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VISUALIZING BIG DATA
Dawit Nida
October 6.2015
EVERY 60 SECONDS
204 million emails sent
1.8 million Facebook likes generated
278,000 Tweets sent
200,000 photos uploaded to
Facebook
Around 100 hours of video
uploaded to YouTube
I. What is this ‘Big Data’?
Introduction and Key words
II. Any secrets behind big data?
The 4V’s of Big Data
III. Competing with Analytics
Big Data Analytics
IV. What to do with this data?
Usefulness of Business Intelligence (BI)
V. Conclusion
VI. References
CONTENTS
INTRODUCTION & KEY WORDS
 Data visualization: refers to the approaches and tools
used to visually understand the insights from data to
prove/disprove a hypothesis.
 Big Data: huge amount of data, that have not been handled
by the traditional data management systems.
 Analytics: knowledge discovery and extracting valuable
trends in data that can be visualized for insights and patterns.
 Business Intelligence (BI): technique, tool required to
collect, store, analyse data into valuable information and
benefit from analysing and making efficient business decisions.
WHY BIG DATA IS ‘BIG’?
BACKGROUND
 Just lot’s of data!
 Or is it?
 Unstructured data
 Text, video, audio, etc.
 Tweets, likes, emails, etc.
 Data is the new oil
 But renewable!
THE FOUR V’S OF BIG DATA
 Volume: huge number of transactional data
stored every second, machine and sensor
data, social media, and enterprise data is
being collected and stored.
 Velocity: data should be dealt in real time, for
instance streaming data may require to be
executed in real time.
THE FOUR V’S OF BIG DATA (CONTINUED)
 Variety: data also varies with respect to
structure. Some data are not structured.
 Veracity: the trustworthiness (uncertainty of
data) of the data, where the data came from?
BIG DATA ANALYTICS
 Descriptive: to provide insight into what has
happened?
 Diagnostic: services demand, customer
segments, any trend?
 Predictive: who need more, helps model
and forecast what might happen?
 Prescriptive: seeks to determine the best
solution or outcome among various
choices, given the known parameters.
BIG DATA ANALYTICS (CONTINUED)
 Reports
 Scorecards
 Dashboards
 Ad hoc analysis
 Visualization
WHY DO WE NEED
BI AND ANALYTICS?
USEFULNESS OF BI
 Optimize business processes
 Better decision making on every level in
the organization based on fact
 Create better customer experience with
better information
 Improve competitiveness
 Increase revenues
CONCLUSION
 Big Data is renewable oil
 Remember the 4 V’s of Big Data
 Analytics can be Descriptive,
Diagnostic, Predictive or Prescriptive
 Business Intelligence is crucial in business
decision making
REFERENCES
 Big Data Analytics, Advanced Analytics in Oracle Database. An
Oracle White Paper. March 2013.
 Tomas Eklund, 4.5.2015. ‘DW Lecture 4: Big Data, ÅAU.
 Marko Grobelnik, 8.5.2012. ‘Big Data Tutorial’, [online]. URL:
http://www.planet-
data.eu/sites/default/files/presentations/Big_Data_Tutorial_part4.
pdf
 Big Data, What it is & why it matters, [online]. URL:
http://www.sas.com/en_us/insights/big-data/what-is-big-
data.html.
 Big Data for Small and Medium-Sized Businesses, [online]. URL:
http://www.ibm.com/midmarket/us/en/article_SmarterAnalytics_
1308.html.
 Doug Laney, Claiming Gartner’s Construct for Big Data, [online].
URL: http://blogs.gartner.com/doug-laney/deja-vvvue-others-
claiming-gartners-volume-velocity-variety-construct-for-big-data/.
THANK YOU!!
CONTACT INFORMATION
dnida@abo.fi
www.dawitnida.com

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Big Data Introduction

  • 1. VISUALIZING BIG DATA Dawit Nida October 6.2015
  • 2. EVERY 60 SECONDS 204 million emails sent 1.8 million Facebook likes generated 278,000 Tweets sent 200,000 photos uploaded to Facebook Around 100 hours of video uploaded to YouTube
  • 3. I. What is this ‘Big Data’? Introduction and Key words II. Any secrets behind big data? The 4V’s of Big Data III. Competing with Analytics Big Data Analytics IV. What to do with this data? Usefulness of Business Intelligence (BI) V. Conclusion VI. References CONTENTS
  • 4. INTRODUCTION & KEY WORDS  Data visualization: refers to the approaches and tools used to visually understand the insights from data to prove/disprove a hypothesis.  Big Data: huge amount of data, that have not been handled by the traditional data management systems.  Analytics: knowledge discovery and extracting valuable trends in data that can be visualized for insights and patterns.  Business Intelligence (BI): technique, tool required to collect, store, analyse data into valuable information and benefit from analysing and making efficient business decisions.
  • 5. WHY BIG DATA IS ‘BIG’?
  • 6. BACKGROUND  Just lot’s of data!  Or is it?  Unstructured data  Text, video, audio, etc.  Tweets, likes, emails, etc.  Data is the new oil  But renewable!
  • 7. THE FOUR V’S OF BIG DATA  Volume: huge number of transactional data stored every second, machine and sensor data, social media, and enterprise data is being collected and stored.  Velocity: data should be dealt in real time, for instance streaming data may require to be executed in real time.
  • 8. THE FOUR V’S OF BIG DATA (CONTINUED)  Variety: data also varies with respect to structure. Some data are not structured.  Veracity: the trustworthiness (uncertainty of data) of the data, where the data came from?
  • 9. BIG DATA ANALYTICS  Descriptive: to provide insight into what has happened?  Diagnostic: services demand, customer segments, any trend?  Predictive: who need more, helps model and forecast what might happen?  Prescriptive: seeks to determine the best solution or outcome among various choices, given the known parameters.
  • 10. BIG DATA ANALYTICS (CONTINUED)  Reports  Scorecards  Dashboards  Ad hoc analysis  Visualization
  • 11. WHY DO WE NEED BI AND ANALYTICS?
  • 12. USEFULNESS OF BI  Optimize business processes  Better decision making on every level in the organization based on fact  Create better customer experience with better information  Improve competitiveness  Increase revenues
  • 13. CONCLUSION  Big Data is renewable oil  Remember the 4 V’s of Big Data  Analytics can be Descriptive, Diagnostic, Predictive or Prescriptive  Business Intelligence is crucial in business decision making
  • 14. REFERENCES  Big Data Analytics, Advanced Analytics in Oracle Database. An Oracle White Paper. March 2013.  Tomas Eklund, 4.5.2015. ‘DW Lecture 4: Big Data, ÅAU.  Marko Grobelnik, 8.5.2012. ‘Big Data Tutorial’, [online]. URL: http://www.planet- data.eu/sites/default/files/presentations/Big_Data_Tutorial_part4. pdf  Big Data, What it is & why it matters, [online]. URL: http://www.sas.com/en_us/insights/big-data/what-is-big- data.html.  Big Data for Small and Medium-Sized Businesses, [online]. URL: http://www.ibm.com/midmarket/us/en/article_SmarterAnalytics_ 1308.html.  Doug Laney, Claiming Gartner’s Construct for Big Data, [online]. URL: http://blogs.gartner.com/doug-laney/deja-vvvue-others- claiming-gartners-volume-velocity-variety-construct-for-big-data/.