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Businesses can study big data to understand the current state of the business and track still-evolving aspects such as customer behavior

Big Data Analytics

IT-INFORMATION TECHNOLOGY, no. 4 (2016): 155-156

Cited by: 1197|Views163
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Abstract

Oddly enough, big data was a serious problem just a few years ago. When data volumes started skyrocketing in the early 2000s, storage and CPU technologies were overwhelmed by the numerous terabytes of big data—to the point that IT faced a data scalability crisis. Then we were once again snatched from the jaws of defeat by Moore’s law. Sto...More

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Introduction
  • Introduction to Big Data Analytics

    Big data analytics is where advanced analytic techniques operate on big data sets.
  • To that end, advanced analytics is the best way to discover new customer segments, identify the best suppliers, associate products of affinity, understand sales seasonality, and so on.
  • For these reasons, TDWI has seen a steady stream of user organizations implementing analytics in recent years
Highlights
  • Introduction to Big Data Analytics

    Big data analytics is where advanced analytic techniques operate on big data sets
  • Businesses can study big data to understand the current state of the business and track still-evolving aspects such as customer behavior
  • Why the rush to advanced analytics? First, change is rampant in business, as seen in the multiple “economies” we’ve gone through in recent years
  • TDWI has seen a steady stream of user organizations implementing analytics in recent years
Methods
  • The purpose of this report is to accelerate users’ understanding of the many new tools and techniques that have emerged for analytics with big data in recent years.
  • It will help readers map newly available options to realworld use cases.
  • The authors asked consultants to fill out the survey with a recent client in mind
Conclusion
  • When data volumes started skyrocketing in the early 2000s, storage and CPU technologies were overwhelmed by the numerous terabytes of big data—to the point that IT faced a data scalability crisis.
  • Enterprises are exploring big data to discover facts they didn’t know before.
  • This is an important task right because the recent economic recession forced deep changes into most businesses, especially those that depend on mass consumers.
  • Businesses can study big data to understand the current state of the business and track still-evolving aspects such as customer behavior
Funding
  • Sponsors Cloudera, EMC Greenplum, IBM, Impetus Technologies, Kognitio, ParAccel, SAND Technology, SAP, SAS, Tableau Software, and Teradata sponsored the research for this report
Study subjects and analysis
survey respondents: 360
The invitation was also distributed via Web sites, newsletters, and publications from TDWI and other firms. The survey drew responses from almost 360 survey respondents. From these, we excluded incomplete responses and respondents who identified themselves as academics or vendor employees

respondents: 325
From these, we excluded incomplete responses and respondents who identified themselves as academics or vendor employees. The resulting completed responses of 325 respondents form the core data sample for this report.

Survey Demographics
. The majority of survey respondents are corporate IT professionals (58%), whereas the others are business sponsors or users (22%) and consultants (20%)

respondents: 325
From these, we excluded incomplete responses and respondents who identified themselves as academics or vendor employees. The resulting completed responses of 325 respondents form the core data sample for this report. Survey Demographics

survey respondents: 325
Media/entertainment/publishing Advertising/marketing/PR Computer manufacturing Education Utilities Other (“Other” consists of multiple industries, each represented by 2% or less of respondents.). Based on 325 survey respondents. Big data used to be a technical problem

respondents: 325
The three Vs of big data. Based on 325 respondents. Based on 92 respondents who report having a name for big data analytics

respondents: 92
Based on 325 respondents. Based on 92 respondents who report having a name for big data analytics. Figure 4

respondents: 325
Figure 4. Based on 1,635 responses from 325 respondents; 5 responses per respondent, on average. Based on 1,153 responses from 325 respondents; 3.5 responses per respondent, on average

respondents: 325
Based on 1,635 responses from 325 respondents; 5 responses per respondent, on average. Based on 1,153 responses from 325 respondents; 3.5 responses per respondent, on average. Based on 325 responses

respondents: 109
Based on 325 responses. Based on 109 respondents who report practicing big data analytics. Based on 113 responses from 109 respondents who report practicing big data analytics

respondents: 109
Based on 109 respondents who report practicing big data analytics. Based on 113 responses from 109 respondents who report practicing big data analytics. Figure 10

respondents: 109
Figure 10. Based on 207 responses from 109 respondents who report practicing big data analytics; 1.9 responses per respondent, on average. Based on 450 responses from 109 respondents who report practicing big data analytics; 4.1 responses per respondent, on average

respondents: 109
Based on 207 responses from 109 respondents who report practicing big data analytics; 1.9 responses per respondent, on average. Based on 450 responses from 109 respondents who report practicing big data analytics; 4.1 responses per respondent, on average. Based on 96 respondents who report practicing big data analytics

respondents: 96
Based on 450 responses from 109 respondents who report practicing big data analytics; 4.1 responses per respondent, on average. Based on 96 respondents who report practicing big data analytics. Figure 14

respondents: 325
Figure 14. Based on 1,098 responses from 325 respondents; 3.4 responses per respondent, on average. Based on varying numbers of responses from 325 respondents. The charts are sorted by the “Potential Growth” column of values

respondents: 325
Based on 1,098 responses from 325 respondents; 3.4 responses per respondent, on average. Based on varying numbers of responses from 325 respondents. The charts are sorted by the “Potential Growth” column of values. Plots are approximate, based on values from Figure 16

Reference
  • 1 See the TDWI Best Practices Report Next Generation Data Warehouse Platforms (Q4 2009), available on tdwi.org.
    Google ScholarFindings
  • 4 For example, see Figures 8, 10, and 12 in the 2007 TDWI report Best Practices in Operational BI, available on tdwi.org.
    Google ScholarLocate open access versionFindings
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