bi practices

7/25/2019 BI practices

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Lecture 1:

Goals:

1. Explain potential advantages and use cases of BI&A (Business Intelligence and Analytics)a. eed a!ility to "a#e clear cut decision!. BI&A is a!out reporting for t$e "asses% advanced BI frontends% realti"e access ti"e%

planning capa!ilities% closed loop perfor"ance "anage"ent'. escri!e t$e !asic concepts of decisional "a#ing and decisional guidance

a. ecision "a#ing is t$e process of suciently reducing uncertainty and dou!t a!out

alternatives to allo* a reasona!le c$oice to !e "ade fro" a"ong t$e"

+our steps:i. Intelligence: searc$ for conditions t$at call for decisionsii. esign: invent% develop% and analy,e possi!le alternative solutionsiii. -$oice: select one of t$e solutionsiv. I"ple"entation: adapt t$e selected course of action to t$e decision situation

. -$aracteri,e t$e purpose of decision support syste"s and !usiness intelligence syste"sa. Decision Support System is a co"puter!ased infor"ation syste" t$at supports /

activities0ro: peedy co"putations% i"proved co""unication2colla!oration% i"provdata "anage"ent% 3uality and agility support% overco"ing cognitive li"its

b. Business Intelligence and Analytics refers to tec$ni3ues% tec$nologies% syste"s% ett$at analy,e critical !usiness data to $elp an enterprise !etter understand its !usinessand "ar#et and "a#e ti"ely !usiness decisions

4. List t$e "ost i"portant co"ponents of BI&A syste"sa. lide 4'5!. ata 6are$ouse Environ"ent (7ec$nical ta8)c. Business Analytics Environ"ent (Business users) *it$ user interfaced. 0erfor"ance and trategy (/anagers2executives) *it$ user interfacee. +eatures provided: reporting% das$!oard% oce integration% searc$!ased BI% "o!ile BI%

9LA0% interactive visuali,ation% scorecards% "etadata "anage"ent% colla!oration% etc.. -$aracteri,e t$e BI soft*are "ar#et and list "a;or players in it

a. -o"plete BI platfor"s:i. IB/% A0% A% 9racle% /% /icrotrategy

!. +ocus on selected BI strategies:i. Info


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a. Ele"ents:i. ata *are$ouse *it$ data and associated soft*areii. ata ac3uisition (!ac#end t$at extracts data fro" E0 syste"s and external

sources and su""ari,es t$e"iii. -lient (frontend) soft*are t$at allo*s access and analy,es data

!. -oncepts:i. ata ources: contains data to !e loaded into 6 (E0% 6e! ervices)ii. Enterprise 6 is a centrali,ed repository for t$e entire enterpriseiii. ata /art is a depart"ental 6 t$at stores only relevant data

c. +actors t$at can a8ect t$e arc$itecture:i. Infor"ation interdependence !et*een units% "anage"ent needs% urgency of ne

for 6% constraints on resources% co"pati!ility% tec$nical issues% social factorsd. Architecture selection: Kimball vs Inmon

i. Ci"!all vie*s data *are$ousing as a constituency of data "arts. ata "arts arefocused on delivering !usiness o!;ectives for depart"ents in t$e organi,ation. At$e data *are$ouse is a confor"ed di"ension of t$e data "arts. Dence a uniedvie* of t$e enterprise can !e o!tained fro" t$e di"ension "odeling on a localdepart"ental level.

ii. In"on !eliefs in creating a data *are$ouse on a su!;ect!ysu!;ect area !asis.Dence t$e develop"ent of t$e data *are$ouse can start *it$ data fro" t$e onlinstore. 9t$er su!;ect areas can !e added to t$e data *are$ouse as t$eir needs

arise. 0ointofsale (09) data can !e added later if "anage"ent decides it isnecessary.

iii.

ata /art Approac$ E6 Approac$Ci"!all In"on

9verallapproac$

Botto"@p 7opo*n

Arc$itecture structure

ata "art is su!;ect oriented

(e.g.% for single !usinessprocesses) or depart"entoriented(e.g.% only for ales)

Build one data "art at a

ti"e t$e 6 is developedse3uentially

6 F collection of data

"arts

9ne central E6 provides t$e

consistent and co"pre$ensivevie* of t$e enterprise

ata "arts are optional

supple"ents for specicdepart"ents or su!;ects

ata "arts are !ased on t$e

E6. 7$at "eans% t$ey gett$eir data fro" t$e E6.

-o"plexity Dig$ Lo*evelop"ent"et$odology

Iterative tep*ise

. Explain t$e "ultidi"ensional "odela. +ours perspectives: Botto"up vs. 7opdo*n% "ultidi"data"odel% relational data

"odels% "eta datab. A Multidimensional model is focused on data analysis% !ased on t$e follo*ing

ele"ents:i. /easura!le business facts (revenues)

ii. Business facts can !e vie*ed and analy,ed along dierent dimensions (ti"e%region)

iii. +acts and di"ensions "a#e up data cubes


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iv. tar c$e"a vs. no*a#e c$e"a

4. Explain t$e extraction% transfor"ation% and load !"#$% processes Data provision procesa. Extraction (reading data fro" data!ase)

i. ync$ronous access 2 async$ronous accessii. +ile !ased extraction 2 strea" !ased extractioniii. +ull extraction 2 delta extractioniv. @sage of lters 2 no lters

v. tandard extractors 2 custo" extractors!. 7ransfor"ation (conversion of data into needed for")

i. +iltering (e.g. lter all deleted orders)ii. Dar"oni,ation (e.g. resolve "aster data inconsistencies)iii. Enric$"ent (e.g. calculate ne* facts fro" existing ones)iv. Aggregation (e.g. !y "ini"i,ing a di"ension)

c. Load (place data in 6)i. uring t$is p$ase data is updatedii. Alternatives: +ull vs elta load% daily2*ee#ly2?iii. Das to !e custo"i,ed to t$e c$osen "odeliv. ata 3uality "ec$anis"s are often i"ple"ented to !e triggered during t$e load

d. @sually a triggered (auto"ated) process% logs and "onitoring $elps to nd errors auto"ation is very i"portant and can re3uire large portions of t$e pro;ect e8ort

. /etadata /anage"enta. /etadata is infor"ation a!out data (ta!les% colu"ns)!. /aster data is t$e opposite of transactional data% one entry per legal entityc. 7ransactional data is t$e opposite of "aster data% one entry per transfer

d. /eta vs. /astere. 7ypes of "etadata:

i. Business: explain *$at t$ings "ean (glossary *it$ ter"s and denitions


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ii. 7ec$nical: tec$nical description of data assets (ta!les% data stores% p$ysicalattri!utes)

iii. 9perational: "onitor ;o! execution (statistics a!out processes)f. Benets:

i. Build co""on understanding of dataii. +acilitate t$e 3uest for data 3ualityiii. upport discovery and reuse of dataiv. Analy,e dependenciesv. +acilitate future c$anges

vi. /onitor @sage

Lecture %4:

1. @nderstand t$e concept of Big ata and so"e typical applicationsa. B is a!out leveraging t$e extended capa!ilities to analy,e infor"ation and e"p$asi,e

diverse data sources and for"ats% volu"e only secondary role at".!. +our di"ensions of B t$at $ave to !e di8erentiated:

i. >olu"e: ata at large scaleii. >ariety: ata in "any for"s (structured% unstructured% text?)iii. >elocity: ata in "otion (analysis of strea"ing data to ena!le decisions in

fractions of a second)iv. >eracity: ata uncertainty ("anaging t$e relia!ility and predicta!ility of in$eren

i"precise data types'. Explain t$e tec$nological c$anges *$ic$ foster Big ata Analysis

a. Increasing data storage capacityfro" analog to digital!. Increasing co"putation capacityc. Increasing data collectiond. e*% e"erging data sources suc$ as social net*or#se. igiti,ation and connection of traditionally p$ysical devicesf. 7ec$nological c$anges and c$anges in t$e use of existing tec$nology processing 'H4!it% "ulticore% dis#J in"e"ory storage% data organi,ation: ro* column&vectorsdictionary encodingvery important because data is only stored

once and not every time a new item is createdg. lide ' as exa"ple5

. i8erentiate Big ata tec$nologies fro" traditional BI approac$esa. 7raditional vs. Big ata approac$ (structured% analytical% logical vs. creative% $olistic

t$oug$t% intuition)

Business @sers deter"ine *$at 3uestion toas#

Business users explores *$at 3uestion could!e as#ed

I7 structures t$e data to ans*er t$at 3uestion I7 delivers a platfor" to ena!le creativediscovery

7raditional -o"puting trea" -o"putingDistorical fact nding% nd stored infor"ation%3uery data results

-urrent fact nding% analy,e data in "otion!efore it is stored: data 3uery results

0ro!le"s of traditional 6 Goals of strea" co"putingata "ig$t !e outdated !efore users are a!leto analy,e it% ata rates and volu"es are too!ig for storing and su!se3uent analysis

eliver ti"ely insig$ts% focus attention toi"portant data% "onitor events fro" variety ofdata sources

lide 5

4. epict t$e i"plications of t$is tec$nology and its applicationa. Basics:

i. a"e data can !e represented using di8erent data "odels pic# t$e one t$at

supports t$e app


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ii. ata "odel denes a *ay of representing data in a d! and $o* an app accessest$e data (relational data "odel is "ost co""on)

iii. 7*o data "odel design: denor"ali,ed (e"!edded data "odels) and nor"ali,ed

enor"ali,ed data "odel(allo*s storage of related pieces of data

infor"ation in t$e sa"e data!ase record)

or"ali,ed data "odelor"ali,ed data "odels descri!e relations$ips

using references !et*een data pieces "ini"u" of redun...

K e3uest and retrieve related data in a singledata !ase operationK Better perfor"ance for read operationsK @pdate related data in a single *riteoperation ata!ase records "ay gro* after creation data!ase record gro*t$ can i"pact *riteperfor"ance t$reat of data frag"entation@se *$en you $ave 1:1 (contains)relations$ip or 1:n

K /ore exi!ility t$an denor"ali,ed data"odelsK o redundant data single trut$K In total% usually re3uires less storagecapacity t$an denor"ali,ed data "odels Accessing related data re3uires ;oining"ultiple pieces of data perfor"ance *$en accessing related dataif deno"ali,ed data "odels provide onlylittle read perfor"ance advantage% if "odeling/: relations$ips and large $ierarc$ical datasets

!. =L (structured 3uery language) relational data and2or nor"ali,ed data "odels

i. tandardi,ed. 0o*erful% $ig$ level progra""ing language for 3uerying data!asesupported !y al"ost all relational data!ases

ii. 0ro: standardi,ed% "any operations% "any people are a!le to *rite =L% Dig$ levcode% A-I

iii. -on: data structure needs to !e dened upfront% Al*ays $ave to !e translated tolo* level code% rat$er slo* execution

c. Application of relational data "odels:i. 7ransactions cas$ierii. Analysis of transactional data creation of !alance s$eets% trad. 6

d. o=L nonrelational data "odels and2or denor"ali,ed data "odelsi. Cey value store: ata is "apping fro" #eys to ar!itrary values% values do not

confor" to any particular structure

very si"ple to progra" and i"ple"ent% ca!e easily distri!uted across "ultiple "ac$inesii. ocu"entoriented storestill si"ple to progra" and i"ple"ent% easily

distri!uted% "ore structured t$an #eyvalue storesiii. Grap$oriented store: exi!le extension of data "odel

Lecture :

1. escri!e di8erent report types and target groups of BI consu"ptiona. Executive /anage"ent: 0erfor"ance /anage"ent% as$!oards% corecards% C0Is!. Business Analysts: AdDoc =ueries% 9nline Analytical 0rocessing (9LA0)c. +ront Line E"ployees: 9perational tandard eports

d.Business reporting Analytical reporting


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'. escri!e Business eporting and its i"portance to organi,ationsa. enition: Business 'eporting describes all types of BI consumption covering

e(cient !visual% communication with limited interactivity and limited analytic

capabilities.!. 7a!le eports: si"plest for" of BI reports (onedi"ensionallist% t*odi"ensional

"atrix report)i. /atrix report% periodic report% special report% exceptions?

. @nderstand !asic considerations of data visuali,ationa. 7$ree steps to visuali,e data:

i. -$oose >isual representation ("apping of availa!le infor"ation to a visualfor"at): data o!;ects% t$eir attri!utes% and t$e relations$ips a"ong data o!;ectsare translated into grap$ical ele"ents suc$ as points% lines% s$apes% and colors.

(i"ensions: grap$% data% interaction and distortion tec$ni3ues) Interaction$elps to encourage exploration and allo*s "ore dyna"ic analysis (ltering%,oo"ing).

ii. Arrange"ent (place"ent of availa!le visual ele"ents *it$in a display) of visualele"ents: can "a#e large di8erence in $o* easy it is to understand t$e data.

iii. election and (e) E"p$asis of interesting data: eli"ination or dee"p$asis ofuninteresting infor"ation and2or e"p$asis of interesting infor"ation

4. elect distinct grap$ types for specic visuali,ation goalsa. Bar grap$: good to co"pare values *it$ eac$ ot$er!. tac#ed grap$: good to display "ultiple instance of a *$ole and its parts focus on

*$ole

c. Grouped !ar grap$: good to display "ultiple instance of a *$ole and its parts focus opartsd. Line grap$: good to reveal s$ape of data% c$anges over ti"ee. par# Lines: very space ecient representation to display c$anges of "ultiple data set

in das$!oardsf. Area grap$:g. 0ie: good to display a *$ole and its parts !ut $as a*s$. 7ree "aps: spaceconstrained visuali,ation of $ierarc$ical structures easy to navigat

into su!treesi. adar grap$ !ar grap$ !etter

;. Gauges and "eters are pro!le"atic !ecause of a"ount of spaces needed and colorcoding

#. Box plots: displays t$e distri!ution of data% visuali,ation of core statistical para"etersl. catter plots: correlation structures can !e recogni,ed easily". e"ar#s: Less Is "ore no MMM

. Explain !estpractices of das$!oard designa. enition: A dashboard is a visual display of the most important information

needed to achieve one or more ob)ectives& consolidated and arranged on a

single screen so the information can be monitored at a glance. dashboards

tell us what*s happening!. 0ros: $ig$ i"pact visuali,ation of #ey "etrics% easy to use and nd infor"ation% intuitiv

"onitor and "anage "etrics% actiona!le analyses% drilla!le "etricsc. as$!oard design: arrange"ent% selection% visual layout of ut"ost i"portance value

of color code% less is "ore% co"pact visuali,ation% $o"ogeneous usage of grap$ types


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d. Gestalt 0rinciples (unied *$ole): atte"pts to descri!e $o* people preconsciouslyorgani,e visual ele"ents into groups or unied *$oles *$en certain principles areappliedproxi"ity% si"ilarity% closure% enclosure% connection% continuity

e. 1 co""on "ista#es5

Lecture H% N:

1. @nderstand t$e i"portance of 9LA0 reports and descri!e t$e !asic concepts of onlineanalytical processing (9LA0) and $o* it can i"prove decision "a#ing

a. 9LA0 F 7ec$nologies and tools t$at support (ad$oc) analysis of "ultidi"ensionally

aggregated data data aggregation% fast data analysis along "ultiple di"ensions% preprocess data fro" "ultiple sources

i. /9LA0: data resides in a "ultidi"ensional /B% "ultidi". Engine providesaccess

ii. 9LA0: data resides in a relational B/% 9LA0 server provides =L 3ueriesiii. D9LA0: detailed data resides in a relational B/% aggregated data resides in a

"ultidi". B/!. K/9LA0: fast 3uery perfor"ance (opti"i,ed storage and "ultidi". Indexing)%

auto"ated co"putation of $ig$er level aggregates% very co"pact for lo* di"ension dasets /9LA0: processing step can ta#e ti"e% traditionally dicult to 3uery "odels *it$ $ig$cardinality di"ensions% often creates redundancy

c. 7ransaction processing and data analysis are do"inant approac$es for I7 infrastructure!ut ad$oc and exploratory reports are gaining i"portance unclear if 9LA0 is replace

d. -oddOs 1' rules for 9LA0:i. /ultidi"ensional conceptual vie*: tructure data along di"ensionsii. 7ransparency: (@nfor"atted) source of data is not visi!le to end useriii. .Accessi!ility: 7ool (not user) provides data sourcingiv. 4.-onsistent reporting perfor"ance: o signicant perfor"ance i"pacts !y

increasing di"ension nu"!ersv. .-lient2server arc$itecture: >arious clients can !e attac$ed to one server

vi. H.Generic di"ensionality: E3uivalent operational capa!ilities for all di"ensionsvii. yna"ic sparse "atrix $andling: 9pti"al $andling of a sparse "atrix

viii. P. /ultiuser support: o concurrency restrictionsix. Q. @nrestricted crossdi"ensional operations: -alculation 2 /anipulation acrossunli"ited di"ensions

x. 1R. Intuitive data "anipulation: o need for "enus !ut direct interaction *it$ t$data

xi. 11. +lexi!le reporting: e.g. exi!le visuali,ationxii. 1'. @nli"ited di"ensions and aggregation levels

e. 7ypical 9LA0 operations:i. oll up (drillup) su""ari,e data !y cli"!ing up $ierarc$y or !y di"ension

reductionii. rill do*n (roll do*n): reverse of rollup fro" $ig$er level su""ary to lo*er lev

su""ary or detailed data

iii. lice and dice lter using one or "ore di"ensioniv. 0ivot (rotate) reorient t$e cu!e% visuali,ation% to series of ' planes.v. 9t$er operations drill across: involving (across) "ore t$an one fact ta!le% drill

t$roug$: t$roug$ t$e !otto" level of t$e cu!e to its !ac#end relational ta!les(using =L)

'. escri!e t$e c$aracteristics of advanced analytics tec$ni3ues and $o* t$ey "ay generate ne#no*ledge

a. /otivation: a"ount of data constantly gro*ing 9LA0 etc. not sucient any"ore "et$ods and tools t$at auto"atically generate #no*ledge fro" large data sets anddocu"ents are needed to tac#le c$allenges li#e nding ano"alies% forecasting% #eyinuencers% relations$ips and trend


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!. enition: Knowledge Discovery in Databases !KDD% is the non+trivial process identifying valid& novel& potentially useful& and ultimately understandable

patterns in data.i. Dypot$esis approac$: @ser "a#es a proposition and see#s to validate itii. iscovery approac$: +inds patterns% associations% relations$ips t$at *ere

previously un#no*n to t$e organi,ationiii. upervised learning: predict data *it$ un#no*n target attri!ute value *it$

"ini"al error searc$ for dependencies pf a target attri!ute on t$e input dataiv. @nsupervised learning: -reate a pattern of a "ore co"pact description of t$e da

no reference to target attri!ute% error not "easura!lev. enition: Data Mining is a process that uses statistical& mathematical&arti,cial intelligence& and machine learning techni-ues to etract and

identify useful information and subse-uent /nowledge from databases.

Data mining is used for ,nding mathematical patterns from usually larg

sets of data. #hese patterns can be rules& a(nities& correlations& trend

or prediction modelsvi. Association ules: descri!e correlations !et*een attri!utes appearing toget$er i

transactions (learn "ore55 .4R?)vii. ecision 7rees: are a set of logical rules t$e pat$ t$at leads fro" t$e leaf to a

specic class represent a set of !oundary conditions easy to readc. 7ypes of Analytics 7$e 0ractitioners >ie*

i. escriptive Analytics: u""ari,e *$at $appened trad. Analytics and 9LA0ii. 0redictive Analytics: /a#e predictions a!out t$e future variety of tec$ni3ues

t$e process of discovering "eaningful ne* correlations !y sifting t$roug$ largea"ounts of data using pattern recognition% statistical and "at$e"aticaltec$ni3ues

iii. 0rescriptive Analytics: eco""end one or "ore causes of action and s$o* "ostli#ely outco"e of t$e action actiona!le data and feed!ac# re3uired in order tolearn continuously

d. enition: #et mining is the application of data mining to non+structured orless structured tet ,les. It entails the generation of meaningful numerical

indices from the unstructured tet and then processing these indices using

various data mining algorithms large databases.i. Exa"ple: spa" recognition% $elp des#s% analysis of related scientic pu!lications

e. enition: 0eb mining is the discovery and analysis of interesting and usefulinformation from the 0eb& about the 0eb& and usually through 0eb+based

tools.


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i. Exa"ple: clic#strea" analysis% product reco""endations% intelligent !ro*singassistance

f. 6e! @sage /ining: iscovery of "eaningful patterns fro" data generated !y 6e! cliee.g. user proles2ratings% "etadata% s$opping cart c$anges% product clic#t$roug$s

. Explain $o* recent BI&A tec$nologies "ay contri!ute to Business 0erfor"ance /anage"entand 0rocess /anage"ent

a. enition: Business 1erformance Management enables an organi2ation toeectively monitor& control and manage the implementation of strategic

initiatives

!. Business 0lanning is creating alternatives for action and deciding on t$e "ost pro"isinpat$"ostly done *it$ oce tools (spreads$eets)i. K s"all !usinesses% extre"ely individual re3uire"ents% s$ortter" needii. 0rocess control% access protection% perfor"ance% co"plexity% errors%

consolidation% etc.iii. preads$eets good for individuals *it$ need for $ig$ exi!ilityiv. E0 syste"s are good for very detailed planningv. pecial 0lanning soft*are allo*s rapid deploy"ent of precongured planning

"odelsvi. 9LA0 is t$e rst c$oice for co"pany*ide% centrally"odeled planning

c. A balanced scorecard is a comprehensive set of performance measures de,n

from four dierent measurement perspectives !,nancial& customer& internal&

and learning and growth% that provides a framewor/ for translating thebusiness strategy into operational terms

d. Business Intelligencei. BI in classical for" is $ig$level orientedii. 0rocess Intelligence focuses on operational perfor"ance to !e transparent at all

ti"esiii. B1I comprises a large range of application areas spanning from process

monitoring and analysis to process discovery& conformance chec/ing&

prediction and optimi2ation.


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iv. -$allenge: /ultiple actors need realti"e infor"ation a!out !usiness processest$at is tailored to t$eir needs approac$: integrating all data in an in"e"orydata!ase

Lecture P% Q:

1. Explain di8erent ele"ents of a BI strategya. 3rgani2ational Adoption(process) involves all actions of individuals in an organi,ati

t$at deal *it$ creating a*areness% selecting% evaluating% initiating and deciding for t$ei"ple"entation of ne* E tec$nology.

!. 4onversion (process) involves all actions of individuals (in an organi,ation or acrossorgani,ations) t$at deal *it$ developing and i"ple"enting a ne* E tec$nology.

c. 5se (process) involves all actions of individuals in an organi,ation t$at deal *it$ usingand c$anging E tec$nology or t$e respective *or# syste" to reali,e intended !usinesvalue.

'. -$aracteri,e di8erent types of #ey perfor"ance indicators and understand t$e i"portance ofalign"ent

a. C0I (Cey 0erfor"ance Indicator) =uarter2Sear /otivation Index!. 90I (9perational 0erfor"ance Indicator) 6ee#s2/ont$s Dealt$ ratec. 00I (0rocess 0erfor"ance Indicator) Dours2days Accidentsd. 0ros of BI strategy: $elp align *it$ !usiness partners% for"ali,e !usiness needs% create

prioriti,ed road"aps *it$ strategic !usiness goals to deliver "easura!le resultse. Align"ent is a continuous processf. ispositive (t$e data contained in data *are$ouses) ata Arc$itecture: $ig$ level

reference "odel for all BI pro;ects of an enterprise tarting fro" t$is $ig$ level "odeindividual pro;ects develop a concrete dispositive data "odel gap detection t$roug$"apping to existing data sources

g. BI Governance: ata /onitoringAdapt entire BI tec$nology stac# (soft*are% $ard*arnet*or#s) to tec$nological c$ange and increasing data volu"es

$. Governance: evelop"ent /odel

i. EALLS BA s. 1H 'Q. @nderstand t$e !asics of BI i"ple"entation and t$e i"portance of BI trategy for BII"ple"entation

a. isparate !usiness data "ust !e integrated need for infor"ation consolidationalign"ent across organi,ations regarding "aster data and C0Is% ne* tec$nology c$anga lot

i. 9rgani,ational Issuesii. 0ro;ect Issuesiii. 7ec$nical Issuesiv. @ser participation in t$e develop"ent is crucial

!. -o""on failure factors in BI pro;ectsi. @nclear !usiness 2 infor"ation o!;ectives


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ii. Lo* levels of data su""ari,ation: Getting lost in detailiii. Lac# of (7op) /anage"ent upportiv. Lac# of clear BI trategyv. -ultural issues !eing ignored

vi. Inappropriate arc$itecture4. Cno* a stateoft$eart pro;ect i"ple"entation "et$odology in principle

a. Best 0ractices for BI I"ple"entationi. 0ro;ect r to corporate strategyii. -o"plete !uyin of "anagersiii. /anage user expectationsiv. 6 s$ould !e !uilt incre"entallyv. Build in adapta!ility

vi. Do*5 I72Business "anage toget$er% training% political a*areness% organi,ationalsupport% etc?

b. 6ood scalability means that -ueries and other data+access functions will grow

linearly with the si2e of the systemc. Security focus on policies and procedures& logical security and restrictions&

limiting physical access& internal control with emphasis on security and priva. Al"ost every #ind of engineering pro;ect goes t$roug$ six stages !et*een inception and

i"ple"entation. Engineering pro;ects are oftenti"es iterative. 9nce deployed% products arerened and i"proved. Eac$ iteration produces a ne* release.

a. Al*ays $ave a core tea" (1RRT availa!le resources% per"anent% diverse) and anextended tea" K so"e executive representatives!. 7rac#s run in parallel after t$e pro;ect re3uire"ents $ave !een dened

i. UE7L 7rac# F !ac#endii. UApplication 7rac# F frontendiii. U/etadata 7rac# F !ridge 2 navigation

c. Vustication is a!out s$o*ing t$e !alance !et*een t$e costs involved and t$e !enetsgained (4 co"ponents)

i. Business analysis issuesii. is# assess"entiii. -ost!enet analysisiv. Business drivers ("ap to strategic !usiness goals)

H. -$aracteri,e t$e individual stages and associated steps of t$e "et$odology in "ore detail sli'?


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6ill der *ir#lic$e 1H verda""te 0$asen einen BI Integration $Wren5 /ir reic$en die Htages?..alles andere *Xrde e$er ,u I4R passenM

Lecture 1R:

1. Explain t$e i"portance of postadoption BI syste" use for t$e return on BI tec$nologyinvest"ents

a. eturns on I invest"ents are "ainly gained *it$in t$e postadoption stagesM

'. i8erentiate !et*een t*o co""on postadoption BI syste" use stagesa. 'outini2ation: State in which IS use is integrated as a normal part of the

employees7 wor/ processesi. epetitious *or#ii. 0erceived as a nor"al part of e"ployeesY *or# activitiesiii. tandardi,ed *or#iv. Incorporated into e"ployeesY *or# processesv. E"ployees develop fa"iliarity *it$ t$e i"ple"ented I

b. Infusion: "mbedding IS deeply and comprehensively in wor/ processesi. eali,ation of $idden value of an Iii. Extension of t$e I (e.g.% developing additional features)iii. Infusion and outini,ation do not necessarily occur in se3uence !ut rat$er

occur in parallel1. E"ployees can display eit$er !e$avior at a precise point in ti"eZ !ut

t$ey can also display !ot$ !e$aviors *it$in a period of ti"e'. Bot$ !e$aviors are expected to vary across e"ployees

c.

Guest Lecture:

eally5


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bi practices

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