Post on 14-Nov-2014
description
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
THANK YOU FOR
YOUR VALUABLE
TIME TO HEAR
US…..