281.SOFTWARE COMPANY ANALYSIS USING PROC CONTENTS | PROC PRINT | PROC MEANS | PROC FREQ | PROC SORT | PROC REPORT | PROC SQL | PROC REG

SOFTWARE COMPANY ANALYSIS USING PROC CONTENTS | PROC PRINT | PROC MEANS | PROC FREQ | PROC SORT | PROC REPORT | PROC SQL | PROC REG

/*Creating The Dataset Of Software Company */

1) Create the dataset

options nocenter;

data software_companies;

    infile datalines dsd truncover;

    length CompanyID 8 CompanyName $40 FoundedYear 8 Employees 8

           Revenue_MnUSD 8 PrimaryProduct $30 HQ_City $30 Valuation_MnUSD 8;

    input CompanyID CompanyName :$40. FoundedYear Employees Revenue_MnUSD

          PrimaryProduct :$30. HQ_City :$30. Valuation_MnUSD;

    RevenuePerEmployee = round((Revenue_MnUSD*1000000)/max(Employees,1),0.01);

    format RevenuePerEmployee comma12.2 Revenue_MnUSD Valuation_MnUSD comma12.2;

    datalines;

1,BlueOrbit Solutions,2008,120,18.5,Cloud ERP,Hyderabad,120.0

2,NeuraCode Labs,2015,45,6.2,AI Analytics,Bengaluru,32.0

3,StackWave Systems,2000,520,210.3,DevOps Platform,Pune,950.0

4,PixelForge Studio,2012,30,2.1,Mobile Games,Mumbai,11.5

5,QuantumBridge,2018,80,25.0,Blockchain Infra,Chennai,140.0

6,SafeGuard Tech,2005,220,55.4,Cybersecurity,New Delhi,410.0

7,DataSpring Inc,2010,150,38.0,Data Integration,Bengaluru,210.0

8,CloudVista,2014,300,90.7,Cloud Hosting,Hyderabad,480.0

9,EdgeLeap Systems,2020,25,1.0,Edge IoT,Coimbatore,6.5

10,OpenStream Soft,1998,900,512.3,Streaming Infra,Mumbai,3200.0

11,GreenByte Solutions,2003,60,7.4,Enterprise SaaS,Ahmedabad,45.0

12,MonoTech Labs,2016,12,0.4,Prototype Tools,Visakhapatnam,2.0

;

run;

proc print;run;

Output:

Obs	CompanyID	CompanyName	FoundedYear	Employees	Revenue_MnUSD	PrimaryProduct	HQ_City	Valuation_MnUSD	RevenuePerEmployee
1	1	BlueOrbit Solutions	2008	120	18.50	Cloud ERP	Hyderabad	120.00	154,166.67
2	2	NeuraCode Labs	2015	45	6.20	AI Analytics	Bengaluru	32.00	137,777.78
3	3	StackWave Systems	2000	520	210.30	DevOps Platform	Pune	950.00	404,423.08
4	4	PixelForge Studio	2012	30	2.10	Mobile Games	Mumbai	11.50	70,000.00
5	5	QuantumBridge	2018	80	25.00	Blockchain Infra	Chennai	140.00	312,500.00
6	6	SafeGuard Tech	2005	220	55.40	Cybersecurity	New Delhi	410.00	251,818.18
7	7	DataSpring Inc	2010	150	38.00	Data Integration	Bengaluru	210.00	253,333.33
8	8	CloudVista	2014	300	90.70	Cloud Hosting	Hyderabad	480.00	302,333.33
9	9	EdgeLeap Systems	2020	25	1.00	Edge IoT	Coimbatore	6.50	40,000.00
10	10	OpenStream Soft	1998	900	512.30	Streaming Infra	Mumbai	3,200.00	569,222.22
11	11	GreenByte Solutions	2003	60	7.40	Enterprise SaaS	Ahmedabad	45.00	123,333.33
12	12	MonoTech Labs	2016	12	0.40	Prototype Tools	Visakhapatnam	2.00	33,333.33

2) Basic exploration with PROCs 

PROC CONTENTS — Purpose: Show dataset structure and variable attributes in one view.

proc contents data=software_companies; 

run;

Output:

The CONTENTS Procedure

Data Set Name	WORK.SOFTWARE_COMPANIES	Observations	12
Member Type	DATA	Variables	9
Engine	V9	Indexes	0
Created	09/06/2025 18:43:25	Observation Length	152
Last Modified	09/06/2025 18:43:25	Deleted Observations	0
Protection		Compressed	NO
Data Set Type		Sorted	NO
Label
Data Representation	SOLARIS_X86_64, LINUX_X86_64, ALPHA_TRU64, LINUX_IA64
Encoding	utf-8 Unicode (UTF-8)

Engine/Host Dependent Information
Data Set Page Size	131072
Number of Data Set Pages	1
First Data Page	1
Max Obs per Page	861
Obs in First Data Page	12
Number of Data Set Repairs	0
Filename	/saswork/SAS_work47260000B4E9_odaws01-apse1-2.oda.sas.com/SAS_work70210000B4E9_odaws01-apse1-2.oda.sas.com/software_companies.sas7bdat
Release Created	9.0401M8
Host Created	Linux
Inode Number	134456831
Access Permission	rw-r--r--
Owner Name	u63247146
File Size	256KB
File Size (bytes)	262144

Alphabetic List of Variables and Attributes
#	Variable	Type	Len	Format
1	CompanyID	Num	8
2	CompanyName	Char	40
4	Employees	Num	8
3	FoundedYear	Num	8
7	HQ_City	Char	30
6	PrimaryProduct	Char	30
9	RevenuePerEmployee	Num	8	COMMA12.2
5	Revenue_MnUSD	Num	8	COMMA12.2
8	Valuation_MnUSD	Num	8	COMMA12.2

PROC PRINT — Purpose: Print the raw observations for quick manual inspection.

proc print data=software_companies noobs;

run;

Output:

CompanyID	CompanyName	FoundedYear	Employees	Revenue_MnUSD	PrimaryProduct	HQ_City	Valuation_MnUSD	RevenuePerEmployee
1	BlueOrbit Solutions	2008	120	18.50	Cloud ERP	Hyderabad	120.00	154,166.67
2	NeuraCode Labs	2015	45	6.20	AI Analytics	Bengaluru	32.00	137,777.78
3	StackWave Systems	2000	520	210.30	DevOps Platform	Pune	950.00	404,423.08
4	PixelForge Studio	2012	30	2.10	Mobile Games	Mumbai	11.50	70,000.00
5	QuantumBridge	2018	80	25.00	Blockchain Infra	Chennai	140.00	312,500.00
6	SafeGuard Tech	2005	220	55.40	Cybersecurity	New Delhi	410.00	251,818.18
7	DataSpring Inc	2010	150	38.00	Data Integration	Bengaluru	210.00	253,333.33
8	CloudVista	2014	300	90.70	Cloud Hosting	Hyderabad	480.00	302,333.33
9	EdgeLeap Systems	2020	25	1.00	Edge IoT	Coimbatore	6.50	40,000.00
10	OpenStream Soft	1998	900	512.30	Streaming Infra	Mumbai	3,200.00	569,222.22
11	GreenByte Solutions	2003	60	7.40	Enterprise SaaS	Ahmedabad	45.00	123,333.33
12	MonoTech Labs	2016	12	0.40	Prototype Tools	Visakhapatnam	2.00	33,333.33

PROC MEANS — Purpose: Get numeric summary statistics (N, mean, std, min, max) for numeric variables.

proc means data=software_companies n mean std min max maxdec=2;

    var FoundedYear Employees Revenue_MnUSD Valuation_MnUSD RevenuePerEmployee;

run;

Output:

The MEANS Procedure

Variable	N	Mean	Std Dev	Minimum	Maximum
FoundedYear Employees Revenue_MnUSD Valuation_MnUSD RevenuePerEmployee	12 12 12 12 12	2009.92 205.17 80.61 467.25 221020.10	7.18 263.59 148.48 904.66 160566.47	1998.00 12.00 0.40 2.00 33333.33	2020.00 900.00 512.30 3200.00 569222.22

PROC FREQ — Purpose: Frequency counts for categorical variables to see distribution.

proc freq data=software_companies;

    tables PrimaryProduct HQ_City / nocum nopercent;

run;

Output:

The FREQ Procedure

PrimaryProduct	Frequency
AI Analytics	1
Blockchain Infra	1
Cloud ERP	1
Cloud Hosting	1
Cybersecurity	1
Data Integration	1
DevOps Platform	1
Edge IoT	1
Enterprise SaaS	1
Mobile Games	1
Prototype Tools	1
Streaming Infra	1

HQ_City	Frequency
Ahmedabad	1
Bengaluru	2
Chennai	1
Coimbatore	1
Hyderabad	2
Mumbai	2
New Delhi	1
Pune	1
Visakhapatnam	1

PROC SORT — Purpose: Sort dataset by metric (e.g., Revenue) for reporting.

proc sort data=software_companies out=swc_sorted_by_revenue;

    by descending Revenue_MnUSD;

run;

proc print data=swc_sorted_by_revenue;run;

Output:

Obs	CompanyID	CompanyName	FoundedYear	Employees	Revenue_MnUSD	PrimaryProduct	HQ_City	Valuation_MnUSD	RevenuePerEmployee
1	10	OpenStream Soft	1998	900	512.30	Streaming Infra	Mumbai	3,200.00	569,222.22
2	3	StackWave Systems	2000	520	210.30	DevOps Platform	Pune	950.00	404,423.08
3	8	CloudVista	2014	300	90.70	Cloud Hosting	Hyderabad	480.00	302,333.33
4	6	SafeGuard Tech	2005	220	55.40	Cybersecurity	New Delhi	410.00	251,818.18
5	7	DataSpring Inc	2010	150	38.00	Data Integration	Bengaluru	210.00	253,333.33
6	5	QuantumBridge	2018	80	25.00	Blockchain Infra	Chennai	140.00	312,500.00
7	1	BlueOrbit Solutions	2008	120	18.50	Cloud ERP	Hyderabad	120.00	154,166.67
8	11	GreenByte Solutions	2003	60	7.40	Enterprise SaaS	Ahmedabad	45.00	123,333.33
9	2	NeuraCode Labs	2015	45	6.20	AI Analytics	Bengaluru	32.00	137,777.78
10	4	PixelForge Studio	2012	30	2.10	Mobile Games	Mumbai	11.50	70,000.00
11	9	EdgeLeap Systems	2020	25	1.00	Edge IoT	Coimbatore	6.50	40,000.00
12	12	MonoTech Labs	2016	12	0.40	Prototype Tools	Visakhapatnam	2.00	33,333.33

PROC REPORT — Purpose: Create a compact summary table combining computed columns for reporting.

proc report data=swc_sorted_by_revenue nowd;

    columns CompanyID CompanyName HQ_City Employees Revenue_MnUSD RevenuePerEmployee Valuation_MnUSD;

    define CompanyName / display width=30;

    define Revenue_MnUSD / analysis format=comma12.2;

    define RevenuePerEmployee / analysis format=comma12.2;

run;

Output:

CompanyID	CompanyName	HQ_City	Employees	Revenue_MnUSD	RevenuePerEmployee	Valuation_MnUSD
10	OpenStream Soft	Mumbai	900	512.30	569,222.22	3,200.00
3	StackWave Systems	Pune	520	210.30	404,423.08	950.00
8	CloudVista	Hyderabad	300	90.70	302,333.33	480.00
6	SafeGuard Tech	New Delhi	220	55.40	251,818.18	410.00
7	DataSpring Inc	Bengaluru	150	38.00	253,333.33	210.00
5	QuantumBridge	Chennai	80	25.00	312,500.00	140.00
1	BlueOrbit Solutions	Hyderabad	120	18.50	154,166.67	120.00
11	GreenByte Solutions	Ahmedabad	60	7.40	123,333.33	45.00
2	NeuraCode Labs	Bengaluru	45	6.20	137,777.78	32.00
4	PixelForge Studio	Mumbai	30	2.10	70,000.00	11.50
9	EdgeLeap Systems	Coimbatore	25	1.00	40,000.00	6.50
12	MonoTech Labs	Visakhapatnam	12	0.40	33,333.33	2.00

3) PROC SQL examples 

PROC SQL — Purpose: Create aggregated summary 

                    (total revenue and avg employees by HQ\_City).

proc sql;

    create table city_summary as

    select HQ_City,

           count(*) as NumCompanies,

           sum(Revenue_MnUSD) as TotalRevenue_MnUSD format=comma12.2,

           mean(Employees) as AvgEmployees format=8.2

    from software_companies

    group by HQ_City

    order by calculated TotalRevenue_MnUSD desc;

quit;

proc print data=city_summary;run;

Output:

Obs	HQ_City	NumCompanies	TotalRevenue_MnUSD	AvgEmployees
1	Mumbai	2	514.40	465.00
2	Pune	1	210.30	520.00
3	Hyderabad	2	109.20	210.00
4	New Delhi	1	55.40	220.00
5	Bengaluru	2	44.20	97.50
6	Chennai	1	25.00	80.00
7	Ahmedabad	1	7.40	60.00
8	Coimbatore	1	1.00	25.00
9	Visakhapatnam	1	0.40	12.00

PROC SQL — Purpose: Select top 5 companies by valuation.

proc sql outobs=5;

    select CompanyID, CompanyName, Valuation_MnUSD

    from software_companies

    order by Valuation_MnUSD desc;

quit;

Output:

CompanyID	CompanyName	Valuation_MnUSD
10	OpenStream Soft	3,200.00
3	StackWave Systems	950.00
8	CloudVista	480.00
6	SafeGuard Tech	410.00
7	DataSpring Inc	210.00

PROC SQL — Purpose: Inner join with a simulated investment table to show merge via SQL.

data investments;

    infile datalines dsd truncover;

    length CompanyID 8 Investor $40 Investment_MnUSD 8 Round $10;

    input CompanyID Investor :$40. Investment_MnUSD Round :$10.;

    datalines;

1,AlphaVentures,5.0,SeriesA

2,NeuroFund,2.0,Seed

3,InfraCap,50.0,SeriesC

6,ShieldPartners,20.0,SeriesB

8,CloudHoldings,100.0,SeriesD

10,StreamGlobal,200.0,SeriesF

7,DataSeed,10.0,SeriesB

;

run;

proc print;run;

Output:

Obs	CompanyID	Investor	Investment_MnUSD	Round
1	1	AlphaVentures	5	SeriesA
2	2	NeuroFund	2	Seed
3	3	InfraCap	50	SeriesC
4	6	ShieldPartners	20	SeriesB
5	8	CloudHoldings	100	SeriesD
6	10	StreamGlobal	200	SeriesF
7	7	DataSeed	10	SeriesB

proc sql;

    create table company_investments as

    select a.*, b.Investor, b.Investment_MnUSD, b.Round

    from software_companies as a

    left join investments as b

    on a.CompanyID = b.CompanyID;

quit;

proc print data=company_investments;run;

Output:

Obs	CompanyID	CompanyName	FoundedYear	Employees	Revenue_MnUSD	PrimaryProduct	HQ_City	Valuation_MnUSD	RevenuePerEmployee	Investor	Investment_MnUSD	Round
1	1	BlueOrbit Solutions	2008	120	18.50	Cloud ERP	Hyderabad	120.00	154,166.67	AlphaVentures	5	SeriesA
2	2	NeuraCode Labs	2015	45	6.20	AI Analytics	Bengaluru	32.00	137,777.78	NeuroFund	2	Seed
3	3	StackWave Systems	2000	520	210.30	DevOps Platform	Pune	950.00	404,423.08	InfraCap	50	SeriesC
4	4	PixelForge Studio	2012	30	2.10	Mobile Games	Mumbai	11.50	70,000.00		.
5	5	QuantumBridge	2018	80	25.00	Blockchain Infra	Chennai	140.00	312,500.00		.
6	6	SafeGuard Tech	2005	220	55.40	Cybersecurity	New Delhi	410.00	251,818.18	ShieldPartners	20	SeriesB
7	7	DataSpring Inc	2010	150	38.00	Data Integration	Bengaluru	210.00	253,333.33	DataSeed	10	SeriesB
8	8	CloudVista	2014	300	90.70	Cloud Hosting	Hyderabad	480.00	302,333.33	CloudHoldings	100	SeriesD
9	9	EdgeLeap Systems	2020	25	1.00	Edge IoT	Coimbatore	6.50	40,000.00		.
10	10	OpenStream Soft	1998	900	512.30	Streaming Infra	Mumbai	3,200.00	569,222.22	StreamGlobal	200	SeriesF
11	11	GreenByte Solutions	2003	60	7.40	Enterprise SaaS	Ahmedabad	45.00	123,333.33		.
12	12	MonoTech Labs	2016	12	0.40	Prototype Tools	Visakhapatnam	2.00	33,333.33		.

4) SAS MACROS

Macro: %top_companies_by_metric

— Purpose: Reusable macro to print top N companies by any numeric metric.

%macro top_companies_by_metric(metric=Revenue_MnUSD, n=5);

    %put NOTE: Running top_companies_by_metric for &metric, top &n.;

    proc sql outobs=&n;

        select CompanyID, CompanyName, &metric

        from software_companies

        order by &metric desc;

    quit;

%mend top_companies_by_metric;

%top_companies_by_metric(metric=Valuation_MnUSD,n=5);

Output:

CompanyID	CompanyName	Valuation_MnUSD
10	OpenStream Soft	3,200.00
3	StackWave Systems	950.00
8	CloudVista	480.00
6	SafeGuard Tech	410.00
7	DataSpring Inc	210.00

%top_companies_by_metric(metric=Revenue_MnUSD,n=3);

Output:

CompanyID	CompanyName	Revenue_MnUSD
10	OpenStream Soft	512.30
3	StackWave Systems	210.30
8	CloudVista	90.70

Macro: %segment_filter 

— Purpose: Create a dataset for companies founded before/after a given year.

%macro segment_filter(year=2010, outds=before_after);

    %if &year = %then %let year=2010;

    data &outds;

        set software_companies;

        if FoundedYear < &year then Segment = 'Before_&year';

        else Segment = 'After_&year';

    run;

    proc freq data=&outds; tables Segment / nocum nopercent; run;

%mend segment_filter;

%segment_filter(year=2010,outds=seg_2010);

Output:

The FREQ Procedure

Segment	Frequency
After_&year	7
Before_&year	5

Macro: %calc_growth_estimate

— Purpose: Estimate simple hypothetical valuation growth over X years at assumed CAGR.

%macro calc_growth_estimate(cagr=0.20, years=3);

    data growth_estimates;

        set software_companies;

        FutureValuation = round(Valuation_MnUSD * (1 + &cagr)**&years,0.1);

        CAGR = &cagr;

        Years = &years;

    run;

    proc sort data=growth_estimates; by descending FutureValuation; run;

    proc print data=growth_estimates (obs=12) noobs;

        var CompanyName Valuation_MnUSD FutureValuation Years CAGR;

    run;

%mend calc_growth_estimate;

%calc_growth_estimate(cagr=0.25, years=5);

Output:

CompanyName	Valuation_MnUSD	FutureValuation	Years	CAGR
OpenStream Soft	3,200.00	9765.6	5	0.25
StackWave Systems	950.00	2899.2	5	0.25
CloudVista	480.00	1464.8	5	0.25
SafeGuard Tech	410.00	1251.2	5	0.25
DataSpring Inc	210.00	640.9	5	0.25
QuantumBridge	140.00	427.2	5	0.25
BlueOrbit Solutions	120.00	366.2	5	0.25
GreenByte Solutions	45.00	137.3	5	0.25
NeuraCode Labs	32.00	97.7	5	0.25
PixelForge Studio	11.50	35.1	5	0.25
EdgeLeap Systems	6.50	19.8	5	0.25
MonoTech Labs	2.00	6.1	5	0.25

5) Validation & QC checks 

Purpose: Ensure data sanity

proc sort data=software_companies out=swc_dup nodupkey dupout=dups;

 by CompanyID;

run;

proc print data=dups;

 title 'Duplicate CompanyIDs if any';

run;

Log:

NOTE: 0 observations with duplicate key values were deleted.

proc freq data=software_companies; 

 tables Employees / missing; 

run;

Output:

The FREQ Procedure

Employees	Frequency	Percent	Cumulative Frequency	Cumulative Percent
12	1	8.33	1	8.33
25	1	8.33	2	16.67
30	1	8.33	3	25.00
45	1	8.33	4	33.33
60	1	8.33	5	41.67
80	1	8.33	6	50.00
120	1	8.33	7	58.33
150	1	8.33	8	66.67
220	1	8.33	9	75.00
300	1	8.33	10	83.33
520	1	8.33	11	91.67
900	1	8.33	12	100.00

CountNegEmployees
0

proc sql;

    select count(*) as CountNegEmployees from software_companies

    where Employees < 0 or Revenue_MnUSD < 0 or Valuation_MnUSD < 0;

quit;

Output:

6) Example advanced analytics 

Purpose: simple linear regression of valuation on revenue & employees

proc reg data=software_companies;

    model Valuation_MnUSD = Revenue_MnUSD Employees;

    title 'Simple regression: Valuation predicted by Revenue and Employees';

run; quit;

Output:

Simple regression: Valuation predicted by Revenue and Employees

The REG Procedure

Model: MODEL1

Dependent Variable: Valuation_MnUSD

Number of Observations Read	12
Number of Observations Used	12

Analysis of Variance
Source	DF	Sum of Squares	Mean Square	F Value	Pr > F
Model	2	8923884	4461942	511.09	<.0001
Error	9	78573	8730.32769
Corrected Total	11	9002457

Root MSE	93.43622	R-Square	0.9913
Dependent Mean	467.25000	Adj R-Sq	0.9893
Coeff Var	19.99705

Parameter Estimates
Variable	DF	Parameter Estimate	Standard Error	t Value	Pr > |t|
Intercept	1	56.10443	46.93671	1.20	0.2625
Revenue_MnUSD	1	8.11022	0.96438	8.41	<.0001
Employees	1	-1.18248	0.54324	-2.18	0.0575

Simple regression: Valuation predicted by Revenue and Employees

The REG Procedure

Model: MODEL1

Dependent Variable: Valuation_MnUSD

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