DocumentCode :
633093
Title :
Data for All: A Systems Approach to Accelerate the Path from Data to Insight
Author :
Kandogan, Eser ; Roth, Michael ; Kieliszewski, Cheryl ; Ozcan, Fatma ; Schloss, Bob ; Schmidt, Marc-Thomas
Author_Institution :
IBM Res., Yorktown Heights, NY, USA
fYear :
2013
fDate :
June 27 2013-July 2 2013
Firstpage :
427
Lastpage :
428
Abstract :
Zettabytes of data are available to be harvested for competitive business advantage, sound government policies, and new insights in a broad array of applications. Yet, most of this data is inaccessible for users, since current data analysis tools require an army of technical people to find, transform, analyze, and visualize data in order to make it consumable for decision making. In this paper, we present work in progress to lower the barriers for data-driven decision making by introducing a systems approach to scale the user experience, not only in the volume and variety of data, but also in the skills required to harvest that data. We call for a new approach for data-intensive applications that engages the user as an intelligent partner in a social and intelligent conversation with data by automating, guiding, and recommending data, transformations, visualizations, analytics, and suggesting collaboration opportunities within an analytics marketplace, and leverages both metadata and semantic information about the data captured from conversations.
Keywords :
data analysis; data visualisation; decision making; groupware; meta data; analytics marketplace; collaboration opportunities; data analysis tools; data analytics; data automating; data guiding; data recommending; data transformations; data visualizations; data-driven decision making; data-intensive applications; intelligent conversation; metadata; path acceleration; semantic information; social conversation; systems approach; Artificial intelligence; Business; Collaboration; Data analysis; Data visualization; Semantics; Transforms; Analytics Marketplaces; Automatic Visualization; Conversational Interfaces; Data Integration; Schema Identification; Visual Analytics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Big Data (BigData Congress), 2013 IEEE International Congress on
Conference_Location :
Santa Clara, CA
Print_ISBN :
978-0-7695-5006-0
Type :
conf
DOI :
10.1109/BigData.Congress.2013.69
Filename :
6597173
Link To Document :
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