Data collection system explained

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:

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:

Sub-collection or master-detail synonyms

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

Data element synonyms

At the lowest level of the data model are the data elements that describe individual pieces of data. Synonyms include:[31]

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:

Dataset synonyms

Finally, the synonyms for dataset include:

See also

Notes and References

  1. Web site: What is a Data Collection System (DCS)? - Definition from Techopedia . Techopedia.com . 2016-10-14 .
  2. Web site: Planning and Design of Data Collection Systems . U.S. Department of Transportation (US DOT) . 2005-08-15 . 2016-10-14.
  3. Web site: Surveys and Data Collection Systems . U.S. Department of Health & Human Services . 2016-04-16 . 2016-10-14.
  4. Web site: Using SharePoint Forms for Data Collection . Microsoft Corporation . 2016-10-14 .
  5. Web site: Using Drupal for Multi-Page Collection of Data from Users . The Drupal Association . 2009-07-03 . 2016-10-14.
  6. Web site: Data collection. SearchCIO. TechTarget. 20 December 2016.
  7. Web site: Which Data Collection Method Should I Choose?. B2B International. 20 December 2016.
  8. Web site: How and Why Data Will Save Small Business. Small Business Trends Small Business Trends. Small Business Trends LLC. 20 December 2016. 2015-03-20.
  9. Web site: 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). 4 February 2017.
  10. Data Collection and Analysis By Dr. Roger Sapsford, Victor Jupp
  11. The role of opportunism in the software design reuse process. IEEE Transactions on Software Engineering. 23. 7. 418–436. 10.1109/32.605760. 1997. Sen. A..
  12. Web site: Data Collection Software. GetApp. Nubera eBusiness S.L.. 20 December 2016.
  13. Web site: Survey Data Collection . NORC at the University of Chicago . 2016 . 2016-10-14.
  14. Web site: Using the Data Collection System . U.S. Department of Education . 2016 . 2016-10-14.
  15. Web site: How to Collect Data . American College of Cardiology . 2016 . 2016-10-14.
  16. eRegistries: Electronic registries for maternal and child health. 16. 11. 4721069. BMC Pregnancy and Childbirth. 2016. 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.. 26791790. 10.1186/s12884-016-0801-7 . free .
  17. Electronic Data Collection Options for Practice-Based Research Networks. 3. Suppl 1. s21–s29. 1466955. Annals of Family Medicine. 2005. Pace. W. D.. Staton. E. W.. 15928215. 10.1370/afm.270.
  18. Web site: MANAGING DATA FOR PERFORMANCE IMPROVEMENT. U. S. Department of Health and Human Services Health Resources and Services Administration.
  19. Collecting and Reporting Data for Performance Measurement: Moving Toward Alignment. Proceedings of the AHRQ Conference on Health Care Data Collection and Reporting. November 8–9, 2006. AHRQ Publication No. 07-0033-EF. March 2007. 4 February 2017.
  20. Web site: Quiz - Drupal.org. Drupal.org. 6 July 2005 . Dries Buytaert. 20 December 2016.
  21. Web site: Online QuizBuilder web app built with Laravel. Webxity. Webxity Technologies.
  22. Web site: Regulatory Filing. FINRA.org. Financial Industry Regulatory Authority, Inc. on behalf of the U.S. Securities and Exchange Commission (SEC). 4 February 2017.
  23. Book: Hay. David C.. Data model patterns a metadata map. 2006. Elsevier Morgan Kaufmann. Amsterdam. 978-0120887989. 40. [Repr.].. 5 February 2017.
  24. Web site: Classification, Taxonomies and You. Verity. Verity, Inc.. 6 February 2017.
  25. Bayona-Oré. Sussy. Calvo-Manzano. Jose A.. Cuevas. Gonzalo. San-Feliu. Tomas. Critical success factors taxonomy for software process deployment. Software Quality Journal. 21 December 2012. 22. 1. 21–48. 10.1007/s11219-012-9190-y. 18047921.
  26. Collecting and Reporting Data for Performance Measurement: Moving Toward Alignment. Proceedings of the AHRQ Conference on Health Care Data Collection and Reporting. 13 of 50. November 8–9, 2006. AHRQ Publication No. 07-0033-EF. March 2007. 4 February 2017.
  27. Web site: Busch. Joseph. Conducting Taxonomy Validation: Healthcare Example. Taxonomy Strategies. Taxonomy Strategies LLC. 7 February 2017.
  28. Web site: 6 Challenges: Performance Measurement Data Collection & Reporting. Extract Systems. 15 December 2016 . 7 February 2017.
  29. Book: Hay. David C.. Data model patterns : conventions of thought. 1996. Dorset House Pub.. New York. 978-0932633293. 218ff. 6 February 2017.
  30. Wendicke. Annemarie. What Makes Data Meaningful? The Important Role of Data Structures. Journal of AHIMA. 87. 3. 34–36. March 2016. 27039625. 7 February 2017.
  31. Web site: Data Element: Federal Standard 1037C: Glossary of Telecommunications Terms. www.its.bldrdoc.gov. U.S. Dept. of Commerce, Institute for Telecommunication Sciences. 7 February 2017. 1 March 2011. https://web.archive.org/web/20110301100455/http://www.its.bldrdoc.gov/fs-1037/fs-1037c.htm. dead.

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