated collection of related files, along with details of the interpretation of the data contained therein. Basically, database system is nothing more than a computer-based record keeping system i.e. a system whose overall purpose is to record and maintain information/data.”
To Round Off
Conclusively, the science of data acquisition and management is a rigorous process that takes into account not only the collection and use of data but also all the stages involved in the management of such data. Evidently, data is as much as useful even after its first application to solve any quantifiable problem or as it is used as indices to make a profitable business decision.
Reference List
Banica, C.K., Seritan, G., Valahia, H.A. and Cepisca, C. (2012). Principles of analog signal conditioning. In Selected Topics in Applied Electrotechnics. (pp.190-237) Finland: IWN, Atena.
Bhojaraju G. (2003). Database System: Concepts and Design. In Proceedings of Knowledge Management in Special Libraries in Digital Environment: XXIV All India Conference of IASLIC. Survey 15-18 December. India: Dehra Dun.
Fox, P. (2011). Data Management Considerations for the Data Life Cycle. Washington DC: Tetherless World Constellation.
Miller, K., Miller, M., Moran, M. and Dai, B. (2018). Data Management Life Cycle. Texas: A&M Transportation Institute.
Patodkar, V., Kothawale, R. and Shirsat, S. (2019). Data Acquisition System. In International Journal of Engineering Research and General Science. 7(5): pp.48-52. www.ijergs.org