1. Advantages of sas for reporting
SAS® mindfulness
Advantages of sas for reporting

2. Advantages of SAS for Reporting
Large Volumes of Data
Multiple Data Sources
Logic Based Fields and Flags
Visualization of Data
Validation
Log Review
Automation
Elizabeth

3. Large Volume of Data and Grouping
Challenges
Record selection in Crystal Reports only offers limited functionality
Datasets can’t be filtered prior to being joined resulting in large final output
Complicated design and formulas necessary to complete in Crystal Reports
Crystal Limitations
Breaking apart queries
Easier to read: Ability to step through process
view oracle queries separately: validation
Enhanced filtering capabilities
Limits on more than one oracle query
Filter on “on”
Exclude in between queries
Merging datasets
\\dhs\dfs\Groups\Decision Support\IS\Elizabeth Villalobos\Completed\I9738 Chlamydia BPA Effectiveness with Existing Orders\DS18I9738 Chlamydia BPA Effectiveness with Existing Orders.sas

4. Large Volume of Data and Grouping
Example:
Accounts Receivable needs a report listing the first late credit card payment.
Obstacle:
Crystal reports would require bringing back all late payments and then only displaying the first row.
Breaking apart queries
Easier to read: Ability to step through process
view oracle queries separately: validation
Enhanced filtering capabilities
Limits on more than one oracle query
Filter on “on”
Exclude in between queries
Merging datasets
\\dhs\dfs\Groups\Decision Support\IS\Elizabeth Villalobos\Completed\I9738 Chlamydia BPA Effectiveness with Existing Orders\DS18I9738 Chlamydia BPA Effectiveness with Existing Orders.sas

5. Large Volume of Data and Grouping
Solution:
Use first dot technique to limit to 1 row in a SAS program.
Breaking apart queries
Easier to read: Ability to step through process
view oracle queries separately: validation
Enhanced filtering capabilities
Limits on more than one oracle query
Filter on “on”
Exclude in between queries
Merging datasets
\\dhs\dfs\Groups\Decision Support\IS\Elizabeth Villalobos\Completed\I9738 Chlamydia BPA Effectiveness with Existing Orders\DS18I9738 Chlamydia BPA Effectiveness with Existing Orders.sas

6. Large Volume of Data and Grouping
Advantage:
Only 1 row/account is displayed as output rather than multiple rows/account.
Breaking apart queries
Easier to read: Ability to step through process
view oracle queries separately: validation
Enhanced filtering capabilities
Limits on more than one oracle query
Filter on “on”
Exclude in between queries
Merging datasets
\\dhs\dfs\Groups\Decision Support\IS\Elizabeth Villalobos\Completed\I9738 Chlamydia BPA Effectiveness with Existing Orders\DS18I9738 Chlamydia BPA Effectiveness with Existing Orders.sas

7. Large Volume of Data and Grouping
Example:
Account representatives have requested account detail on late payments. The representatives will be contacting customers with more than 3 late payments.
Elizabeth
Obstacle:
Crystal reports is unable (through standard record selection) to use advanced programming techniques, such as a having clause.

8. Large Volume of Data and Grouping
Solution:
Group on 1 column while querying all data.
Elizabeth

9. Large Volume of Data and Grouping
Advantage:
Only accounts with more than 3 late payments are output rather than all accounts with late payments
Elizabeth

10. Multiple Data Sources Example: Obstacle:
Following attendance of a conference, sales needs a report that compares an excel file with leads against existing accounts to determine new leads to contact.
Melissa
Multiple data sources
Combines multiple databases (e.g. Data Warehouse, Data Marts, Access)
Inputs external files (e.g. excel, flat files)
Obstacle:
Crystal reports struggles with combining multiple data sources – may require hard-coding.

11. Multiple Data Sources Solution:
Import excel file. Merge with account table to create report with lead type.
Melissa
Multiple data sources
Combines multiple databases (e.g. Data Warehouse, Data Marts, Access)
Inputs external files (e.g. excel, flat files)

12. Multiple Data Sources
Advantage: Ability to combine data sources including…
Flat files
Excel
Data Warehouse or Data Mart tables
Access databases
Melissa
\\dhs\dfs\Groups\Decision Support\IS\SAS\SAS_Scheduled_Reports\Production\Programs\Scheduled\Scheduled Programs\DS _Termed_or_Transferred_Provider_Attribution_(Monthly_23rd).sas

13. Validation Techniques
Why is validation important?
Elizabeth

14. Validation Techniques in=
Payments
Accts w/
Pmts
Accounts
Elizabeth

15. Validation Techniques in=
Elizabeth
122 should be 123

16. Validation Techniques Using Counts
Elizabeth
Account_id 789 is duplicating rows based on account_id being built in 2 rows in account table

17. Validation Techniques Using Counts
Elizabeth

18. Validation Techniques Using Sums
Elizabeth

19. Validation Techniques Using Sums
CODING ERROR ISSUE FOUND: FLAG LIMIT NOT CORRECT
Elizabeth

20. Log Review
Melissa

21. Automation: Macros Why would you want macro?
Are your parameters constantly changing?
Do the results of one of your queries impact your subsequent filters?
How often do you need to run the same code for various locations, areas, groups?
Elizabeth

22. Automation: Macro Variables
Example:
The marketing department would like a list of customers whose account was setup 10 years ago and have not had any credit card action on the account for the last 5 years.
Additional Information:
Report will run monthly so dates need to be dynamic
Both datasets are very large and querying together could cause performance issues.
Elizabeth

23. Automation: Macro Variables
1) Dynamically Create Dates
Elizabeth

24. Automation: Macro Variables
2) Dynamically Create List of Accounts
Elizabeth

25. More Ways to Automate Additional automations options in SAS Scheduling
Notifications
Validation of results
Melissa
Scheduling -Need a job to run at the same time every day – middle of the night or trigger to start running jobs (after extract)
Notifications – reports available for users
Validation of results – certain record counts sent to analysts

26. More Ways to Automate Scheduling SAS EG works with Windows scheduler
Melissa

27. More Ways to Automate How do I see which jobs are scheduled? Melissa
Limit based on macro variables created from initial query

28. More Ways to Automate Emailing Notification of jobs finishing
Automatically send out reports or notifications process completed
Melissa
Limit based on macro variables created from initial query

29. More Ways to Automate Validation of results
Output observation counts into s
Melissa

30. Getting Your Feet Wet… Enterprise Guide Wizards Program without coding
View/alter SAS created code
Learn programming techniques
Limit based on macro variables created from initial query

31. …SAS Makes it Easy Programming as an art…especially in SAS
Combines different types of programming:
SQL
Data steps
Procedures
Macro language
Enterprise Guide interface
Limit based on macro variables created from initial query

32. Contact Information
Melissa L. Nedvecki, MBA | Data Analyst Dean Clinic - Corporate Office Business Intelligence 1808 W Beltline Hwy Madison, WI Phone |
Elizabeth J. Villalobos | Data Analyst Dean Clinic - Corporate Office Business Intelligence W Beltline Hwy Madison, WI Phone |
Partners who care
Thank you!
Limit based on macro variables created from initial query
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