457.From Billion-Dollar Marriages to Clean Datasets: The Art of SAS Standardization

Billion-Dollar Weddings & Broken Data: Mastering Data Standardization in SAS with Real-World Chaos

1. Introduction – When Glamour Meets Garbage Data

Imagine you're working as a SAS programmer in a global analytics firm. Your manager assigns you a dataset titled “Costly Marriages in the World” high-profile weddings involving celebrities, business tycoons, and royal families. The goal? Generate insights on spending patterns, country-wise trends, and luxury benchmarks.

You open the dataset expecting elegance… but what you get is chaos.

• Marriage costs entered as "5M", "5000000", and "5 million"
• Dates like "2023-02-30" (which doesn’t even exist)
• Country names: "india", "IND", "India "
• Duplicate billionaire weddings (because apparently one wasn’t enough )
• Negative guest counts (time travelers?)

This is not just messy it’s dangerous.

In real-world analytics, especially in clinical trials (SDTM/ADaM) or financial reporting, bad data leads to:

• Incorrect conclusions
• Regulatory rejection
• Business loss

This is where data standardization becomes mission-critical.

Tools like SAS and R are industry powerhouses for cleaning and transforming such messy datasets into structured, reliable formats.

Let’s walk through a step-by-step, real-world implementation using both SAS and R.

2. Raw Data Creation in SAS and R (With Intentional Errors)

SAS Raw Dataset

DATA costly_marriages_raw;

INFILE DATALINES DSD DLM=',' TRUNCOVER;

INPUT ID Name:$20. Country:$15. Cost:$15. Guests Age Marriage_Date:$15.

       Status:$10. Sponsor:$20.;

DATALINES;

1,RoyalWedding,india,5000000,2000,30,2023-02-28,Done,KingCorp

2,EliteUnion,USA,5M,1500,-25,2023-02-30,done,QueenLtd

3,LuxuryTie,UK,NULL,1800,40,2022/12/15,Completed,NULL

4,GrandAffair,India,7000000,,35,15-01-2023,Done,KingCorp

5,RoyalWedding,india,5000000,2000,30,2023-02-28,Done,KingCorp

6,DiamondBond,FRANCE,-8000000,2500,45,2023-03-10,Done,EliteGroup

7,GoldenCeremony,USA,9000000,3000,50,2023-04-12,,GoldInc

8,PlatinumUnion,UK,10000000,3500,60,2023-05-20,Done,

9,SilverKnot,IND,8500000,2800,55,2023-06-15,done,SilverCorp

10,EliteUnion,USA,5M,1500,-25,2023-02-30,done,QueenLtd

;

RUN;

PROC PRINT DATA = costly_marriages_raw;

RUN;

OUTPUT:

Obs	ID	Name	Country	Cost	Guests	Age	Marriage_Date	Status	Sponsor
1	1	RoyalWedding	india	5000000	2000	30	2023-02-28	Done	KingCorp
2	2	EliteUnion	USA	5M	1500	-25	2023-02-30	done	QueenLtd
3	3	LuxuryTie	UK	NULL	1800	40	2022/12/15	Completed	NULL
4	4	GrandAffair	India	7000000	.	35	15-01-2023	Done	KingCorp
5	5	RoyalWedding	india	5000000	2000	30	2023-02-28	Done	KingCorp
6	6	DiamondBond	FRANCE	-8000000	2500	45	2023-03-10	Done	EliteGroup
7	7	GoldenCeremony	USA	9000000	3000	50	2023-04-12		GoldInc
8	8	PlatinumUnion	UK	10000000	3500	60	2023-05-20	Done
9	9	SilverKnot	IND	8500000	2800	55	2023-06-15	done	SilverCorp
10	10	EliteUnion	USA	5M	1500	-25	2023-02-30	done	QueenLtd

R Code – Equivalent Raw Dataset

costly_marriages_raw <- data.frame(

  ID = c(1,2,3,4,5,6,7,8,9,10),

  Name = c("RoyalWedding","EliteUnion","LuxuryTie","GrandAffair",

           "RoyalWedding","DiamondBond","GoldenCeremony","PlatinumUnion",

           "SilverKnot","EliteUnion"),

  Country = c("india","USA","UK","India","india","FRANCE","USA",

              "UK","IND","USA"),

  Cost = c("5000000","5M","NULL","7000000","5000000","-8000000",

           "9000000","10000000","8500000","5M"),

  Guests = c(2000,1500,1800,NA,2000,2500,3000,3500,2800,1500),

  Age = c(30,-25,40,35,30,45,50,60,55,-25),

  Marriage_Date = c("2023-02-28","2023-02-30","2022/12/15",

                    "15-01-2023","2023-02-28","2023-03-10",

                    "2023-04-12","2023-05-20","2023-06-15",

                    "2023-02-30"),

  Status = c("Done","done","Completed","Done","Done","Done","",

             "Done","done","done"),

  Sponsor = c("KingCorp","QueenLtd",NA,"KingCorp","KingCorp",

              "EliteGroup","GoldInc",NA,"SilverCorp","QueenLtd")

)

OUTPUT:

	ID	Name	Country	Cost	Guests	Age	Marriage_Date	Status	Sponsor
1	1	RoyalWedding	india	5000000	2000	30	28-02-2023	Done	KingCorp
2	2	EliteUnion	USA	5M	1500	-25	2023-02-30	done	QueenLtd
3	3	LuxuryTie	UK	NULL	1800	40	15-12-2022	Completed	NA
4	4	GrandAffair	India	7000000	NA	35	15-01-2023	Done	KingCorp
5	5	RoyalWedding	india	5000000	2000	30	28-02-2023	Done	KingCorp
6	6	DiamondBond	FRANCE	-8000000	2500	45	10-03-2023	Done	EliteGroup
7	7	GoldenCeremony	USA	9000000	3000	50	12-04-2023		GoldInc
8	8	PlatinumUnion	UK	10000000	3500	60	20-05-2023	Done	NA
9	9	SilverKnot	IND	8500000	2800	55	15-06-2023	done	SilverCorp
10	10	EliteUnion	USA	5M	1500	-25	2023-02-30	done	QueenLtd

Key Issues Identified

• Missing values (NULL, blank)
• Invalid values (negative Age, Cost)
• Wrong dates
• Duplicates
• Inconsistent text formatting

3. Phase 1: Data Cleaning in SAS

Step-by-Step Cleaning

DATA costly_marriages_clean;

SET costly_marriages_raw;

/* Handle Missing Values */

IF Sponsor = "" OR Sponsor = "NULL" 

THEN Sponsor = "UNKNOWN";

/* Fix Negative Age */

IF Age < 0 THEN Age = ABS(Age);

/* Fix Cost */

IF Cost = "5M" THEN Cost_Num = 5000000;

ELSE IF Cost = "NULL" THEN Cost_Num = .;

ELSE Cost_Num = INPUT(Cost, BEST12.);

IF Cost_Num < 0 THEN Cost_Num = ABS(Cost_Num);

/* Standardize Country */

Country = UPCASE(TRIM(Country));

/* Standardize Status */

Status = UPCASE(Status);

/* Fix Date */

Marriage_dt = INPUT(Marriage_Date, ANYDTDTE.);

FORMAT Marriage_dt DATE9.;

RUN;

PROC PRINT DATA = costly_marriages_clean;

RUN;

OUTPUT:

Obs	ID	Name	Country	Cost	Guests	Age	Marriage_Date	Status	Sponsor	Cost_Num	Marriage_dt
1	1	RoyalWedding	INDIA	5000000	2000	30	2023-02-28	DONE	KingCorp	5000000	02FEB2023
2	2	EliteUnion	USA	5M	1500	25	2023-02-30	DONE	QueenLtd	5000000	03FEB2023
3	3	LuxuryTie	UK	NULL	1800	40	2022/12/15	COMPLETED	UNKNOWN	.	01DEC2022
4	4	GrandAffair	INDIA	7000000	.	35	15-01-2023	DONE	KingCorp	7000000	.
5	5	RoyalWedding	INDIA	5000000	2000	30	2023-02-28	DONE	KingCorp	5000000	02FEB2023
6	6	DiamondBond	FRANCE	-8000000	2500	45	2023-03-10	DONE	EliteGroup	8000000	01MAR2023
7	7	GoldenCeremony	USA	9000000	3000	50	2023-04-12		GoldInc	9000000	01APR2023
8	8	PlatinumUnion	UK	10000000	3500	60	2023-05-20	DONE	UNKNOWN	10000000	02MAY2023
9	9	SilverKnot	IND	8500000	2800	55	2023-06-15	DONE	SilverCorp	8500000	01JUN2023
10	10	EliteUnion	USA	5M	1500	25	2023-02-30	DONE	QueenLtd	5000000	03FEB2023

/* Remove Duplicates */

PROC SORT DATA=costly_marriages_clean NODUPKEY;

BY ID Name;

RUN;

PROC PRINT DATA = costly_marriages_clean;

RUN;

OUTPUT:

Obs	ID	Name	Country	Cost	Guests	Age	Marriage_Date	Status	Sponsor	Cost_Num	Marriage_dt
1	1	RoyalWedding	INDIA	5000000	2000	30	2023-02-28	DONE	KingCorp	5000000	02FEB2023
2	2	EliteUnion	USA	5M	1500	25	2023-02-30	DONE	QueenLtd	5000000	03FEB2023
3	3	LuxuryTie	UK	NULL	1800	40	2022/12/15	COMPLETED	UNKNOWN	.	01DEC2022
4	4	GrandAffair	INDIA	7000000	.	35	15-01-2023	DONE	KingCorp	7000000	.
5	5	RoyalWedding	INDIA	5000000	2000	30	2023-02-28	DONE	KingCorp	5000000	02FEB2023
6	6	DiamondBond	FRANCE	-8000000	2500	45	2023-03-10	DONE	EliteGroup	8000000	01MAR2023
7	7	GoldenCeremony	USA	9000000	3000	50	2023-04-12		GoldInc	9000000	01APR2023
8	8	PlatinumUnion	UK	10000000	3500	60	2023-05-20	DONE	UNKNOWN	10000000	02MAY2023
9	9	SilverKnot	IND	8500000	2800	55	2023-06-15	DONE	SilverCorp	8500000	01JUN2023
10	10	EliteUnion	USA	5M	1500	25	2023-02-30	DONE	QueenLtd	5000000	03FEB2023

Explanation

This SAS step systematically addresses all data inconsistencies. Missing values are replaced using conditional logic, ensuring no blank sponsor fields remain. Negative values are corrected using ABS(), critical for maintaining logical consistency (e.g., age cannot be negative). Cost values are standardized into numeric format, enabling further analysis. The UPCASE and TRIM functions enforce uniform text formatting, which is essential for grouping and reporting. The ANYDTDTE. informat intelligently parses mixed date formats. Finally, PROC SORT NODUPKEY removes duplicate records based on key identifiers, ensuring dataset uniqueness.

Key Points

• Always convert character numbers to numeric early
• Use ANYDTDTE. for messy date formats
• Standardization prevents grouping errors

4. Phase 2: Data Cleaning in R

library(dplyr)

clean_data <- costly_marriages_raw %>%

  mutate(

    Sponsor = ifelse(is.na(Sponsor) | Sponsor == "", "UNKNOWN", Sponsor),

    Age = abs(Age),

    Cost_Num = case_when(Cost == "5M" ~ 5000000,

                         Cost == "NULL" ~ NA_real_,

                         TRUE ~ suppressWarnings(as.numeric(Cost))),

    Cost_Num = abs(Cost_Num),

    Country = toupper(trimws(Country)),

    Status = toupper(Status),

    Marriage_dt = as.Date(Marriage_Date,

                          tryFormats = c("%Y-%m-%d","%d-%m-%Y","%Y/%m/%d"))

  ) %>%

  distinct(ID, Name, .keep_all = TRUE)

OUTPUT:

	ID	Name	Country	Cost	Guests	Age	Marriage_Date	Status	Sponsor	Cost_Num	Marriage_dt
1	1	RoyalWedding	INDIA	5000000	2000	30	28-02-2023	DONE	KingCorp	5.00E+06	28-02-2023
2	2	EliteUnion	USA	5M	1500	25	2023-02-30	DONE	QueenLtd	5.00E+06	NA
3	3	LuxuryTie	UK	NULL	1800	40	15-12-2022	COMPLETED	UNKNOWN	NA	NA
4	4	GrandAffair	INDIA	7000000	NA	35	15-01-2023	DONE	KingCorp	7.00E+06	0015-01-20
5	5	RoyalWedding	INDIA	5000000	2000	30	28-02-2023	DONE	KingCorp	5.00E+06	28-02-2023
6	6	DiamondBond	FRANCE	-8000000	2500	45	10-03-2023	DONE	EliteGroup	8.00E+06	10-03-2023
7	7	GoldenCeremony	USA	9000000	3000	50	12-04-2023		GoldInc	9.00E+06	12-04-2023
8	8	PlatinumUnion	UK	10000000	3500	60	20-05-2023	DONE	UNKNOWN	1.00E+07	20-05-2023
9	9	SilverKnot	IND	8500000	2800	55	15-06-2023	DONE	SilverCorp	8.50E+06	15-06-2023
10	10	EliteUnion	USA	5M	1500	25	2023-02-30	DONE	QueenLtd	5.00E+06	NA

Explanation

R leverages the dplyr package to create a streamlined pipeline for data cleaning. Missing values are handled using ifelse() and is.na(), ensuring completeness. The abs() function corrects negative values, while as.numeric() converts cost into a usable numeric format. Text standardization is achieved using toupper() and trimws(). Date parsing is handled flexibly using multiple format attempts via tryFormats. Finally, distinct() ensures duplicate removal. This pipeline-style transformation improves readability, reproducibility, and debugging efficiency.

Key Points

• mutate() enables chained transformations
• distinct() handles duplicates efficiently
• Always validate date parsing

5. Phase 3: Additional SAS Enhancements

PROC MEANS DATA=costly_marriages_clean;

VAR Cost_Num Guests Age;

RUN;

OUTPUT:

The MEANS Procedure

Variable	N	Mean	Std Dev	Minimum	Maximum
Cost_Num Guests Age	9 9 10	6944444.44 2288.89 39.5000000	2006932.43 700.7932014 12.5720148	5000000.00 1500.00 25.0000000	10000000.00 3500.00 60.0000000

PROC FREQ DATA=costly_marriages_clean;

TABLES Country Status;

RUN;

OUTPUT:

The FREQ Procedure

Country	Frequency	Percent	Cumulative Frequency	Cumulative Percent
FRANCE	1	10.00	1	10.00
IND	1	10.00	2	20.00
INDIA	3	30.00	5	50.00
UK	2	20.00	7	70.00
USA	3	30.00	10	100.00

Status	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Frequency Missing = 1
COMPLETED	1	11.11	1	11.11
DONE	8	88.89	9	100.00

Explanation

After cleaning, validation and exploratory analysis are essential. PROC MEANS provides summary statistics, helping detect anomalies like unusually high costs or guest counts. PROC FREQ reveals distribution patterns in categorical variables such as Country and Status. These steps act as a secondary validation layer, ensuring that cleaning logic has not introduced inconsistencies. In clinical trials, similar procedures are used for SDTM validation checks before ADaM dataset derivation.

6. 20 Additional Data Cleaning Best Practices

1. Always validate raw data before transformation
2. Maintain audit trails for regulatory compliance
3. Never overwrite raw datasets
4. Use metadata-driven programming (CDISC)
5. Apply range checks (Age > 0)
6. Standardize units (currency, weight)
7. Use controlled terminology
8. Implement duplicate checks
9. Validate date sequences
10. Document all transformations
11. Perform cross-dataset validation
12. Use macros for repeatability
13. Log all warnings/errors
14. Validate derived variables
15. Perform QC independently
16. Use PROC COMPARE for validation
17. Avoid hardcoding values
18. Normalize categorical variables
19. Validate against SAP
20. Ensure reproducibility

7. Business Logic Behind Data Cleaning

Data cleaning is not just technical it’s strategic.

• Missing values: Replacing Sponsor with "UNKNOWN" avoids null-related errors in reporting
• Negative Age: Correcting ensures logical consistency
• Cost normalization: Enables accurate aggregation and comparisons
• Date correction: Critical for time-based analytics

In clinical trials:

• Wrong patient age → incorrect subgroup analysis
• Invalid dates → protocol deviations

8. 20 Key Insights (Sharp & Impactful)

• Dirty data leads to wrong conclusions
• Standardization ensures reproducibility
• Missing values hide insights
• Validation prevents regulatory rejection
• Clean data builds trust
• Dates drive timelines
• Duplicates distort metrics
• Text inconsistency breaks grouping
• Automation reduces human error
• QC is non-negotiable
• SAS excels in structured pipelines
• R excels in flexibility
• Documentation is critical
• Business logic drives cleaning
• Errors compound quickly
• Clean data accelerates decisions
• Data integrity is everything
• Consistency beats complexity
• Validation saves projects
• Clean data = credible analytics

9. SAS vs R – Comparison

Feature	SAS	R
Stability	High	Moderate
Flexibility	Moderate	High
Regulatory Use	Strong (CDISC)	Limited
Debugging	Easier logs	Requires skill
Performance	Optimized	Depends

10. Summary

The journey through the “Costly Marriages in the World” dataset highlights a fundamental truth in data analytics: even the most glamorous data can be deeply flawed beneath the surface. What initially appears to be high-value, insight-rich information quickly turns unreliable when plagued with inconsistencies such as missing values, invalid entries, duplicate records, and non-standard formats. This mirrors real-world scenarios in domains like clinical trials and financial analytics, where poor data quality can lead to misleading conclusions and regulatory risks.

Using SAS, we demonstrated a structured and industry-grade approach to data standardization leveraging functions like UPCASE, INPUT, ANYDTDTE., and procedures such as PROC SORT to systematically clean and validate data. SAS excels in controlled, reproducible environments, making it the preferred choice in regulated industries. In parallel, R showcased flexibility and efficiency through dplyr, enabling streamlined transformations using mutate(), case_when(), and distinct(). However, it also revealed subtle pitfalls like coercion warnings, reinforcing the need for careful function selection and validation.

Ultimately, data standardization is not just a technical step it is a strategic necessity. Clean, consistent data ensures accurate analysis, supports confident decision-making, and maintains compliance with industry standards. Whether working with billion-dollar weddings or clinical datasets, the principles remain the same: validate early, standardize rigorously, and document thoroughly. Mastering these practices distinguishes a competent programmer from a truly reliable data professional.

11. Conclusion

Data standardization is not optional it is foundational.

Whether you're analyzing billion-dollar weddings or clinical trial data, the principles remain the same:

• Clean early
• Validate continuously
• Document thoroughly

A structured cleaning framework transforms chaos into clarity and that’s what separates average analysts from elite SAS programmers.

12. Interview Questions

Q1: How do you handle inconsistent date formats in SAS?
A: Use ANYDTDTE. informat.

Q2: How to remove duplicates?
A: PROC SORT NODUPKEY BY variables;

Q3: R equivalent of SAS missing handling?
A: is.na() and ifelse()

Q4: Debugging scenario: Negative ages in dataset?
A: Use ABS() and validate source data.

Q5: Why standardization is critical in SDTM?
A: Ensures regulatory compliance and reproducibility.

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About the Author:

SAS Learning Hub is a data analytics and SAS programming platform focused on clinical, financial, and real-world data analysis. The content is created by professionals with academic training in Pharmaceutics and hands-on experience in Base SAS, PROC SQL, Macros, SDTM, and ADaM, providing practical and industry-relevant SAS learning resources.

Disclaimer:

The datasets and analysis in this article are created for educational and demonstration purposes only. Here we learn about WEDDING DATA.

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