國立虎尾科技大學 |

Data Analysts' Experience with Big Data.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Data Analysts' Experience with Big Data./
Author:	Tannenbaum, Harve A.
Description:	1 online resource (99 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-07(E), Section: A.
Contained By:	Dissertation Abstracts International79-07A(E).
Subject:	Information science. -
Online resource:	click for full text (PQDT)
ISBN:	9780355614220

Data Analysts' Experience with Big Data.
Tannenbaum, Harve A.

Data Analysts' Experience with Big Data. - 1 online resource (99 pages)

Source: Dissertation Abstracts International, Volume: 79-07(E), Section: A.

Thesis (D.B.A.)--Northcentral University, 2017.

Includes bibliographical references

In this phenomenological study, the researcher examined the work experience of data analysts when working with Big Data to better understand their experience and the conditions that exist in their effort to produce relevant and usable analytic results. The volume, variety, and velocity of Big Data contain an exponential increase in the number of possible correlations, making it difficult to determine which relationships and supporting data are relevant and which are not. Decisions based on irrelevant data could lead to disastrous results for the organization, as well as for those who are subject to that organization's decisions. Extracting meaningful information from larger datasets requires organizations to ask the right questions and analysts to make the right assumptions in selecting, cleaning, and processing data. In this study, the researcher will gather information about the work experiences of data analysts in an era of Big Data, where enough data to fill every library in the United States eight times over is generated every single day. With a growing interest in data-driven decision-making, organizations have employed an increasing number of data analysts, but few researchers have examined how data analysts compensate for the overwhelming amount of unstructured data that must be selected, cleaned, and fitted into appropriate data models. The current phenomenological researcher will interview 12 data analysts in south central Pennsylvania about their experiences within their respective organizations.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355614220Subjects--Topical Terms:

561178
Information science.
Index Terms--Genre/Form:

554714
Electronic books.

Data Analysts' Experience with Big Data.
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500 $a Source: Dissertation Abstracts International, Volume: 79-07(E), Section: A.
500 $a Adviser: Garrett Smiley.
502 $a Thesis (D.B.A.)--Northcentral University, 2017.
504 $a Includes bibliographical references
520 $a In this phenomenological study, the researcher examined the work experience of data analysts when working with Big Data to better understand their experience and the conditions that exist in their effort to produce relevant and usable analytic results. The volume, variety, and velocity of Big Data contain an exponential increase in the number of possible correlations, making it difficult to determine which relationships and supporting data are relevant and which are not. Decisions based on irrelevant data could lead to disastrous results for the organization, as well as for those who are subject to that organization's decisions. Extracting meaningful information from larger datasets requires organizations to ask the right questions and analysts to make the right assumptions in selecting, cleaning, and processing data. In this study, the researcher will gather information about the work experiences of data analysts in an era of Big Data, where enough data to fill every library in the United States eight times over is generated every single day. With a growing interest in data-driven decision-making, organizations have employed an increasing number of data analysts, but few researchers have examined how data analysts compensate for the overwhelming amount of unstructured data that must be selected, cleaned, and fitted into appropriate data models. The current phenomenological researcher will interview 12 data analysts in south central Pennsylvania about their experiences within their respective organizations.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Information science. $3 561178
650 4 $a Information technology. $3 559429
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