Your SlideShare is downloading. ×
How to be data savvy manager
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

How to be data savvy manager

113

Published on

Data is growing exponentially. What should business managers do to make better business decisions? I explain three key things step by step. Just start today!

Data is growing exponentially. What should business managers do to make better business decisions? I explain three key things step by step. Just start today!

Published in: Data & Analytics
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
113
On Slideshare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
1
Comments
0
Likes
1
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. How to be Data savvy Managers 1 Toshifumi Kuga CEO of TOSHI STATS SDN. BHD 3 June 2014
  • 2. The status quo • Data, Data and Data • The internet on things : A lot of devices and parts will be connected to internet • The number of smartphone and tablet is increasing rapidly • Wearable devices will be available with reasonable price soon • Model, Model and Model • Machine Learning, Statistical models are available as a service with reasonable costs through internet 2
  • 3. The digital universe is increasing exponentially 3 4.4 zetta bytes 44 zetta bytes 2013 2020 Source : EMC Digital Universe with Research & Analysis by IDC, April 2014
  • 4. How can we consume data ? • Although a lot of data is available for data analysis, It is not obvious that how we can analyze it in order to make better business decisions • At the result of that, most of data may not be analyzed for decision making 4
  • 5. Data savvy managers are needed • Someone in the company should take an initiative to lead the project for big data analytics all over the company • Someone should take actions to obtain better business decisions based on the results of data analysis 5
  • 6. Data scientist vs Data savvy manager 6 Data savvy manager Data scientist Data knowledge on data in the business data cleaning make it structured Statistical Models ensure models are relevant to make business decisions develop models validate models Output from analysis ensure the results are relevant to make business decisions ensure calculation is accurate
  • 7. How to be data savvy managers • No need to pay a lot of money to be the managers • A lot of useful information are available through internet for free • Statistical tools and data visualization tools (such as R) are also available free or with reasonable costs • A lot of universities provide their courses about data analysis through internet for free (called "MOOCs") 7
  • 8. R language and RStudio 8 It is free !
  • 9. Key three things to be data savvy mangers • Data • Model • Output from analysis 9
  • 10. First : Data • Starting point of data analysis • Business managers should know what kind of data are available for their businesses • Make the list of available data • Discuss data which are available with data scientists 10
  • 11. Second : model • Data savvy managers should know what kind of models exist for data analysis • Statistical model (regression, time series analysis, optimization, etc.) • Machine learning (neural network, decision tree, etc.) • Genetic Programming 11
  • 12. No need to develop models by yourself Just discuss it with data scientists ML<-function(a){ ! x=matrix(c(1,1,1,1,2,3),3,2) y=matrix(c(5, 7, 9),3,1) t=matrix(1,2,1) m=length(y) h=x%*%t j=1/(2*m)*(t(h-y)%*%(h-y)) ! 12 for (i in seq(1,1000)){ h=x%*%t tnew=t-a/m*t(x)%*%(h-y) hnew=x%*%tnew jnew=1/(2*m)*(t(hnew-y)%*%(hnew-y)) if (abs(jnew-j)<=10^(-8)) break t=tnew j=jnew end }} An example of Liner regression model by R
  • 13. Third : Output • Outputs from data analysis are critically important • Outputs are numerical numbers (price, population, revenue, etc.) • Outputs are probability of events (explain later) 13
  • 14. To make better business decisions • Marketing strategy (target, product, price, etc.) • Resource and cost allocations • Risk management (credit, market, liquidity, etc.) 14
  • 15. Predictive analytics • Logistic regression model is used to calculate probability of events • Output means probability of events (buy products, come to shops, cure diseases, be in default, etc) • Logistic regression model generates numbers between 0 and 1 as the result of calculations • These outputs are considered as probability of occurring events 15
  • 16. Let us start today ! • Data is increasing every day • New services of data analysis and visualizations will be available going forward • Download "R language" and try it ! 16
  • 17. Website of R and RStudio! • R is a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-90005107-0 URL http://www.R-project.org • I prepare short movie about how to use R. http://www.toshistats.net/introduction-to-r-language/ • RStudio is one of the best IDE for R. http://www.rstudio.com/products/rstudio/download/ 17
  • 18. Thanks for your attentions! • TOSHI STATS SDN. BHD, Digital-learning center for statistical computing in Asia • CEO : Toshifumi Kuga, Certified financial services auditor • Company web site : www.toshistats.net • Company blog : http://toshistats.wordpress.com/aboutme/ • Company FB page : www.facebook.com/toshistatsco • Please do not hesitate to send your opinion and massages about our courses to us ! 18
  • 19. Disclaimer • TOSHI STATS SDN. BHD. and I do not accept any responsibility or liability for loss or damage occasioned to any person or property through using materials, instructions, methods, algorithm or ideas contained herein, or acting or refraining from acting as a result of such use. TOSHI STATS SDN. BHD. and I expressly disclaim all implied warranties, including merchantability or fitness for any particular purpose. There will be no duty on TOSHI STATS SDN. BHD. and me to correct any errors or defects in the codes and the software. © 2014 TOSHI STATS SDN. BHD. All rights reserved 19

×