MISY 930 BusinessInformationSystemsAndTechnology Syllabus-11

8/12/2019 MISY 930 BusinessInformationSystemsAndTechnology Syllabus-11

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MISY 930 - Business Information Systems & Technologies

Fall 2011

Instructor: Dr. Ramesh Konda

E-mail: [email protected] ours: By appointment (Please send me an email for appointment)

!e"uire# Te$t Boo%:

ana!ement "nformation #ystems$ 12%&

Ken 'audon ane 'audon

"#B*+10, 01-212/"#B*+1-, 3/01-212/

Pu4lisher, Prenti5e 6all7 8opyri!ht, 2012

lass Sche#ule:

#ept 2 2$ 2011 (#at #un)95t 22 2-$ 2011 (#at #un)

*o: 2 23$ 2011 (#at #un)

O'er'ie( of the ourse

;his 5ourse pro:ides inte!rati:e 5o:era!e of essential ne< te5hnolo!ies$ information system

appli5ations$ and their impa5t on 4usiness models and mana!erial de5ision makin!. =e <illdis5uss "nformation #ystems and >lo4al &+Business and 8olla4oration. Part of this$ <e <ill

5o:er "nformation #ystems$ 9r!ani?ations$ and #trate!y. =e <ill also 5o:er key aspe5ts of"nformation ;e5hnolo!y "nfrastru5ture$ &mer!in! ;e5hnolo!ies$ Foundations of Business"ntelli!en5e$ 5hie:in! 9perational &A5ellen5e$ and Buildin! and ana!in! #ystems.

)earning O*+ecti'es

;his 5ourse pro:ides you <ith the opportunity to,

1. Be a4le to understand the insi!hts of "nformation #ystems$ 9r!ani?ations$ and #trate!y2. Be a4le to define "nformation #ystems in >lo4al Business ;oday

-. Be a4le to identify strate!ies >lo4al &+Business and 8olla4oration

. 8learly define "nformation ;e5hnolo!y "nfrastru5ture alon! <ith &mer!in! ;e5hnolo!ies. Be a4le to lay Foundations of Business "ntelli!en5e in terms of Data4ases and "nformation

ana!ement

. nderstand the importan5e of ;ele5ommuni5ations$ the "nternet$ and =ireless ;e5hnolo!y3. Define strate!y for a5hie:in! 9perational &A5ellen5e and 8ustomer "ntima5y usin! Di!ital

arkets$ Di!ital >oods

/. Be a4le to define and ana!in! Kno<led!e and enhan5e data dri:en de5ision makin!

mailto:[email protected]

mailto:[email protected]



Key Factors for an Effective Quality Assurance in Data Warehousing

. Be a4le to 4uild and ana!in! #ystems in terms of "nformation #ystems and ProCe5ts

lass Sche#ule:

=eekend 5lass ssi!nments,0am-,:30.m ,:30.m-/.m

2+#ept 5hap 1$ 2 8hap -$

2+#ept 5hap $ Re:ie< 8#'9 " (three uestions)

Due, 8#'9 " (1th 95t)

22+95t 5hap 3$ / 5hap $ 10 8#'9 "" (three uestions)

2-+95t 5hap 11$ 12 Re:ie< id+term (online%take home)

Due, id+term (th *o:)

Due, 8#'9 "" (th *o:)

2+*o: 5hap 1-$ 1 8hap 1$ Re:ie< Due, 8lass ProCe5t (1th *o:)

-1o' 2inal E$am In-class4

5ra#ing 6ssignments

8#'9s (;hree 8#'9s) -0E

id+term 1E

8lass ProCe5t%"ndi:idual ProCe5t 20E

Final &Aam -0E

ttendan5e 8lass Parti5ipation E

lass 5ra#ing riteria:

1. 8#'9 (;hree 8#'9 assi!nments7 ea5h <ill ha:e four essay uestions) + approA. -0E

2. id+term &Aam (pproAimately 2+-0 multiple 5hoi5e uestions + take home eAam) +

approA. 1E

-. "ndi:idual ProCe5t (#tudents su4mit paper on a spe5ifi5 topi5 from the 5lass sylla4us$

minimum / pa!es (dou4le+spa5e) lon! and must use at least referen5es and use P

format for the paper) + approA. 20E

. Final &Aam (pproAimately 0+0 multiple 5hoi5e uestions + eAam <ill 4e !i:en in the

5lass + students must attend the 5lass to take this eAam) + approA. -0E

. 8lass Parti5ipation (Based on the attendan5e as <ell as parti5ipation in 5lass dis5ussions)

+ approA. E




5ra#ing 2ormula

100

+ 0

BG /3 /

B /- /

B+ /0 /2

8G 33 3

8 3- 3

8+ 30 32

D 0

F or H

lass 7artici.ation

8lass parti5ipation <ill 4e 4ased on the :alue you add to the 5lass throu!h your uestions$

statements$ and 5omments. "t is the uality of these 5ontri4utions that is more important

than the uantity.

6tten#ance

ttendan5e is mandatory and <ill 4e 5he5ked for ea5h 5lass session. "n addition$ an

uneA5used 5lass a4sen5e <ill affe5t your 5lass parti5ipation !rade. Please si!n+in theattendan5e sheet in e:ery time the 5lass meets.

6ca#emic Miscon#uct

5ademi5 mis5ondu5t or 5heatin! <ill not 4e tolerated. ;he follo<in! definition ofa5ademi5 mis5ondu5t has 4een de:eloped 4y ";

5ademi5 mis5ondu5t is defined as re5eipt or transmission of unauthori?ed aid onassi!nments or eAaminations$ pla!iarism$ unauthori?ed use of eAamination materials$ or

other forms of dishonesty in a5ademi5 matters. 5ademi5 mis5ondu5t is a maCor offense

at "; 4e5ause it diminishes the uality of s5holarship in our a5ademi5 5ommunity and

5heats those <ho may e:entually depend upon our kno<led!e and inte!rity.

S.ecial ircumstances

"f you ha:e a do5umented disa4ility and <ish to dis5uss a5ademi5 a55ommodations$

please 5onta5t me as soon as possi4le.




lass 7ro+ect 5ui#elines

;he purpose of the 5lass proCe5t is to demonstrate the appli5ation of kno<led!e that you ha:e!ained from the 5lass. Iou 5an pi5k any topi5 from the 5lass 5urri5ulum$ and 5an <ork on it

usin! the follo<in! options. Iou 5an pi5k one of the follo<in! options for your 5lass proCe5t.(;o<ards end of this se5tion$ atta5hed is the sample paper that you 5an use as referen5e for the

format.)

O.tion ,: )iterature re'ie(4

'iterature re:ie< of on any spe5ifi5 topi5 from the 5lass 5urri5ulum (&Aample, importan5e of"nformation #ystems in health5are$ "nformation #ystems metri5s in health5are$ "nformation

#ystems proCe5t s5ope mana!ement$ &mer!in! te5hnolo!y in "nformation #yst!ems$ et5.).

Iou should find and study minimum papers from your topi5 area. 9n5e you re:ie< the papers$ please re<rite the kno<led!e that you ha:e !ained from the a4o:e in a paper format as follo<s.

The length of your .a.er shoul# 8 .ages #ou*le s.ace .lease follo( 676 format4

Format for the paper,a) 4stra5t, Des5ri4e at hi!h+le:el a4out the information that you are !oin! to 4e

presentin! in the paper.

4) "ntrodu5tion, Pro:ide introdu5tion a4out your topi5 and <hy it is important% interestin!to study and its appli5ations% 5hallen!es.

5) 'iterature re:ie<, Pro:ide information that is a:aila4le in the literature that is related

to your topi5.d) #i!nifi5ant findin!s%learnin! from the 'iterature re:ie<, Des5ri4e the kno<led!e that

you ha:e !ained from the literature re:ie< and make any ar!uments and dis5ussion.

e) #ummary and potential topi5s for future resear5h, Pro:ide your 5omments on ho< the

a4o:e study (that you ha:e found in the literature) is useful and ho< it 5ould ha:e done tomake it 4etter.

O.tion : 7ractical or% 7ro+ect

Iou may 5hoose a proCe5t from your <ork eAperien5e more that is rele:ant to the 5lass

5urri5ulum. Please <rite this proCe5t in the follo<in! format. "t is re5ommended that you use

appropriate referen5es from the literature.lso$ list 5hallen!es and ho< you 5ould ha:e addressed no< ha:in! that you may ha:e more

kno<led!e in topi5 from this 5lass. ;ry to use any pu4lished papers to support your

ar!uments%dis5ussion. The length of your .a.er shoul# 8 .ages #ou*le s.ace .lease follo(

676 format4

Format for the paper, (#ee in option 1 for des5ription for some of the follo<in!)

a) 4stra5t 4) Des5ription

5) Plan%#teps%ethodolo!y follo<ed

d) #i!nifi5ant findin!s%learnin! from the ProCe5te) #ummary and lessons learned and potential topi5s for future resear5h




Detailed 5hapters,

++++++++++++++++++++++++++++++++++++++++++++++++

Part 1, 9r!ani?ations$ ana!ement$ and the *et<orked &nterprise8hapter 1, "nformation #ystems in >lo4al Business ;oday

8hapter 2, >lo4al &+Business and 8olla4oration8hapter -, "nformation #ystems$ 9r!ani?ations$ and #trate!y

8hapter , &thi5al and #o5ial "ssues in "nformation #ystems

Part 2, "nformation ;e5hnolo!y "nfrastru5ture8hapter , "; "nfrastru5ture and &mer!in! ;e5hnolo!ies

8hapter , Foundations of Business "ntelli!en5e, Data4ases and "nformation

ana!ement8hapter 3, ;ele5ommuni5ations$ the "nternet$ and =ireless ;e5hnolo!y

8hapter /, #e5urin! "nformation #ystems

Part -, Key #ystem ppli5ations for the Di!ital !e8hapter , 5hie:in! 9perational &A5ellen5e and 8ustomer "ntima5y, &nterpriseppli5ations

8hapter 10, &+8ommer5e, Di!ital arkets$ Di!ital >oods

8hapter 11, ana!in! Kno<led!e8hapter 12, &nhan5in! De5ision akin!

Part , Buildin! and ana!in! #ystems

8hapter 1-, Buildin! "nformation #ystems8hapter 1, ana!in! ProCe5ts

8hapter 1, ana!in! >lo4al #ystems

++++++++++++++++++++++++++++++++++++++++++++++++




Sample Paper

Key Factors for an Effective Quality Assurancein Data Warehousing

4y

Ramesh Konda1$ Rao R. *emani2 and amuna R. *emani-

1 *o:a #outheastern ni:ersity

;he >raduate #5hool of 8omputer and "nformation #5ien5es

Fort 'auderdale$ F' ---1

[email protected] 8olle!e of Business

ni:ersity of #t. ;homas1000 'a#alle :enue $ ;6

inneapolis $ * 0-

*ema//[email protected]

-Primetherapeuti5s ''8

1-0 8orporate 8enter Dr$&a!an$ * 121

*[email protected]#ili5on Jalley meri5an #o5iety for uality 8onferen5e (95to4er 200)=ritten on, 2 uly 200


6*stract

#trate!i5 and data+dri:en de5ision makin!$ in tur4ulent en:ironments$ has 4een

pushin! or!ani?ations to 4uild 4usiness related Data =arehouse (D=) en:ironment to

store and mana!e :ast amounts of data. ;he main premise of ha:in! a D= is to pro:ide a

sin!le point of truth and 5oherent data at one pla5e. D= 5an 4e defined as a 5olle5tion of

su4Ce5t+oriented$ inte!rated$ non+:olatile data that supports the mana!ement de5ision




pro5ess. #u55essful D= implementation helped the 4usinesses to store$ analy?e$ and

share 5riti5al and 5onfidential data on+line amon! their 4usiness partners and 5ustomers.

6o<e:er$ ineffi5ien5ies in data uality <ithin a !i:en D= 4een the main 5on5ern of the

4usiness users that ha:e not 4een addressed adeuately. nless a defined and planned

approa5h for data uality is follo<ed durin! the different phases of D=$ the

or!ani?ations may suffer from data uality issues$ 5onseuently$ any efforts to fiA the

Data uality (D) issues 4e5ome :ery eApensi:e and time 5onsumin!. D 5an 4e

attri4uted to se:eral fa5tors su5h as data a55ura5y$ 5ompleteness$ timeliness$ 5oheren5y$

5onsisten5y$ 5onformity$ and re5ord dupli5ation. ;his paper presents a su55essful

approa5h for implementation of key uality fa5tors into D= durin! the de:elopment and

deployments phase. &Aamples from 4usiness are used to demonstrate the pra5ti5al aspe5ts

of the proposed approa5h that yield positi:e results in D= de:elopment and deployment.

;ey(or#s

Data =arehousin!$ Data uality$ uality ssuran5e$ uality ssuran5e ;estin!$

uality ssuran5e Plannin!$ uality ssuran5e Deployment

, Intro#uction:

ean+Pierre (200) 4elie:es that an un5lear definition of D itself leads to la5k of solid

methodolo!y to deal <ith D. uality is a relati:e statement and :aries 4y indi:iduals 4ased

upon their per5eptions. "n simplisti5 terms D is per5ei:ed as Ltrue and a55urateM. ;his makes

D hard to define and measure. ;o understand ho< to ta5kle the pro4lem$ D needs to 4e

understood thorou!hly from the or!ani?ational point of :ie<$ and then a pro5ess 5an esta4lished

to deal <ith D <ithin the or!ani?ation. "n simplisti5 terms$ D 5an 4e defined as an a4sent of

undesira4le 5hara5teristi5s or presen5e of desira4le 5hara5teristi5s in the data.




Bu5kley and Poston (1/) defined soft<are uality assuran5e (#) as a planned and

systemati5 pattern of all a5tions ne5essary to pro:ide adeuate 5onfiden5e that the soft<are

5onforms to the defined reuirements. 8ho< (1/) ar!ued that failure to pay enou!h attention

to # has often resulted in s5hedule delays$ 4ud!et o:erruns$ and failure to meet the 5ustomer

satisfa5tion. 4del+6amid (1//) arti5ulated in his resear5h that # not only holds the key to

5ustomer satisfa5tion$ 4ut also has a dire5t impa5t on the 5ost and the s5hedulin! of a proCe5t.

;here has 4een !reat pro!ress and impro:ement in the 5ore te5hnolo!y of D=7 ho<e:er the

D aspe5ts are one of the 5ru5ial issues that <ere not adeuately addressed. "n a sur:ey 4y

Friedman$ *elson$ and Rad5liffe (200)$ it <as stated that 3 per5ent of sur:ey respondents

reported si!nifi5ant pro4lems stemmin! from defe5ti:e and fra!mented data$ o:er 0 per5ent has

in5urred 5ost for data re5on5iliations$ and -- per5ent <ere delayed "; systems o<in! to data

uality pro4lems. se5ond sur:ey 4y m4ler (200) reported se:eral metri5s from the response

that indi5ate Data uality has 4een the maCor issue and reuires 5onsiderate attention to sol:e

this pro4lem. For eAample$ the follo<in! 5hart illustrates only 2 per5ent of the respondents feel

!ood a4out the data uality in their data <arehousin! and rest of the / per5ent indi5ate some

kind of data uality issues that need 4e addressed.

Fi!ure 1. 8urrent #tate of Data uality (m4ler$ 200)

9ne of the maCor fa5tors of influen5in! the D is user per5eption. Furthermore$ if user




assumptions or per5eptions are un5he5ked$ then o:er time it starts to 4e5ome Nthe truthO <hether

or not it has an o4Ce5ti:e or fa5tual 4asis$ from 4oth 4usiness and te5hni5al perspe5ti:es (Bryan$

2002). D indi5ates ho< <ell enterprise data mat5hes up <ith the real <orld at any !i:en time.

;here are many sour5es of dirty data. ;hese sour5es 5onsist of a) Poor data entry$ <hi5h

in5ludes misspellin!s$ typo!raphi5al errors and transpositions$ and :ariations in spellin! or

namin!$ 4) data missin! from data4ase fields$ 5) la5k of 5ompany+<ide or industry+<ide data

5odin! standards$ d) multiple data4ases s5attered throu!hout different departments or

or!ani?ations$ <ith the data in ea5h stru5tured a55ordin! to the rules of that parti5ular data4ase$

and e) older systems that 5ontain poorly do5umented or o4solete data (ndrea iriam$ 200).

*ord (200) mention that the D has 4e5ome an in5reasin!ly 5riti5al 5on5ern and it has 4een

rated as a top 5on5ern to data 5onsumers in many or!ani?ations. *ord (200) 5ontinued statin!

that the data uality is !ainin! its importan5e <ithin resear5h and amon! the 5onsumer

or!ani?ations.

&nsurin! hi!h le:el D is one of the most eApensi:e and time+5onsumin! tasks to

perform in data <arehousin! proCe5ts. any data <arehouse proCe5ts ha:e failed half<ay

throu!h due to poor D. ;his is often 4e5ause D pro4lems do not 4e5ome apparent until the

proCe5t is under<ay. ny 5han!es to D= at the implementation sta!e are eAtremely 5ostly and

may push proCe5t 4ud!et limits. "f all the 5onsiderations are eAamined thorou!hly at the strate!y

and desi!n sta!e of D=$ the plans and 5ontrols 5an 4e formulated into the desi!n for D that 5an

de5rease operational 5osts$ in5rease 5ustomer satisfa5tion$ impro:e effe5ti:e de5ision+makin!$

and employee 5onfiden5e in usin! the data (ndrea iriam$ 200). ;he uality of information

systems ("#) is 5riti5ally important for 5ompanies to deri:e return on their in:estments.

;herefore$ de:elopin! !ood uality in Data =arehousin! that meets user needs is 4e5omin! a




5riti5al theme for information te5hnolo!y mana!ement (>uimaraes$ #taples 5Keen$ 2003).

&n!lish (2001) listed se:eral eAamples in his paper that dra< attention to the ne!ati:e impa5t of

the D issues in D=. #ome of them in5lude errors in students Basi5 #tandards ;est s5ores$

pension <ithholdin!s$ in:oi5in!$ and food pro5essin! that led to the loss of 4illions of dollars as

<ell as loss of reputation of those 4usinesses.

#e5tion 2 of this paper presents a 4rief literature re:ie<. "n se5tion -$ the authors eAamine

the pro5ess and 5riti5al fa5tors of D. ;hen$ the follo<in! se5tion dis5usses the 5urrent pra5ti5es

of D in D=$ and proposes a solution 4ased on the pra5titioners point of :ie< for impro:in! the

uality of data. ;he last se5tion summari?es the paper.

)iterature !e'ie(

#oft<are uality assuran5e () is one of the 5riti5al fun5tions in the soft<are

de:elopment and maintenan5e of soft<are systems. Be5ause is a ri!orous fun5tion that adds

si!nifi5ant effort and 5ost to the total soft<are de:elopment 5ost$ the pro5ess is often

5ompromised durin! the soft<are de:elopment. 6o<e:er$ the 5on5ern has not 4een adeuately

addressed in the literature. ;here are many fa5ets of in a D= proCe5t7 this paper is primarily

intended to fo5us on pro5ess and fa5tors in:ol:ed in D=Os Data uality aspe5ts. s the

uality assuran5e aims to dete5t systemati5 risks in order to a:oid them$ the authors <ill dis5uss

:arious uality assuran5e fa5tors in this paper. s aims at systemati5 5o:era!e of 4usiness

reuirements to system reuirements to test plan and test eAe5ution$ the pro5ess ensures data

uality is a5hie:ed to the a55epta4le le:el.

"ain Don (200) ar!ue that in order to ta5kle this diffi5ult issue$ or!ani?ations need

4oth a top+do<n approa5h to D sponsored 4y the most senior le:els of mana!ement and a

5omprehensi:e 4ottom up analysis of data sour5in!$ usa!e and 5ontent in5ludin! an assessment




of the enterprises 5apa4ilities in terms of data mana!ement$ rele:ant tools$ and people skills. Qu$

*ord$ Bro<n$ and *ord (2002) 4elie:e that for or!ani?ations 5onsiderin! implementin! of D=$

it is essential that D issues 4e thorou!hly understood and the or!ani?ations should o4tain

kno<led!e of the 5riti5al su55ess fa5tors essential to ensure D durin! the implementation

pro5ess. ;he main 5omponents of data that determines the D are$ 5ompleteness$

appropriateness$ a55ura5y$ !roupin! a55ura5y$ a55ess$ 5onfiden5e$ 5urren5y$ re!ulators$ le!al

5omplian5e$ and meta+linkin!. Data interfa5e$ data repli5ation and data mi!ration and mo:ement

all share 5ommon 5hara5teristi5s su5h as :olume of data$ timeliness of mo:ement and

pro5essin!$ dire5tion of flo< 4et<een sour5es and tar!ets (Bryan$ 2002).

D tools !enerally fall into one of three 5ate!ories, auditin!$ 5leansin! and mi!ration.

Data auditin! tools apply predefined 4usiness rules a!ainst a sour5e data4ase. ;hese tools

enhan5e the a55ura5y and 5orre5tness of the data at the sour5e. #ome of the data 5leansin! tools

5ompare the data a!ainst an independent sour5e e.!. # Postal 8odes for :erifyin! the data.

Data is typi5ally mo:ed from the sour5e to intermediate sta!in! area <here the data 5leansin!

a5ti:ities are performed.

Data mi!ration is an a5ti:ity <here data is eAtra5ted and transported from one sour5e to

another. Data mi!ration tools perform the a5ti:ity of eAtra5tion$ transportation and mappin! for

data from one platform to another. Poor D impa5ts the typi5al enterprise in many <ays su5h as

5ustomer dissatisfa5tion$ in5reased 5ost$ and lo<ered employee Co4 satisfa5tion. ;he sli!htest

suspi5ion of poor D often hinders mana!ers from rea5hin! any de5ision. "n order to ensure D

assessment$ 6ufford (1) proposed a model <hi5h 5onsists of definin! D eApe5tations and

metri5s$ identifyin! and assessin! risks$ miti!atin! risks$ and monitorin! and e:aluatin! results

on an on+!oin! 4asis.




3 7rocess an# the ritical 2actors of <=

Data <arehousin! depends on inte!ratin! data uality assuran5e into all <arehousin!

phases plannin!$ implementation$ and maintenan5e (Ballou and ;ayi$ 1). &Aperts in uality

5ontrol methodolo!y al<ays re5ommend addressin! the Lroot 5auseM duly 5onsiderin! the

follo<in! uality eApe5tations,

1) 55ura5y

2) 8ompleteness

-) ;imeliness

) "nte!rity

) 8onsisten5y

) 8onformity

3) Re5ord Dupli5ation

*emani and Konda (200) ha:e presented an eAtended :ersion of Data =arehouse

De:elopment 'ife 8y5le (D=D'8) 'ayers$ <hi5h lists 5omprehensi:e phases and links the Data

uality fa5tors as follo<s. ;he maCor theme in ea5h of the D=D'8 layers 5an 4e des5ri4ed as

follo<s,




Fi!ure 2. Data =arehouse De:elopment 'ife 8y5le (D=D'8) 'ayers$ adopted

from *emani and Konda (200)

1) Plannin!, part from D proCe5t su55ess$ it is e:ident that definin! and mana!in! the

proCe5t s5ope influen5es the proCe5tOs o:erall su55ess. &:ery D= proCe5t reuires a 5areful

4alan5e data sour5es$ pro5esses$ pro5edures$ and other fa5tors are s5oped as 5ommensurate <ith

the proCe5tOs si?e$ 5ompleAity$ and importan5e.

2) nalysis, "n this layer$ one should 5onsider analy?in! the data from :arious a:aila4le

data sour5es. "n this phase it is re5ommended to perform the data profilin! of the data.

-) Reuirements, "n this layer$ D= professional <ill 5olla4orates <ith the 4usiness

stakeholders to understand the 4usiness pro4lem 4y definin! and do5umentin! the reuired data

uality fa5tors for the D= proCe5t.




) De:elop, "n this phase$ the D= professional <ill de:elop and test the D= solution

keepin! in mind the D fa5tors defined in the reuirement phase.

) "mplement, "n this phase$ the D solution <ill 4e implemented after duly si!ned off 4y

the uality assuran5e team.

) easure, "n this phase$ a data samplin! is done and a measure to understand 5urrent

pro5ess 5apa4ility is <orked out on D fa5tors defined in the reuirements phase. ;his a5ti:ity

<ill ensure to minimi?e the data uality pro4lem.

s a 4asi5 pro5ess of pra5ti5in! the Data uality$ or!ani?ations need to understand and

define the pro5ess of data flo<$ data transformation and data stora!e. ;he pro5ess should 5onsist

of the sour5e$ sta!in! area$ data pro5essin!$ data transformation$ and data stora!e as follo<s,

Fi!ure -. Foundational Data =arehouse 'oad Pro5ess #ta!es

=e ha:e identified four different kinds of D assessment 5lassifi5ations, Data #our5e$

Data 'oad pro5ess$ Data ;ransformation and Data 'oad to ;ar!et ;a4les. =e further defined

multiple D assessment 5riterions for ea5h D assessment 5lass. ;hese D assessment

5riterions ha:e 4een linked to a uality assuran5e method from a pra5titioners perspe5ti:e and

summari?ed in ;a4le 1 4elo<.

Data #our5e #ta!in!

rea

Data 'oad

Pro5ess

Data

;ransformation

Data 'oad to

tar!et ta4les

Pre:ention from data5orruption should 4ethe fo5us

pply the 4usinesslo!i5 to the data to meetthe desired form.

Repair and reload thedata as needed.

udit%"nspe5t the datausin! 4usiness rules fordata uality. Build$

&Ae5ute$ and Report theD rules%metri5s.

&nsure all the files are 4ein! pro5essed.Repro5ess the failed

ones$ and lo! dis5ardthe 5orrupt ones



;a4le 1. 8lassifi5ation of D assessment 5lass$ 5riteria and method

<= 6ssessment lass <= 6ssessment riterion =uality 6ssurance Metho#

Data #our5e #our5e 'o5ation. Jalidate #our5e 'o5ation

#euen5e of Data Files Jalidate #euen5e of Files

#enders ddress 8onfirm #enders ddress

File #i?e Jalidate File #i?e

File re5eipt 5kno<led!ement 8onfirm File Re5eipt

Re5ord 8ount Re5on5ile <ith #our5e

Data 'oad Pro5ess 'oadin! ;ime Jerify 'oad ;ime

*otify 'oad Pro5ess Jerify 'oad #tatus

*otifi5ation

File #tatus ;ra5kin! Jalidate 5tionData 'oad Jerify 8omplete 'oad

;ra5k Failed Data 'oad Re5on5ile Failed Data 'oad

Data ;ransformation Business Rules Jalidate Business Rules

;ra5k Failed Business Rules Re5on5ile Failed Business

Rules

;ar!et Data 'oads Jalidate Data 'oads

Data 'oad to ;ar!et ;a4les Data ;ypes Jalidate 8onsistent Data

;ypes

Business Rules Jalidate ;ar!et Data

Data 8ompleteness Jalidate Data 8ompleteness

Data 'oad Jerify 8omplete 'oad;ra5k Failed Data 'oad Re5on5ile Failed Data 'oad

*otify 'oad Pro5ess Jerify 'oad #tatus

*otifi5ation

;he sour5e 5an 4e defined as the sour5e of the data. For eAample$ if an or!ani?ation has

se:eral lo5ations <here data is 4ein! 5aptured$ then ea5h of the lo5ations <ill 4e5ome a sour5e.

"n the pro5ess of loadin! the sour5e data into the D=$ the data <ill 4e held in a sta!in! area of

D=. ;he home !ro<n or off+the+shelf soft<are 5an 4e used to load data from sta!in! area into

the D= stora!e ta4les. ;ypi5ally <ithin the pro5ess$ the sour5e data is transformed to meet the

4usiness lo!i5 prior to loadin! into the tar!et ta4les of D=. 9n5e the data is transformed into

tar!et ta4les % stora!e$ an audit%inspe5tion plan must 4e de:ised 4ased on the 4usiness rules that

1



are 4ased on the desira4le 5hara5teristi5s of the final data. ;he desira4le 5hara5teristi5s 5an 4e

:erified 4y 4uildin! the D rules$ eAe5utin! the tests$ reportin! out any issues <ith data

5hara5teristi5s for 5orre5tin!%repairin! and for pre:enti:e a5tion in the upstream pro5ess.

> lassification of <=-riteria for a =6 solution

#e:eral resear5h proCe5ts ha:e ta5kled the pro4lem of assessin! s5ores for information

uality 5riteria. *aumann Rolkers (2000) present a 5lassifi5ation of " 5riteria <hi5h is

desi!ned to help or!ani?ations to assess the status of their or!ani?ational information uality and

monitor their " impro:ements. ;he authors of this paper ha:e eAtended *aumann Rolkers

(2000) 5lassifi5ation of " 5riteria to the D 5riteria. *emani and KondaOs (200) Data

=arehouse De:elopment 'ife 8y5le (D=D'8) 'ayers model <as also le:era!ed to further

stren!then the a5tiona4le and detailed tasks to a55omplish the data uality. uality ssuran5e is

a frame<ork in 4road sense that en5ompasses understandin!$ plannin!$ and eAe5ution of test

plans 4efore soft<are appli5ations are deployed for intended use durin! ea5h phase of the

D=D'8. ;ypi5ally$ the pro5ess starts <ith studyin! the 4usiness reuirements and systems

reuirements do5uments to understand the o:erall s5ope of the appli5ation%soft<are as <ell as to

define the s5ope of the . *eAt step in the pro5ess is to de:ise the test strate!y$ test plan and

test 5ases. 9ne of the key tasks in de:elopin! test 5ases is to understand the 4usiness and systems

reuirements$ and formulate the test 5ases for ea5h of the reuirements. ;he test 5ase de:eloper

must also in5lude the information a4out the test en:ironment$ and 4efore and after results from

the test. "n the ;a4le 1 a4o:e$ <e ha:e defined the assessment 5riterion and the respe5ti:e

uality methodolo!y. Belo<$ <e <ill pro:ide the 4rief definition of ea5h and the respe5ti:e

1



details on methodolo!y.

<ata Source: aCor emphasis durin! this phase is to 5he5k and :alidate that the files are

4ein! re5ei:ed from the pre+identified lo5ations$ and from the desi!nated sender. Files are also

5ross+:alidated to ensure that the file si?e and seuen5e of dimension and fa5t data are in order.

;he follo<in! detailed steps <ill ensure the a4o:e o4Ce5ti:e 5an 4e met.

Validate Source Location: "dentify the sour5e lo5ation and its :alidation$ for eAample$

files must 4e re5ei:ed only from the pre+identified lo5ations and in pre+identified format

su5h as DB2$ #'$ #00 or any other eAternal sour5e file.

Validate Sequence of Files: 'o!i5al seuen5e of the entire data files identified are

:alidated su55essfully. For eAample$ mem4er file needs to 4e loaded prior to pro5essin!

any 5laims.

Confirm Senders Address: Jerifi5ation of senders address7 it is 5riti5al to kno< the

sour5e sender information for tra5kin! and feed4a5k purpose.

Validate File Size: 8ross :erify the si?e of the re5ei:ed files to sour5e files to ensure that

the entire eApe5ted file has 4een re5ei:ed.

Confirm File Receipt: Jerify that a re5eipt a5kno<led!ment 5onfirmation is sent to the

sour5e for re5on5iliation%tra5kin! purpose.

Reconcile with Source: Jerify that all the re5ords in all files are pro5essed 4y :alidatin!

the follo<in! three steps,

File header validation: Jerify that header displays the re5ord type for eAample

L0O for header alon! <ith date and time stamped.

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File detail validation: Jerify that total of re5ords displayed in trailer 5ount

eual to the total re5ords eAist in detail se!ment and re5ord type is N2O

File Trailer validation: Jalidation of re5ord type e.!. trailer re5ord displays N/O

and also displays total num4er 5ount in detail re5ord eA5ludin! the header and

trailer re5ord.

<ata )oa# 7rocess: &mphasis durin! this phase is to ensure that the files re5ei:ed ha:e

4een pro5essed. =ithin this$ the key measures in5lude the loadin! time$ user notifi5ation7 file

status tra5kin!$ and re5on5ilin! the failed data load. ;he follo<in! detailed steps <ill ensure

the a4o:e o4Ce5ti:e 5an 4e met.

Verify Load Time: Jalidate that the estimated data load time has not enormously eA5eed

the time.

Verify Load Status otification: Jalidate that email notifi5ation pro5ess is fun5tional as

eApe5ted. &mail%status notifi5ations are sent periodi5ally indi5atin! the status of the load.

Validate Action: File status tra5kin! :alidation is to :erify that the failed data load are re+

pro5essed after identifyin! and 5orre5tin! the issue <ithin the spe5ified stipulated time.

Verify Complete Load: 8riti5al :alidation is 5ompleteness of data load. ake sure that all

the fields$ <ith spe5ified si?e and 5riteria ha:e su55essfully loaded.

Reconcile Failed !ata Load: Jerify that failed data load re5ords durin! data load pro5ess

are 4ein! tra5ked$ :alidated$ re:ie<ed$ updated and re+pro5essed if ne5essary.

<ata Transformation: ;ypi5ally$ the sour5e data is transformed in order to meet the

4usiness needs as <ell as standardi?ation a5ross the data4ase. "n this phase of $ the emphasis

1/



is to :erify the 4usiness rules are 4ein! used$ and re5on5ile the failed data loads. ;he follo<in!

detailed steps <ill ensure the a4o:e o4Ce5ti:e 5an 4e met.

Validate "usiness Rules: Jalidate the 4usiness rules transformation$ for eAample if the

sour5e ta4le reuires 5han!in! data :alue from LFemaleM to num4er L2M in

transformation. Jerify that female :alues are all transformed to numeri5 L2M.

Reconcile Failed "usiness Rules: Jerifyin! that transformation of any failed 4usiness

rule or any unidentified 4usiness rules are 5aptured$ re:alidated$ re+5onsidered and

5han!ed per 4usiness reuirements.

Validate !ata Loads: Re+:alidate that the data transformation file is su55essfully loaded

and mat5hed to the identified 5ounts in sour5e tar!et.

<ata )oa# to Target Ta*les: ;his is the 5riti5al phase <here one 5an :erify and :alidate

the final data. ;his <ill in5lude :alidatin! the 5onsistent usa!e of data types$ data 5ompleteness$

ri!ht data in ri!ht tar!et ta4les$ and re5on5iliation of failed data loads. ;he follo<in! detailed

steps <ill ensure the a4o:e o4Ce5ti:e 5an 4e met.

Validate Consistent !ata Types: 'astly :erify in the tar!eted ta4les and the data field

types are 5onsistent throu!hout the data4ase. For eAample$ 8ustomer"D is num4er

datatype a5ross all ta4les <here:er 5ustomerid <as used.

Validate Tar#et !ata: Jerify that the 4usiness rules are 5urrent and produ5in! the

reuired data in the tar!et ta4les.

Validate !ata Completeness: Jerifyin! data 5ompleteness$ <hi5h is to ensure that the

ri!ht data is loaded into the ri!ht tar!et ta4les.

1



Verify Complete Load: Jalidate that tar!eted data load is 5omplete in si?e and ri!ht in

data field :alues <hen 5ompared from the sour5e and transformation files.

Reconcile Failed !ata Load: Jerify that the failed data load is re5onsidered for re+

pro5essin! after the reuired 5han!es made or modified in the ori!inal data file.

Verify Load Status otification: Jerify that the status of the load pro5ess has 4een

pu4lished periodi5ally durin! data loadin! pro5ess$ for eAample$ if the Co4 a4orts$ LFile

B8 a4orted durin! identified time or else L'oad su55essfully 5ompleted (in5ludin! the

re5ord 5ount)M.

? onclusions

"n this paper$ the authors ha:e eAtended the D=D'8 (*emani Konda$ 200) approa5h

4y 5om4inin! su4Ce5ti:e and o4Ce5ti:e D assessments <hi5h are applied in pra5ti5e. ;he main

o4Ce5ti:e of any D= is to pro:ide de5ision makers a Lsin!le :ersion of the truthM of hi!h uality

data. ;his ena4les de5ision mana!ers and employees to make informed and 4etter de5isions.

Data uality (D) 5an 4e attri4uted to se:eral fa5tors su5h as data a55ura5y$ 5ompleteness$

timeliness$ 5oheren5y$ 5onsisten5y$ 5onformity$ and re5ord dupli5ation. 6o<e:er$ lo< uality

data has se:ere effe5ts on an or!ani?ation performan5e. nless a defined and planned approa5h

for data uality is follo<ed durin! the different phases of D=$ the or!ani?ations may suffer from

data uality issues$ and any efforts to fiA the D issues 4e5ome :ery eApensi:e and time

5onsumin!. "n this paper$ <e ha:e identified the D ssessment 8lasses$ D ssessment

8riterions$ and the respe5ti:e uality ssuran5e ethods. detailed eAplanation is pro:ided for

ea5h of the D ssessment 8riterion <ith related ethod and test 5ases that <ill ensure

20



the a5hie:ement of eApe5ted uality le:el in D= de:elopment and deployment.

!eferences

4del+6amid$ ;. (1/). ;he &5onomi5s of #oft<are uality ssuran5e, #imulation+Based

8ase #tudy. "# uarterly$ 12(-)$ -+11.

m4ler$ #. =. (200). Data uality #ur:ey Results.

http,%%<<<.am4ysoft.5om%do<nloads%sur:eys%Datauality2000.ppt$ a55essed on uly2$ (200).

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Ballou$ D.$ ;ayi$ >. (1). &nhan5in! Data uality in Data =arehouse &n:ironments.8ommuni5ations of the 8$ 2(1)$ pp. 3-+3/.Bryan$ F. (2002). ana!in! ;he uality and 8ompleteness of 8ustomer Data. ournal of

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#oft<are &n!ineerin!$ pp$ -+1.&n!lish$ '.P. (2001). "nformation uality ana!ement, ;he *eAt Frontier. nnual uality

8on!ress Pro5eedin!s$ meri5an #o5iety for uality$ il<aukee$ ="$ pp.2+--.

8ho<$ ;.#. (ed.) (1/). #oft<are uality ssuran5e, Pra5ti5al pproa5h$ "&&& 8omputer#o5iety Press$ #il:er #prin! $ D.

Friedman$ *elson$ and Rad5liffe (200). 8R Demands Data 8leansin!. >artner Resear5h.

>uimaraes$ ;.$ #taples$ D.#.$ 5Keen$ .D. (2003). ssessin! the "mpa5t from "nformation#ystems uality$ uality. ana!ement ournal$ Jol. 1$ *o. 1$ pp. -0+.

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"ain$ 6.$ Don$ . (200). Prioriti?in! and Deployin! Data uality "mpro:ement 5ti:ity.ournal of Data4ase arketin! 8ustomer #trate!y ana!ement$ Jol. 12$ *o. 2$ pp.

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ean+Pierre$ D. (200). "nte!ratin! D into Iour Data =arehouse r5hite5ture. Business

"ntelli!en5e ournal$ (2)$ 1/. *aumann$ F. Rolker$ 8. (2000). ssessment ethods for "nformation uality 8riteria. "n,

Pro5eedin!s of the 2000 8onferen5e on "nformation uality$ 8am4rid!e$ 1$ pp.

1/+12. *emani$ R. R$ Konda$ R. (200). Frame<ork for Data uality in Data =arehousin!.

Pro5eedin!s of the third "nternational nited "nformation #ystems 8onferen5e$

*"#89*$ #ydney$ ustralia$ 20(1)$ pp. 22+23. *ord$ >. D$ (200). n "n:esti!ation of the "mpa5t of 9r!ani?ation #i?e on Data uality "ssues.

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MISY 930 BusinessInformationSystemsAndTechnology Syllabus-11