2026-09-19T04:59:59Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1045152022-01-13T07:54:52Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Sinan K. Aral.
Shukla, Soumya
Sloan School of Management.
Sloan School of Management
2016-09-30T19:33:16Z
2016-09-30T19:33:16Z
2016
2016
http://hdl.handle.net/1721.1/104515
958296268
Thesis: S.M. in Management Studies, Massachusetts Institute of Technology, Sloan School of Management, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages [85]-[90]).
In today's age of 'Information Explosion' most companies are struggling with optimally utilizing all the data generated. The last two decades have seen tremendous progress in data collection, storage, processing and visualization technologies. The last decade has seen a remarkable growth in the types of user data: social, web and more recently mobile. As newer sources of data emerge, our ability to separate an individual from a segment improves. The data being analyzed is not just structured in nature. Several processing technologies are analyzing unstructured data for insights. Emerging technologies based on machine learning are improving our ability to migrate decision making from discovery & diagnostics to prediction & preemption. The rise of Internet of Things enhances the opportunity to collect further granular data. At the same time, as system efficiency increases, concerns about privacy loss and malpractices also increase. The world of big data is more complex and controversial than ever before. This study focuses on creating a baseline of big data technologies and attempts to identify near term trends within the horizon of 5 years due to the pace of technological development. The study places special emphasis on E-commerce Sales & Marketing analytics to determine current challenges and develops a key considerations framework for a new entrant in that space.
by Soumya Shukla.
S.M. in Management Studies
101 unnumbered pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Sloan School of Management.
Study of big data analytics landscape : considerations for market entry of an E-commerce analytics vendor
Considerations for market entry of an E-commerce analytics vendor
Thesis
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<Abstract>In today's age of 'Information Explosion' most companies are struggling with optimally utilizing all the data generated. The last two decades have seen tremendous progress in data collection, storage, processing and visualization technologies. The last decade has seen a remarkable growth in the types of user data: social, web and more recently mobile. As newer sources of data emerge, our ability to separate an individual from a segment improves. The data being analyzed is not just structured in nature. Several processing technologies are analyzing unstructured data for insights. Emerging technologies based on machine learning are improving our ability to migrate decision making from discovery & diagnostics to prediction & preemption. The rise of Internet of Things enhances the opportunity to collect further granular data. At the same time, as system efficiency increases, concerns about privacy loss and malpractices also increase. The world of big data is more complex and controversial than ever before. This study focuses on creating a baseline of big data technologies and attempts to identify near term trends within the horizon of 5 years due to the pace of technological development. The study places special emphasis on E-commerce Sales & Marketing analytics to determine current challenges and develops a key considerations framework for a new entrant in that space.</Abstract>
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