Human Resource Management

• Sumit Singh• Rohit Rana• Hardeep Kumar• Dhawal Tandel• Mayank Singh• Saurabh Patil• Abhay Singh• Anwaar Khan

What is Data Warehouse???

• According to W.H. Inmon “A data warehouse is:– Subject Oriented– Integrated– Time-variant– Non volatile storage

collection of data in support of management decision making process.

Database Lifecycle

• Analyze the company situation:– Analysis means to break up any whole into parts so as to find

out their nature, functions and so on.• These issues must be resolved:

– What is the organization general operating environment and what is its mission within that environment.

– What is the organization structure?• Knowing who controls what and who reports to whom

is quite useful when you are trying to define required information.

• flows, specific reports and query formats.

Human Resource Management Database….

HiringRecruitingEducation& Training

Retrention BenefitAdministration

PotentialEmployees Employees Retired

Employees

Data Management

Primary HR Activities

Accountinginformation

system

Human resourcesresearchsystem

Manufacturingintelligencesubsystem

Work forceplanning

subsystem

Work forcemanagement

subsystem

Benefitssubsystem

Users

Data Information

Environmentalreporting

subsystem

HRMSDatabase

HRMS Model

Recruitingsubsystem

CompensationSubsystem

Define problem and constraints

• Some of the questions to be answered are:– Has some kind of system in place already?– How does the existing system function?– What input does the system require?– What documents does the system generate?– How is the system output used?– by whom?

Data warehouse Requirements for HRMS

• The highest priority business requirement is to – track and analyze the employee transactions

events accurately• It should provide the answer to every possible

employee profile inquiry.

What are the Objectives???• The initial objective might be to create an efficient

inventory query and management system.• The database system must be designed to help solve at

least the major problems identified during the problem discovery process.

• The database designer must begin to address the following questions:

• Will the system interface with other existing or future systems in the company?

• Will the system share the data with the other systems or users?

Define scope and boundaries

• The designer must recognize the existence of two sets of limits:– Scope – Boundaries

• Scope defines the extent of the design according to the operational requirements.

• The proposed system is also subject to Boundaries. Which are external to the system. Boundaries are also imposed by existing hardware and software.

Dimension Analysis…

• Dimensions are qualifying characteristics that provide additional perspectives to a given fact.– For eg:- HRMS has employee, transaction, job,

salary, department etc dimensions..• Such dimensions are normally stored in

Dimension Tables.

Example of Dimension Table…

Employee Dimension

Emp_idDept_idFirst_nameLast_nameEmailPh_noHire_dateJob_idSalary

Employee Dimension table…

• Each employee has a detailed HR profile with some attributes including date of hire, job grade, salary, review dates, review outcomes, insurance plan and may others.

• Employees are constantly being hired, transferred and promoted as well as adjusting their profiles in a variety of ways. The measurements associated with the employee transaction are the changes made to the employee profile such as a new address or job grade promotion.

What are the Facts???

• Facts are numeric measurements (values) that represent specific business aspect or activity.

• Facts commonly used in business data analysis are units, cost, prices and revenues.

• Facts are normally stored in fact table• Fact tables are updated periodically with data

form operational databases.

Example of fact table..

Fact Table

Loc_idDept_idEmp_idJob_id

• Star schema– The star schema is a data modeling technique use to map

multidimensional decision support data into a relational database.

– Reason for developing star schema is that existing relational modeling techniques, ER and normalization did not yield a database structure, that served the advanced data analysis requirement .

– The basis star schema has four components:• Facts• Dimensions• Attributes• Attribute hierarchies

Data warehouse Schema…

Star Schema for HRMS…

Location Dimension

Loc_idStreet_addrPost_codeCityCont_id

Salary Dimension

DateEmp_idTime_inTime_out

Department Dimensions

Loc_idDept_idDept_nameMgr_id

Performance Dimension

Emp_idPrev_yr_achRatin_pmRatin_tlSelf_ratinFinal_grd

Employee Dimension

Emp_idDept_idFirst_nameLast_nameEmailPh_noHire_dateJob_idSalary

Job Dimension

Job_idjob_titleSalaryFact Table

Loc_idDept_idEmp_idJob_idEmp_countTransfer_count

Employee Transaction Type

Emp_tr_typ_keyTran_descTr_dateTr_time

Employee Fact Table

Emp_idEmp_tr_typ_key(FK)Emp_typeEmp_nameEmp_addr

Attendance Dimension

DateEmp_idTime_inTime_out

Schema Contd……

Fact Constellation…

Location Dimension

Loc_idStreet_addrPost_codeCityCont_id

Salary Dimension

Emp_idDateJob_id

Fact Table

Emp_tr_typ_keyLoc_idDept_idEmp_idJob_id

Department Dimensions

Loc_idDept_idDept_nameMgr_id

Performance Dimension

Emp_idPrev_yr_achRatin_pmRatin_tlSelf_ratinFinal_grd

Attendance

DateEmp_idTime_inTime_out

Job Dimension

Job_idjob_titleSalary

Emp Dimension Fact

Emp_idEmp_tr_typ_key

Employee transaction Type

Emp_tr_typ_keyTran_descTr_dateTr_time

Snow Flake Schema…

Location Dim

Loc_idStreet_addrPost_codeCitykey

Salary Dim

DateEmp_idTime_inTime_out

Department Dim

Loc_idDept_idDept_nameMgr_id

Performance Dim

Emp_idPrev_yr_achRatin_pmRatin_tlSelf_ratinFinal_grd

Employee Dim

Emp_idEmp_typeDept_idFirst_nameLast_nameEmailPh_noHire_dateJob_idSalary

Job Dimension

Job_idjob_titleSalaryFact Table

Loc_idDept_idEmp_idJob_idEmp_countTransfer_count

City Dimension

CityCitykeyProvincecountry

Potential emp dim

Emp_idhire_dateAppl_no

Retired Emp Dim

Emp_idretr_datePension

PC

Data Cube

Date

Trans

actio

n Typ

e

Dep

artm

ent

Retrented

HiredPromoted

1Qtr 2Qtr 3Qtr 4Qtr

Manufacturing

Finance

Sales

Slice and DiceTra

nsac

tion

Type

Dep

artm

ent

Retrented

Hired

Promoted Manufacturing

Finance

Sales

PC

Date 1Qtr 2Qtr 3Qtr 4Qtr

Employee Transaction in Manufacturing

Employee Promoted

Employee Transaction in 3rd Qtr

Employee Transcation in Manufacturing(3rd Qtr)

Employees promoted in manufacturing(3rd Qtr )

Roll up (drill-up) (dimension reduction example)

Date

sum

1Qtr 2Qtr 3Qtr 4Qtr

Group by date, department

- reduced product dimension

Group by department

- reduced date dimension

Trans

actio

n Typ

e

Dep

artm

ent

Retrented

HiredPromoted Manufacturing

Finance

Sales

Roll up (drill-up)

Group by transaction, departmentDate

sum

sum 1Qtr 2Qtr 3Qtr 4Qtr

sum

Gro

up b

y da

te, d

epar

tmen

t

Group by transaction

Group by dateALL

Group by department

Trans

actio

n Typ

e

Dep

artm

ent

Retrented

HiredPromoted Manufacturing

Finance

Sales

Drill down (roll-down)

Date 1Qtr 2Qtr 3Qtr 4Qtr

Gro

up b

y da

te, d

epar

tmen

t

ALL

Group by department

Trans

actio

n Typ

e

Dep

artm

ent

Retrented

Hired

Promoted Manufacturing

Finance

Sales

Advantages & Disadvantage

• Advantages of Using the Snowflake Schema• The snowflake schema:– In some cases may improve performance because smaller

tables are joined, which is easier to maintain,– It also increases flexibility.

• Disadvantages of Using the Snowflake Schema• The snowflake schema:– increases the number of tables an end-user must work with.– makes the queries much more difficult to create because

more tables need to be joined.

Conclusion

• Thus we conclude that “the schema that is more appropriate for HRMS data warehouse is the snow flake schema”.

References…

Database Management Systems, Rob Coronell

Data Warehousing, Nagbhushan

Datawarehousing toolkit, by Ross

www.sas.com

www.google.co.in

IT tool Box

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