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WILM Report Strategy

Datamining WILM 09/19 - WordPress.com

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Page 1: Datamining WILM 09/19 - WordPress.com

WILM Report Strategy

Page 2: Datamining WILM 09/19 - WordPress.com

WILM – Reporting Strategy

PeopleSoft – lost many or all reports from legacy

Report Strategy -The strategy for WILM reporting is to develop and deploy queries and reports in a timely manner that meets the needs of all the WILM users.

Page 3: Datamining WILM 09/19 - WordPress.com

WILM – Reporting Strategy

Active Supportive

High Usage

Low Usage

Categories of Managed DataCategories of Managed Data

Real-Time Operations

Data Reported through Query

Data Repositories

REPORTING

Data Warehouse

* *

Recovery & Active Archiving

Data Recovered from Microfiche

Data Mining

Data Mining (Automotion of

patterms, trends, etc.)

Page 4: Datamining WILM 09/19 - WordPress.com

WILM – Reporting Strategy

Peoplesoft

Live Data

RDS SA/HR

Last Nights Data

Data Warehouse

Historical

Data

WILM Database Infrastructure

Peoplesoft Data Base Peoplesoft RDS Data Base WILM Data Warehouse Data Warehouse Actual “Live” Data Data replaced Nightly extract Historical Data Cubes 1,000’s of data fields 4,000+ data elements from 600+ Tables

COGNOS Decision Stream

Extraction

COGNOS Decision Stream

Extraction Tool

Data Warehouse

Cube

Date Warehouse

Cube

Page 5: Datamining WILM 09/19 - WordPress.com

WILM – Reporting Strategy

RDS – Reporting Database Service

“Free the Data!!”

• RDS• SA/HR

• Last Nights Data

Page 6: Datamining WILM 09/19 - WordPress.com

WILM – Reporting Strategy

Decisions Information

Operation

Strategic

Unscheduled Summarized Infrequent Wide Scope

Prespecified Scheduled Detailed Frequent Internal Narrow Focus

Information Characteristics Decision

Structure

Unstructured

Structured

Management

Operational

Strategic

Page 7: Datamining WILM 09/19 - WordPress.com

WILM – Reporting StrategyT ra n sa c tio n R e po rtin g :

P S /Q u e ry

se cu re , fa s t, e a sy , a d -h o c a cce ss to p rod u c t io n d a ta‘re su lt se t ’ p a ssed to re p o rt in g to o ls

Page 8: Datamining WILM 09/19 - WordPress.com

WILM – Reporting Strategy

T ra n sa c t io n R e p o rt in g : C ry s ta l R ep o rts

F a s t a n d e a sy

R e p o rts , lis ts , la b e ls ,c ro s s ta b s , m a il-m e rg e

E x te n s iv e ca lcu la t io n s

S o rt in g /g ro u p in g

T o ta ls a n d su b to ta ls

E -m a il, W e b

Page 9: Datamining WILM 09/19 - WordPress.com

WILM – Reporting Strategy

S o … W h a t ’s T h e Is su e ?

Q u e ry a n d C ry s ta l a re e x ce lle n t to o ls

S o … w h y is re p o r t in g s lo w a n d co m p le x ?

D a ta S tru c tu re s !d e s ig n e d fo r tra n sa c t io n s - - n o t re p o rt in g d a ta b a se s a re n o rm a lize d re p o rt in g to o ls w o rk w e ll w ith fe w ta b le sre p o rt in g fro m m an y ta b le s is co m p le x a n d s lo w

E xa m p le … a C la s s L is t R e p o rt

Page 10: Datamining WILM 09/19 - WordPress.com

WILM – Reporting Strategy

D a ta S tru c tu re C h a lle n g e : T h e C la ss L is t R ep o rt

w h a t d a ta d o y o u w a n t?

c la s s d a ta , m e e tin g t im e s , in s tru c to r, b u ild in g , s tu d e n t, c a re e r, m a jo r, ...

lo ca te th e ta b le s th a t co n ta in th o se d a ta ite m s

se le c t d e s ire d d a ta f ie ld s

Page 11: Datamining WILM 09/19 - WordPress.com

WILM – Reporting Strategy

D a ta S tru c tu re C h a lle n g e : T h e C la ss L is t R ep o rt

m a n y f ie ld s a re co d e s

d e sc r ip t io n s in X LA T s , E d it T a b le s

th e se a re “E ffe c t iv e D a te d ”

q u e r ie s n e e d JO IN s a n d E ffe c t iv e D a te lo g ic

Page 12: Datamining WILM 09/19 - WordPress.com

WILM – Reporting Strategy

D a ta S tru c tu re C h a lle n g e : T h e C la s s L is t R e p o rt

H o w m a n y JO IN s?

Y o u w a n t m o red a ta o n th is re p o r t?

se s s io n , m e e t in g t im e s ?

Page 13: Datamining WILM 09/19 - WordPress.com

WILM – Reporting Strategy

D a ta S tru c tu re C h a lle n ge : T h e C la ss L is t R ep o rt

m o re d a te = m o re ta b le s

s t ill m o re d a ta n ee d ed ?

ro o m n u m b e r, lo ca tio n , in s tru c to r, ...

Page 14: Datamining WILM 09/19 - WordPress.com

WILM – Reporting Strategy

D a ta S tru c tu re C h a lle n ge : T h e C la ss L is t R ep o rtit ’s lo o k in g co m p le x … 1 3 JO IN s so fa r

o u te r JO IN s , E ffe c tiv e D a te d

o h … yo u w a n te d s tu d en t d a ta o n th a t C la ss L is t R ep o rt?

Page 15: Datamining WILM 09/19 - WordPress.com

WILM – Reporting Strategy

D a ta S tru c tu re C h a lle n ge : T h e C la ss L is t R ep o rt

P S /Q u e ry g oo d fo r fe w tab le sm an ag e m en t re p o rts n ee d m an y tab le sp ro g ram m in g to o ls lik e S Q R a re n ee d e d

Page 16: Datamining WILM 09/19 - WordPress.com

WILM – Reporting Strategy

H ig h e r E d u ca tio n R ep o rtin g D a ta M a rt (R D M ): Fa s t, E a sy , A ffo rd ab le

n ig h tly e x trac t

h ig h e r e d u c a tio n

r e p o r tin g d a ta m a r t (R D M )

tr a n sa c tio n a p p lic a tio n

n igh tly e x tra c ts from tran sac t io n sy s temload d a ta in to R e po rtin g D a ta M a rt (R D M )d a ta m a rt o p tim ize d fo r re p o rt in g

Page 17: Datamining WILM 09/19 - WordPress.com

WILM – Reporting Strategy

H ig h e r E d u ca t io n R ep o rtin g D a ta M a rt (R D M ): F a s t , E a sy , A ffo rd a b le

STUDENT DIMENSIONSTU_PKSTU_DATE_TIMESTU_EMPLIDSTU_SSNSTU_NAMESTU_GENDERSTU_BIRTHDATESTU_CAMPUS_EMAIL_CDSTU_CAMPUS_EMAILSTU_CAMPUS_ADDR_CDSTU_CAMPUS_ADDR1STU_CAMPUS_ADDR2STU_CAMPUS_ADDR3STU_CAMPUS_CITYSTU_CAMPUS_STATESTU_CAMPUS_ZIPSTU_CAMPUS_PHONE_CDSTU_CAMPUS_PHONESTU_CAMPUS_PHONE_EXTSTU_HOME_EMAIL_CDSTU_HOME_EMAILSTU_HOME_ADDR_CDSTU_HOME_ADDR1STU_HOME_ADDR2STU_HOME_ADDR3STU_HOME_CITYSTU_HOME_STATESTU_HOME_ZIPSTU_HOME_PHONE_CDSTU_HOME_PHONESTU_HOME_PHONE_EXTSTU_CITZ_STATUS_CDSTU_CITZ_SDESCSTU_CITZ_LDESCSTU_ETHNIC_CDSTU_ETHNIC_PRIMESTU_ETNHIC_PRIME_SDESCSTU_ETHNIC_PRIME_LDESCSTU_ADMIS_EXT_PKSTU_FINAID_EXT_PKSTU_STUFIN_EXT_PKSTU_CITZ_LDESCSTU_ETHNIC_CDSTU_ETHNIC_PRIMESTU_ETNHIC_PRIME_SDESCSTU_ETHNIC_PRIME_LDESCSTU_ADMIS_EXT_PKSTU_FINAID_EXT_PKSTU_STUFIN_EXT_PK CLASS DIMENSION

CLS_PKCLS_DATE_TIMECLS_COURSE_IDCLS_COURSE_OFFER_NBRCLS_TERMCLS_TERM_SDESCCLS_TERM_LDESCCLS_SESSION_CDCLS_SESSION_SDESCCLS_SESSION_LDESCCLS_CLASS_SECTIONCLS_INSTITUTION_CDCLS_INSTITUTION_SDESCCLS_INSTITUTION_LDESCCLS_CAMPUS_CDCLS_CAMPUS_SDESCCLS_CAMPUS_LDESCCLS_LOCATION_CDCLS_LOCATION_SDESCCLS_LOCATION_LDESCCLS_ADDRESS1CLS_ADDRESS2CLS_CITYCLS_STATECLS_ZiPCLS_GROUP_CDCLS_GROUP_SDESCCLS_GROUP_LDESCCLS_ORG_CDCLS_ORG_SDESCCLS_ORG_LDESCCLS_SUBJECT_CDCLS_SUBJECT_SDESCCLS_SUBJECT_LDESCCLS_CIP_CDCLS_HEGIS_CDCLS_CATALOG_NBRCLS_COURSE_LDESCCLS_CLASS_NBRCLS_CLASS_COMPONENT_CDCLS_CLASS_COMPONENT_SDESCCLS_CLASS_COMPONENT_LDESCCLS_CLASS_TYPECLS_CLASS_TYPE_SDESCCLS_CLASS_TYPE_LDESCCLS_ASSOCIATED_CLASS_NBRCLS_START_DATECLS_END_DATECLS_FACILITY_CODE1CLS_FACILITY_SDESC1CLS_FACILITY_LDESC1CLS_STANDARD_MTG_PATTERN1CLS_MTG_START_TIME1CLS_MTG_END_TIME1CLS_FACILITY_CODE2CLS_FACILITY_SDESC2CLS_FACILITY_LDESC2CLS_STANDARD_MTG_PATTERN2CLS_MTG_START_TIME2CLS_MTG_END_TIME2CLS_INSTRUCTOR_EMPLIDCLS_INSTRUCTOR_NAMECLS_INSTRUCTOR_PHONECLS_ENROL_TOTAL

CAREER_TERM DIMENSIONCTRM_PKCTRM_DATE_TIMECTRM_STUIDCTRM_ACAD_CAREERCTRM_ACAD_CAREER_SDESCCTRM_ACAD_CAREER_LDESCCTRM_ACAD_CAREER_NBRCTRM_INSTITUTIONCTRM_INSTITUTION_SDESCCTRM_INSTITUTION_LDESCCTRM_STRM_CDCTRM_STRM_SDESCCTRM_STRM_LDESCCTRM_STRM_BEGIN_DTCTRM_STRM_END_DTCTRM_WEEKS_OF_INSTRUCTCTRM_TERM_CATEGORY_CDCTRM_TERM_CATEGORY_SDESCCTRM_TERM_CATEGORY_LDESCCTRM_ACAD_YEARCTRM_WITHD_CDCTRM_WITHD_SDESCCTRM_WITHD_LDESCCTRM_WITHD_REASON_CDCTRM_WITHD_REASON_SDESCCTRM_WITHD_REASON_LDESCCTRM_WITHDRAW_DATECTRM_ACAD_PROG_PRIME_CDCTRM_ACAD_PROG_PRIME_SDESCCTRM_ACAD_PROG_PRIME_LDESCCTRM_ACAD_PROG_STATUS_CDCTRM_ACAD_PROG_STATUS_SDESCCTRM_ACAD_PROG_STATUS_LDESCCTRM_ACAD_PROG_ACTION_CDCTRM_ACAD_PROG_ACTION_SDESCCTRM_ACAD_PROG_ACTION_LDESCCTRM_ACAD_PROG_REASON_CDCTRM_ACAD_PROG_REASON_SDESCCTRM_ACAD_PROG_REASON_LDESCCTRM_ACAD_LOAD_CDCTRM_ACAD_LOAD_SDESCCTRM_ACAD_LOAD_LDESCCTRM_ACAD_LEVEL_BOT_CDCTRM_ACAD_LEVEL_BOT_SDESCCTRM_ACAD_LEVEL_BOT_LDESCCTRM_ACAD_LEVEL_EOT_CDCTRM_ACAD_LEVEL_EOT_SDESCCTRM_ACAD_LEVEL_EOT_LDESCCTRM_CUR_GPA_RANGECTRM_CUM_GPA_RANGE

DATE_TIME DIMENSIONDTIM_PKDTIM_DATE_TIMEDTIM_TYPEDTIM_DESCRDTIM_ACAD_YEARDTIM_ACAD_MONTH_NBRDTIM_CAL_YEARDTIM_CAL_QUARTERDTIM_CAL_MONTH_NBRDTIM_CAL_MONTH_SDESCDTIM_CAL_MONTH_LDESCDTIM_CAL_WEEK_NBRDTIM_CAL_WEEK_NBR_YTDDTIM_CAL_DAY_NBRDTIM_CAL_DAY_SDESCDTIM_CAL_DAY_LDESCDTIM_CAL_DAY_NBR_YTDSA_VERSIONSA_RDBMS_VERSIONDECISION_STREAM_VERSIONDS_ODS_VERSIONother fields that would help us auditchanges to the ODS …

CLASS_SECTIONENROLLMENT FACTS

ENRL_PKENRL_STU_FKENRL_CLASS_FKENRL_DATE_TIME_FKENRL_HEADCOUNTENRL_UNT_TAKENENRL_GRADE_POINTSENRL_GRADE_OFFENRL_PKENRL_STU_FKENRL_CLASS_FKENRL_DATE_TIME_FKENRL_HEADCOUNTENRL_UNT_TAKENENRL_GRADE_POINTSENRL_GRADE_OFF

STUDENT DIMENSIONSTU_PKSTU_DATE_TIMESTU_EMPLIDSTU_SSNSTU_NAMESTU_GENDERSTU_BIRTHDATESTU_CAMPUS_EMAIL_CDSTU_CAMPUS_EMAILSTU_CAMPUS_ADDR_CDSTU_CAMPUS_ADDR1STU_CAMPUS_ADDR2STU_CAMPUS_ADDR3STU_CAMPUS_CITYSTU_CAMPUS_STATESTU_CAMPUS_ZIPSTU_CAMPUS_PHONE_CDSTU_CAMPUS_PHONESTU_CAMPUS_PHONE_EXTSTU_HOME_EMAIL_CDSTU_HOME_EMAILSTU_HOME_ADDR_CDSTU_HOME_ADDR1STU_HOME_ADDR2STU_HOME_ADDR3STU_HOME_CITYSTU_HOME_STATESTU_HOME_ZIPSTU_HOME_PHONE_CDSTU_HOME_PHONESTU_HOME_PHONE_EXTSTU_CITZ_STATUS_CDSTU_CITZ_SDESCSTU_CITZ_LDESCSTU_ETHNIC_CDSTU_ETHNIC_PRIMESTU_ETNHIC_PRIME_SDESCSTU_ETHNIC_PRIME_LDESCSTU_ADMIS_EXT_PKSTU_FINAID_EXT_PKSTU_STUFIN_EXT_PK

ex tra c t

r e p o r t in g d a ta m a r t (R D M )

tr a n sa c tio n a p p lic a tio n

m a g ic h a p p e n s

h e re

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WILM – Reporting Strategy

R D M :Fa s t, E a sy , A ffo rd a b le

3 ,0 00 + d a ta ite m s e x tra c te d fro m 6 0 0 + S A ta b le s

cam p u s co m m u n ityb io g r a p h ic / d e m o g r a p h ic a tt r ib u te sg ro u p s , in te re s ts , c o m m u n ic a t io n s , c o m m e n ts , c h e ck lis ts

ad m is s io n sp ro s p e c ts , re c ru ite rs , a p p lic a n t s , h ig h s c h o o ls , te s t s c o re se v a lu a t io n s , ra t in g s , s ta tu s , a t t r ib u te s , o u tc o m e s

s tu d e n t re co rd sc a re e rs , p ro g r a m s , p la n s , s u b -p la n s , te rm s , e n ro llm e n ts , g r a d e sc la s s e s , s e c t io n s , c o m p o n e n ts , m e e t in g t im e s , in s tru c to rs , fa c ilit ie s

f in an c ia l a idIS IR s , fe d / lo c a l c a lc u la t io n s , C O A , P C , P E L L , p a c k a g in g , d is b u r s e m e n ts

s tu d e n t f in an c ia lsa c c o u n ts , re fu n d s , 3 rd p a rty , p a y m e n t p la n s , c a s h ie r in g , G L , c o lle c t io n s

Page 19: Datamining WILM 09/19 - WordPress.com

WILM – Reporting Strategy

Student Administration is only suite in RDS currentlyHuman Resources suite is planned to be added in Spring 2003

Page 20: Datamining WILM 09/19 - WordPress.com

WILM – Reporting Strategy

Purchased Crystal Reports 8.5 interimRFP – JuneCognos Tools of Impromptu and PowerPlayConract Signed – AugustCognos Training – October 14

Page 21: Datamining WILM 09/19 - WordPress.com

WILM – Reporting Strategy

Peoplesoft

Live Data

RDS SA/HR

Last Nights Data

Data Warehouse

Historical

Data

WILM Database Infrastructure

Peoplesoft Data Base Peoplesoft RDS Data Base WILM Data Warehouse Data Warehouse Actual “Live” Data Data replaced Nightly extract Historical Data Cubes 1,000’s of data fields 4,000+ data elements from 600+ Tables

COGNOS Decision Stream

Extraction

COGNOS Decision Stream

Extraction Tool

Data Warehouse

Cube

Date Warehouse

Cube

Page 22: Datamining WILM 09/19 - WordPress.com

WILM – Reporting Strategy

Data WarehouseData

Warehouse Cube

Data Warehouse

Historical Data

Data Warehouse

Cube

Page 23: Datamining WILM 09/19 - WordPress.com

WILM – Reporting Strategy

Why a data warehouse?Relieves transactional database for reporting needsRDS is replaced nightlyComparisons require stable number to compare toHolds a snapshot of the dataAbility to analyze patterns and trends over a period of timeAbility to users to drag-and-drop, swap, etc.

Page 24: Datamining WILM 09/19 - WordPress.com

WILM – Reporting Strategy

StepsPlanningGathering Data Requirements and Modeling

Defining FieldsDefining Dimensions

DevelopmentPhysical Database DesignData Mapping and TransformationData Extraction and Load

Testing and VerificationTraining Rollout

Page 25: Datamining WILM 09/19 - WordPress.com

WILM – Reporting Strategy

R D S SA /H R

L ast N ights

D ata

D ata W arehouse

H istorical D ata

W IL M R eports C O G N O S

- Im prom ptu W IL M R eports

C O G N O S - P ow erP lay

Peop lesoft

L ive D ata

D ata W arehouse

C ube

D ate W arehouse

C ube

W IL M R ep orting In frastructure

Page 26: Datamining WILM 09/19 - WordPress.com

WILM – Reporting Strategy

Outlook for ReportingReal-time, secure, and personalized data access, analysis, and sharing for all information usersMust be applicable for all technical and non-technical usersMust have performance consistent with desktop applications from both a functionality and speed perspective

Page 27: Datamining WILM 09/19 - WordPress.com

WILM – Reporting Strategy

Must be easy to use and supportMust interface with/access data in applicationsMust deliver immediate return on investmentMust be well-documented and easily accessible and easy to understand

Must be Web-based and flexible to meet the needs of all the users

Page 28: Datamining WILM 09/19 - WordPress.com

Thank you!

Page 29: Datamining WILM 09/19 - WordPress.com

WILM – Reporting Strategy