Hello World by A SAS programmer/CDISC consultant
A look back at 2008 and some crystal ball predictions…, byTammi Kay Geroge, from SASBlog*/
Major Data Warehousing Events of 2008 (and Predictions for 2009), by Michael Schiff, from TDWI,
Major Data Warehousing Events of 2008:
- Everyone had an appliance story
- Industry consolidations continued
- The recessionary environment encourage further BI developments
- Open source grew
Predictions for 2009:
- Further industry consolidation(Informatica by HP, SPSS by SAP)
- Cloud computing will come down to earth
- Open source growth will accelerate
- The IT world will become greener
- Major emphasis on solutions rather than tools and technology
BusinessIntelligence Tools: Year in Review，by Cindi Howson, from BeyeNetwork
Top Virtualization Trends for 2009, by John Suit, from ZDNet
Surround the Warehouse: Prediction for 2009 , by Neil Raden, from IntelligentEnterprise
SAS and Teradata Partnership: Press
In BI industry, the pure players such as SAS, Teradata and Microstrategy, need to demonstrate their indispensable values against the megavendors, IBM (acquired Cognos), SAP (acquired Business Object), Oracle (acquired Hyperion) and Microsoft. Teradata is solely focused on enterprise data warehouse. SAS, dominating in business analytics (e.g. advanced statistics and data mining), will check and balance the BI industry due to the private-hold structure. SAS and Teradata Advantage Program partnership, includes wide business lines, such as Analytics, AML (Anti-Money Laundering), Credit Risk, Enterprise Intelligence and Optimization Services. I think It’s a effective way to learn from each other in mutual emulation and counterbalance the concentration market.
(santaDashBoard.png, With permission by Mr. Robert Allison)
Merry Christmas again. SAS marketing staff started up an interesting Christmas campaign on how Santa operates his workshop. Here is another wonderful work about Santa’s Dashboard, created by SAS senior R&D staff, Robert Allison.
Robert is a master of graphics and visualization. You can view his SAS/Graph examples in the following link:
1. Forecasting an Autoregressive Progress
2. Forecasting a Moving Average Process
3. Forecasting a Seasonal Process
4. Seasonal Adjustment and Forecasting
5. Forecasting with Transfer Function Models
6. Forecasting with Intervention Models
7. Forecasting Multivariate Time Series
8. Preparing Time Series Data for Forecasting
9. Using Macros for Forecasting Tasks
10. Fitting and Forecasting a Linear Model by OLS
11. Testing Forecasting Models for Break Points with Chow Tests
12. Fitting and Forecasting Linear Models with Linear Restrictions
13. Fitting and Forecasting a Linear Model with an AR Error Correction
14. Fitting Linear Models with Heteroscedastic Error Terms
15. Fitting Linear Models with ARCH-GARCH Error Terms
16. Assessing Forecast Accuracy
17. Forecasting Using a Lagged Dependent Variable Model
18. Static and Dynamic Forecasting Using a Lagged Dependent Variable Model
19. Fitting and Forecasting Polynomial Distributed Lag Models
20. Fitting and Forecasting Restricted Polynomial Distributed Lag Models
21. Fitting and Forecasting a Linear System by SUR and ITSUR
22. Testing and Restricting Parameter Estimates in a Linear System Forecast
23. Producing Goodness-of-Fit Statistics for Forecasts of a Linear System o
24. Fitting a Linear System by Instrumental Methods
25. Linear System Diagnostics and Autoregressive Error Correction
26. Creating Forecast confidence Limits with Monte Carlo Simulation
27. Fitting and Forecasting a Nonlinear Model
28. Restricting and Testing Parameters of a Nonlinear Forecasting Model
29. Producing Forecasts Automatically Using the Time Series Forecasting Sys
30. Developing Forecasting Models Using the Time Series Forecasting System
How to know the boys and girls’ real demands around the world? and how to predict their demands in next Christmas?
How to purchase toys and goodies with a balance of costs and profits? and how to deliver them more efficiently?
There are lots of questions in the list of Santa, CEO of Santa’s Workshop. SAS’s marketing staff held a very creative champion for the coming Christmas. You can watch the interview with Santa in youtube.com, or read the success story about Santa, Santa’s Secret: Magic? No. It’s SAS(R) Business Analytics.
Merry Christmas.
In the latest post, What Makes a Good Business Analyst?, Rajan Chandras cites some soft items from Forrester’s Business Analyst Assessment Workbook:
What’s more, there are some suggestions by Rajan Chandras himself:
No doubt, no boss can reject such a perfect analyst. But I’m afraid these standards are suitable for every professionals. That is to say, they create a model to explain everything. It is too universal to be served as a good filter to select the most proper analysts. She or he may more marketable in any other business line.
Data Mining in Stock Market? Is it crazy? or is it just a hopeless try? Every mentor in mathematics and finance educates us that the stock market is too chaotic and sentimental to use mathematical models. Most of all gift rock scientists are concentrated in the study of interest of rates and fixed income securities. It sounds profitable to use mathematical and statistical models to predict the price of stock, but there are little successfull stories.
I know I might hold some academic doctrines, so I have interest to monitor any effort to try to forecast stock prices using data mining techniques. Some links from a popular data mining blog , Data Mining Research, are listed as follows:
