Data collection system

Data collection system (DCS) is a computer application that facilitates the process of data collection, allowing specific, structured information to be gathered in a systematic fashion, subsequently enabling data analysis to be performed on the information.[1][2][3] Typically a DCS displays a form that accepts data input from a user and then validates that input prior to committing the data to persistent storage such as a database.

Many computer systems implement data entry forms, but data collection systems tend to be more complex, with possibly many related forms containing detailed user input fields, data validations, and navigation links among the forms.

DCSs can be considered a specialized form of content management system (CMS), particularly when they allow the information being gathered to be published, edited, modified, deleted, and maintained. Some general-purpose CMSs include features of DCSs.[4][5]

Importance

Accurate data collection is essential to many business processes,[6][7][8] to the enforcement of many government regulations,[9] and to maintaining the integrity of scientific research.[10]

Data collection systems are an end-product of software development. Identifying and categorizing software or a software sub-system as having aspects of, or as actually being a "Data collection system" is very important. This categorization allows encyclopedic knowledge to be gathered and applied in the design and implementation of future systems. In software design, it is very important to identify generalizations and patterns and to re-use existing knowledge whenever possible.[11]

Types

Generally the computer software used for data collection falls into one of the following categories of practical application.[12]

Vocabulary

There is a taxonomic scheme associated with data collection systems, with readily-identifiable synonyms used by different industries and organizations.[23][24][25] Cataloging the most commonly used and widely accepted vocabulary improves efficiencies, helps reduce variations, and improves data quality.[26][27][28]

The vocabulary of data collection systems stems from the fact that these systems are often a software representation of what would otherwise be a paper data collection form with a complex internal structure of sections and sub-sections. Modeling these structures and relationships in software yields technical terms describing the hierarchy of data containers, along with a set of industry-specific synonyms.[29][30]

Collection synonyms

A collection (used as a noun) is the topmost container for grouping related documents, data models, and datasets. Typical vocabulary at this level includes the terms:[29]

  • Project
  • Registry
  • Repository
  • System
  • Top-level Container
  • Library
  • Study
  • Organization
  • Party
  • Site

Data model synonyms

Each document or dataset within a collection is modeled in software. Constructing these models is part of designing or "authoring" the expected data to be collected. The terminology for these data models includes:[29]

  • Datamodel
  • Data dictionary
  • Schema
  • Form
  • Document
  • Survey
  • Instrument
  • Questionnaire
  • Data Sheet
  • Expected Measurements
  • Expected Observations
  • Encounter Form
  • Study Visit Form

Sub-collection or master-detail synonyms

Data models are often hierarchical, containing sub-collections or master–detail structures described with terms such as:[29]

  • Section, Sub-section
  • Block
  • Module
  • Sub-document
  • Roster
  • Parent-Child[31]
  • Dynamic List[31]

Data element synonyms

At the lowest level of the data model are the data elements that describe individual pieces of data. Synonyms include:[29][32]

Data point synonyms

Moving from the abstract, domain modelling facet to that of the concrete, actual data: the lowest level here is the data point within a dataset. Synonyms for data point include:[29]

  • Value
  • Input
  • Answer
  • Response
  • Observation
  • Measurement
  • Parameter Value
  • Column Value

Dataset synonyms

Finally, the synonyms for dataset include:[29]

  • Row
  • Record
  • Occurrence
  • Instance
  • (Document) Filing
  • Episode
  • Submission
  • Observation Point
  • Case
  • Test
  • (Individual) Sample
gollark: * code
gollark: If you edit the coe.
gollark: Well, it ism
gollark: @Terrariola#0000 Why would squid add that?
gollark: @Terrariola#0000 No.

See also

References
  1. "What is a Data Collection System (DCS)? - Definition from Techopedia". Techopedia.com. Retrieved 2016-10-14.
  2. "Planning and Design of Data Collection Systems". U.S. Department of Transportation (US DOT). 2005-08-15. Retrieved 2016-10-14.
  3. "Surveys and Data Collection Systems". U.S. Department of Health & Human Services. 2016-04-16. Retrieved 2016-10-14.
  4. "Using SharePoint Forms for Data Collection". Microsoft Corporation. Retrieved 2016-10-14.
  5. "Using Drupal for Multi-Page Collection of Data from Users". The Drupal Association. 2009-07-03. Retrieved 2016-10-14.
  6. "Data collection". SearchCIO. TechTarget. Retrieved 20 December 2016.
  7. "Which Data Collection Method Should I Choose?". B2B International. B2B International. Retrieved 20 December 2016.
  8. "How and Why Data Will Save Small Business". Small Business Trends Small Business Trends. Small Business Trends LLC. 2015-03-20. Retrieved 20 December 2016.
  9. "FAQ: Data Collection Requirements for Broker-Dealers". FINRA.org. Financial Industry Regulatory Authority, Inc. on behalf of the U.S. Securities and Exchange Commission (SEC). Retrieved 4 February 2017.
  10. Data Collection and Analysis By Dr. Roger Sapsford, Victor Jupp ISBN 0-7619-5046-X
  11. Sen, A. (1997). "The role of opportunism in the software design reuse process". IEEE Transactions on Software Engineering. 23 (7): 418–436. doi:10.1109/32.605760.
  12. "Data Collection Software". GetApp. Nubera eBusiness S.L. Retrieved 20 December 2016.
  13. "Survey Data Collection". NORC at the University of Chicago. 2016. Retrieved 2016-10-14.
  14. "Using the Data Collection System". U.S. Department of Education. 2016. Retrieved 2016-10-14.
  15. "How to Collect Data". American College of Cardiology. 2016. Retrieved 2016-10-14.
  16. Frøen, J. F.; Myhre, S. L.; Frost, M. J.; Chou, D.; Mehl, G.; Say, L.; Cheng, S.; Fjeldheim, I.; Friberg, I. K.; French, S.; Jani, J. V.; Kaye, J.; Lewis, J.; Lunde, A.; Mørkrid, K.; Nankabirwa, V.; Nyanchoka, L.; Stone, H.; Venkateswaran, M.; Wojcieszek, A. M.; Temmerman, M.; Flenady, V. J. (2016). "eRegistries: Electronic registries for maternal and child health". BMC Pregnancy and Childbirth. 16: 11. doi:10.1186/s12884-016-0801-7. PMC 4721069. PMID 26791790.
  17. Pace, W. D.; Staton, E. W. (2005). "Electronic Data Collection Options for Practice-Based Research Networks". Annals of Family Medicine. 3 (Suppl 1): s21–s29. doi:10.1370/afm.270. PMC 1466955. PMID 15928215.
  18. "MANAGING DATA FOR PERFORMANCE IMPROVEMENT" (PDF). U. S. Department of Health and Human Services Health Resources and Services Administration.
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  20. "Quiz - Drupal.org". Drupal.org. Dries Buytaert. Retrieved 20 December 2016.
  21. "Online QuizBuilder web app built with Laravel". Webxity. Webxity Technologies.
  22. "Regulatory Filing". FINRA.org. Financial Industry Regulatory Authority, Inc. on behalf of the U.S. Securities and Exchange Commission (SEC). Retrieved 4 February 2017.
  23. Hay, David C. (2006). Data model patterns a metadata map ([Repr.]. ed.). Amsterdam: Elsevier Morgan Kaufmann. p. 40. ISBN 978-0120887989. Retrieved 5 February 2017.
  24. "Classification, Taxonomies and You" (PDF). Verity. Verity, Inc. Retrieved 6 February 2017.
  25. Bayona-Oré, Sussy; Calvo-Manzano, Jose A.; Cuevas, Gonzalo; San-Feliu, Tomas (21 December 2012). "Critical success factors taxonomy for software process deployment". Software Quality Journal. 22 (1): 21–48. doi:10.1007/s11219-012-9190-y.
  26. "Collecting and Reporting Data for Performance Measurement: Moving Toward Alignment". Proceedings of the AHRQ Conference on Health Care Data Collection and Reporting. AHRQ Publication No. 07-0033-EF (March 2007): 13 of 50. November 8–9, 2006. Retrieved 4 February 2017.
  27. Busch, Joseph. "Conducting Taxonomy Validation: Healthcare Example" (PDF). Taxonomy Strategies. Taxonomy Strategies LLC. Retrieved 7 February 2017.
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  31. "NCDR® AFib Ablation Registry™ v1.0 - Data Dictionary - Full Specifications [PDF]". ACC Quality Improvement for Institutions. American College of Cardiology. p. 36 of 143. Retrieved 9 February 2017.
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