صفحه 1:
010 رک ی ‎ON‏ OES rac 4 ‏سم ل‎ 4000 OeAd ices 5۰۱/۹۹۵

صفحه 2:
‎Oberives‏ ور ‎۱ ‏ل‎ eaccd 0 eariicz < ‏صصجصوصم اشردمی وله رها عاسجو()‎ (ODO ‏ا‎ aU PAC EASA} NON Ne Nera GI= Pee PAL PACE, C1 a ELD ANI Et CIC IPAC IRS EASI NSCS ۱ can eteas tas inn eas Smeal eas eg Peed enced (CO ‏ور مر‎

صفحه 3:
‎Oberives‏ ور ‎2) ۰ ‎۹ ‎4 easiness Dress! ت‎ ۱۱۳ ‏من در رز‎ Ear ‏کی ‎eles era ese Oe) Cp)‏ ۱۹

صفحه 4:
رح( 2 ور ۱ ل م ‏ ل ل ل ۱ 7 OAc ae ‏ل‎ A Oe Na cS ‏ار رز‎ ۰ Dez ance ‎CARS A Dera‏ ل ال 0ك ‎eect ows eben er era ee bicd casera ice ‏اك‎ aU ec eae Ae BESTE ‏ا ا‎ teat a CL bce Cod (ECCS ‏ل‎ ‎product. Ousppare itty Oracle Retail (see ‎DALE ‏وا‎ ‎Orive ‏دص‎

صفحه 5:
Oveniew 6 ‏مس بر مسر را‎ ۹۱ ‏ال ل ل‎ NU AAS ‏ا‎ 7 021 ‏رمرم‎ ood 7

صفحه 6:
ANU cathe Dr (®@) ‏تشرط‎

صفحه 7:
Pke Oustess Paabics (BO) Piet: Oa Overview ‏22ص مسر‎ OO toe: Oa ed Ye ‏اش‎ ‎٩ ۳۵۵۵ ‏رو‎ Oube umipsis ۱ ea ‏یت مس‎ ‏ا رو ور رح‎ ۹۳ ‏ا ار‎

صفحه 8:
ANU cathe Dr (®@) ‏تشرط‎

صفحه 9:
Overview > OC's cossficaive oP strategic euerprise ‏بسح مت‎ ‏مر مرس سم ا‎ Operative 07 ‏دمصي‎ ‎ea‏ وت

صفحه 10:
Oveniew مسر مر ملس بر مرس تسا ‎VO‏ ‏ام مرس مصعم لاجد ررعرة سا صصعووت لیم زور۳ مر ل ا ا ۱۱ 0000 ا کی سس سس ۶ ‎EASI=a‏ دمم رد دی ةا ‎CLIN asleacat aU CLM‏ ا ل ا

صفحه 11:
Overview > Orikbwea ‏رم 0ك‎ (EAN Piodiog 2 ‏ا ل ی‎ رصم امرس را

صفحه 12:
ا م ۵ را رتسم لس سس سر CN ‏ا ل‎ ca ce TE ane fact (ml OOM rane Ure ‏ری مر‎ Sesh 0 cs EA EAS ‏را مر سل رم للك‎ ‏ما‎ در زر ۱ IN AION eae ecuea nese ‏مر رد زر مر‎

صفحه 13:
۵ | eee eg ORC CD] > OLOP versus OLTP ‏ل از ا‎ neat OC ie ‏ری‎ PARIS EAS 7 ‏7ط‎ ‎0 ‏ا ا ا‎ eile be cL 0 ‏ر ا ا‎ OUT ‏کر در ا ا‎

صفحه 14:
Qepors und Quertes > Reports 0 Cs eaten rie © Od hor (or verdeovaend) reports © ‏تنل(‎ support Seas ‏ار‎ CN ELAS acd ا ار ۱ ۶ أداصدجاعيم! نوه جأتخحصهجاا دمشنجاصعاك اعرصمرج ةا وم جأسعك لججوج ماكو روه جه اأعس جد وصاادر وجل ‎Se ee‏ ‎a SCE Nee ca‏ ‎Qucwair vooteut persvoaization,‏

صفحه 15:
Qepors und Quertes 6 ‏ندعب راز‎ 4 ACNE EAN El es OLNCLEAC Dia eee ted NE EASES Ean nie UA ‏اس و‎ © Ginutured Query bop (GAL) 6 ‏ار در را مت رت زر‎ Ea fee ‏دی رم زک من سم‎ ‏ی رن رم‎ ۱۹ Ge

صفحه 16:
(۳ rican cr eas) Bd LONG ences ae, ‎ie]‏ یر روت ‎ee ye Pace by eric oe BRS ane‏ ا 3ك ‎by severd dipeusious, suck us sules by nexion,‏ ‎by product, by sulespersva, ood by teve (Pour‏ ۳ ‎Se ee‏ ‏رز( با ‏سم( ۷ ‎۱ (7

صفحه 17:
(2 serseaseg case UZ سم ای مر را ۶ ام ‎CON‏ ‎RS NL‏ یت رصم ‎Daca‏ ری ۱ 2 )(۵ ‏علی‎ ‎۹ h Cg 0 tc - ‏ار مرو و رت و رای مرول‎ tee Se sf fees eae CANIN EAC A ee ELS eer]

صفحه 18:
(2 serseaseg case UZ 9 5 أدكاز لمك لعواعم رهز برأكاكا ات اعوجانك 09 ‎DANS EAN Soe‏ عواجابومج ما جعموسن 2 رز ‎Tes‏ ‏۳۵۵7 رعس لصم ۲ 5۱۲7۷ حانج 0 10 ‎ANSEL a Zc LNA cal (cS‏ 0 ‎LCA recs ca clara Bee‏ ا ل 0 ار رت سر تم ‎cr oe ne‏ و 00 7 "۱717۳17171171 ۲0 17۳1

صفحه 19:
Nuts Sorews Boks Washors Products (P) use Sorows Bote Washers 0

صفحه 20:
(2 serseaseg case UZ مر مر ‎COO reat‏ و ماه وروی هلال ‎OL‏ رن و رت ار 2 ‎FAS‏ ‎rcs‏ ار اک 5

صفحه 21:
ANNI سس 0 SSS لس PAR Analysis Cubes

صفحه 22:
(2 serseaseg case UZ 0 ‎ASC‏ 7 ك0" ‎EAI | CASS ISIS Ne Cac red‏ لجمتى تدك هد حمجاا بكممما ‎TCAs, ACL ELLER =A‏ ‎cL]‏ تم ی( و ‎ce‏ لمم أدوم ادامر ‎0 ‏ا‎ ص‎ ee CLASS ‏ا ا‎ ca Net Bee ea cca AC IR SPAS ha EAS SSI EANS ANSE Mees ‏هم م۳‎ EAS Cec

صفحه 23:
6 (pase eer ‎a wedi ted‏ ی لكا سس سس انا صن ا ‎Oe Ace ese‏ یت لد ی سس مد ی ‎Dea a ashe fd DS cecal CL ca PANS Cac CAS ca ANN‏ موس ۱۳۸ ‎eae seh eee‏ ا ا ا 0 ‎a‏

صفحه 24:
“طناك للك حار كان () 2 0 71 ی 000 5 اوه مس اه نع لت تنل ‎VA cans) cans PAs ean Da ۳‏ رس ره ۶ ی ‎ed‏ و رت دص ی رز رت رز وا اد لاح رو ل ا امد مد 1 ۱ ۱۳/717۹ رما

صفحه 25:
ا 9 0 ‏ا‎ AO Pac cast ‏ل 0 لي‎ ‏ا ا ا ا ان‎ g ‏ا ال كينا‎ es =e ۱

صفحه 26:
هیر رگ را Visual Spreadsheet of Risk Analysis "سردا

صفحه 27:

صفحه 28:
ا 6 ‏مسر سح مراک‎ ۷ ‏سم لس مسر را‎ 0 00 PEED SA Ns rissi Rca cscr/ eae Pie la cin pases he csreom ‏تسس سبط رس ری را‎ 0 ۹ ۱ ل ل م مر رد رام ۳ صرحت . (دك (1) سد »)سجرب 1 در ۱۹۳ پر ا ا ۱ ۷

صفحه 29:
ee ‏ور‎ کر ور مر م۹ ار 7 ۱۹ مر مت سم ‎ae‏ مر ۱ ‎Tee eS cles oe‏

صفحه 30:
ور ‎ee‏ ‏کر ور مر م۹ ‎AEs PASE ea aca NN | Pca EL La Pa ca‏ دادما ‎Os O16‏ > ا ا ا 0 :له ع مدلل جا ‎Ne eS‏ ات مه م۹ 0 ام و سم ل ل ‎ere ca VANS‏ ‎etme PA crac‏ وان ما ور را ۳ ۰۱

صفحه 31:
ور ‎ee‏ ‏کر ور مر م۹ سارت ها عا مدمه وونل ‎CART, Em‏ لصص وه ورن ۳18) ۰ ل 0 ‎Oe ae ant cee ca ce‏ ا تمس م۱۱ و سس سمل سا سر ور مرا ‎CLT‏ تم مرا وا ‎PAS EAC ENING fa Es ELLER LEASE‏ و

صفحه 32:
ee ‏ور‎ ٩ ‏مس ورس رمرم‎ (616) * O16 cowbiced wits BPG * Clobdl posticaicny systews (BPG) ee carom ‏ی‎ cca CA AC canceled ed ۱ eer AS CASS ‏خام‎ casa (can ‏عه جوت‎ رم ا ا ۱ مس مر

صفحه 33:
ور ‎ee‏ ‏کر ور مر م۹ اه لصو عدا لحه 18 2 ‎neice‏ اک ی مت ل مراک وا مت 0 ‏مت مه 051086 ‎٠»‏ ‏ام سم سم ‏ا ل ا رت وا 0 ‎ual iciecl eee)‏ کر 7.7 ۱7-2 7(

صفحه 34:
1 ناا دص ار ۱ ‎Vad ed acc fc Pac a CoA‏ طح رت رپ رم ما ا ۱ VON ar ae nod aU canis ie aera ‏ا‎ ‎۱ ‏را خر‎ i pperoiocdl aed ‏ا‎ sv CSUN ‏مر‎ (PLO pe eres ‏رد ار‎ eset cad NAN ce acai ASEAN CAN CZ ea EASICa

صفحه 35:
ل 0 Oreck: ‏ا‎ ‎0 ‏ا و‎ ‏ال ل‎ 7 ‏دم د‎ Fee aA aa cea ARTA PA CAINS TAS Rae nc ace eae مر در ۱ ‎a VAGINAS SV Cacsca cecal ee‏ ل ال دوک با ‎Taal rast rime (EOD Coy = aiesereee‏

صفحه 36:
۹ ‏رت‎ cd Dc Ak Desai pect Ne ‏لس تس‎ 0 66 م جاع( عا مجك( ۶ 2-0-3382 ‏زر(‎ ec ere acres cy ees icra oe Na aS ae fe AN cad

صفحه 37:
®@ uad the Orb: Orb Teteligeare cart Orb @udlpics ۶ ٩ ‏رب مرحم ر‎ 4 ‏اک رب‎ ied ۳ TH. ۶ ۹ ‏دسر سر سم ر‎ ‏نز۱۱۱‎ untiviies cord FUNDA nea racial cA a Ez ACCA (cea Pe LEAN Cen aed Via Ea DU ea be aci cioe Koy trary)

صفحه 38:
‎Shot om ۵ ۳ Vi‏ مسب

صفحه 39:
Osage, Bruehits, wad Guovess oF BO ~ Osage ‏<م‎ OD ا ل ل ل لل ره با _ ل ا ل ل ۱۹/۹ ا 0 ا ۱۱۳2 دی مت مه لل مر ما( وا ‎ANNU SLC I ADCS ceca‏ ۱ ‎PAN eas LES ANSNS LEAH DAE PAC =P ceca‏ ۱ ‎uaee cece‏ رب

صفحه 40:
Osu, (ee ay wad Guovess oF BO Nocera hae an 0 ‏ره با‎ cceeal (nO) acre ieee ‏ا‎ eet as Dr AST CAS SNS esas TAS Cte ica cA ARS SANS CSc ۱ ‏بوجواعمصر جز عععوصت‎ tes fe ‏عاوصس‎

صفحه 41:
Osu, (ee ay wad Guovess oF BO < ‏لها صعصؤصم 0641/0608 روا(‎ ۰ ‏ا ال را‎ mecca ‏تسم ی رت و بر مر‎ ۱ ‏مب یت مر‎ ‏ند‎ ‏ما رز(‎ صصص یمس هی رم

صفحه 42:
Osage, @ruetits, NS CAC car (or) OSC ar aan | @ back oP shiled (or avalable) ‏مر‎ ‎5 4 casei (Beers ‏رن مر‎ Ded ENS. EAS) ‏ل ا 4 ع‎ 0 307 00 (

صفحه 43:
‎LC as ae‏ مه( ‎un Guovess oP OD‏ ‎Oh O80 proect Pal‏ * ‎PAs]‏ ا ل ‎٩۱‏ ۰ ‎BIA Acie‏ ‎LO‏ رم رد اک ۱ ‎hac SLE‏ 0 ات ا ‎ane‏ ,2 ۱ ‏یت مر ك2 ‎Co‏ ‏111

صفحه 44:
Osage, @ruetits, Nee CA ‏رت‎ pe el Cp) la Gpstew developed ded the aeed Por iotegraiva 0 4 ‏ل ا‎ » 2 ‏روصتم يهو‎ EU Dc Del ‏عاط بجع تمك وأاولك بعود سردن خات‎ ‏را ره ل ل‎ pal mrs ‏كم‎

Introduction to Business Analytics Chapter 3: Business Analytics and Data Visualization Matthew J. Liberatore Thomas Coghlan Fall 2008 Learning Objectives  List and briefly describe the major BA methods and tools  Describe how online analytical processing (OLAP), data visualization, and multidimensionality can improve decision making  Describe geographical information systems (GIS) and their support to decision making Learning Objectives  Describe real-time BA  Explain how the Web relates to BA  Describe Web intelligence and Web analytics and their importance to organizations  Describe implementation issues related to BA and success factors for BA Lexmark Improves Operations with BI 1. 2. 3. 4. 5. Identify the challenges Lexmark faced regarding information flow How were the information flows provided before and after implementation of the system? Identify the decisions supported by the new system. How can the new system improve customer service? Go to http://www.sas.com/industry/retail/tour/itour_noflash.html and take the interactive tour of the SAS Retail Intelligence product. Compare it to Oracle Retail (see http://www.oracle.com/applications/retail.html) and Oracle Active Retail Intelligence in particular The Business Analytics (BA) Field: An Overview  Business Analytics The use of analytical methods, either manually or automatically, to derive relationships from data  Remember that we defined business analytics (BA) to include the access, reporting, and analysis of data supported by software to drive business performance and decision making The Business Analytics (BA) Field: An Overview The Business Analytics (BA) Field: An Overview MicroStrategy’s classification of BA tools: The five styles of BI  1. 2. 3. 4. 5. Enterprise reporting Cube analysis Ad hoc querying and analysis Statistical analysis and data mining Report delivery and alerting The Business Analytics (BA) Field: An Overview The Business Analytics (BA) Field: An Overview  SAP’s classification of strategic enterprise management  Three levels of support 1. 2. 3. Operational Managerial Strategic The Business Analytics (BA) Field: An Overview  Executive information and support systems   Executive information systems (EIS) Provides rapid access to timely and relevant information aiding in monitoring an organization’s performance Executive support systems (ESS) Also provides analysis support, communications, office automation, and intelligence support The Business Analytics (BA) Field: An Overview  Drill-down The investigation of information in detail (e.g., finding not only total sales but also sales by region, by product, or by salesperson). Finding the detailed sources Online Analytical Processing (OLAP)   Online analytical processing (OLAP) An information system that enables the user, while at a PC, to query the system, conduct an analysis, and so on. The result is generated in seconds Some applications can be found at: http://www.olapreport.com/ CaseStudiesIndex.htm Online Analytical Processing (OLAP)  OLAP versus OLTP     OLTP concentrates on processing repetitive transactions in large quantities and conducting simple manipulations OLAP involves examining many data items complex relationships OLAP may analyze relationships and look for patterns, trends, and exceptions OLAP is a direct decision support method Reports and Queries  Reports      Routine reports Ad hoc (or on-demand) reports Multilingual support Scorecards and dashboards Report delivery and alerting • • • • Report distribution through any touchpoint Self-subscription as well as administrator-based distribution Delivery on-demand, on-schedule, or on-event Automatic content personalization Reports and Queries  Ad hoc query A query that cannot be determined prior to the moment the query is issued  Structured Query Language (SQL) A data definition and management language for relational databases. SQL front ends most relational DBMS Multidimensionality  Multidimensionality The ability to organize, present, and analyze data by several dimensions, such as sales by region, by product, by salesperson, and by time (four dimensions)  Multidimensional presentation    Dimensions Measures Time Multidimensionality  Multidimensional database A database in which the data are organized specifically to support easy and quick multidimensional analysis  Data cube A two-dimensional, three-dimensional, or higherdimensional object in which each dimension of the data represents a measure of interest Multidimensionality  Cube A subset of highly interrelated data that is organized to allow users to combine any attributes in a cube (e.g., stores, products, customers, suppliers) with any metrics in the cube (e.g., sales, profit, units, age) to create various twodimensional views, or slices, that can be displayed on a computer screen Multidimensionality Multidimensionality  Multidimensional tools and vendors   Tools with multidimensional capabilities often work in conjunction with database query systems and other OLAP tools Temtec Executive Viewer Multidimensionality Multidimensionality  Limitations of dimensionality     The multidimensional database can take up significantly more computer storage room than a summarized relational database Multidimensional products cost significantly more than standard relational products Database loading consumes significant system resources and time, depending on data volume and the number of dimensions Interfaces and maintenance are more complex in multidimensional databases than in relational databases Advanced Business Analytics  Data mining and predictive analysis    Data mining Predictive analysis Use of tools that help determine the probable future outcome for an event or the likelihood of a situation occurring. These tools also identify relationships and patterns Several data mining tools will be discussed later Data Visualization  Data visualization A graphical, animation, or video presentation of data and the results of data analysis   The ability to quickly identify important trends in corporate and market data can provide competitive advantage Check their magnitude of trends by using predictive models that provide significant business advantages in applications that drive content, transactions, or processes Data Visualization  New directions in data visualization  In the 1990s data visualization has moved into:   Mainstream computing, where it is integrated with decision support tools and applications Intelligent visualization, which includes data (information) interpretation Data Visualization Data Visualization Data Visualization  New directions in data visualization   Dashboards and scorecards Visual analysis http://www.lumina.com/software/influencediagrams. html influence diagrams   Financial data visualization Tree map examples:  http://www.robkerr.com/post/2008/04/ Favorite-Visualization-2-e28093-ThePerformance-Map-(Heat-Map).aspx  http://visudemos.ilog.com/webdemos/treemap/treemap.html Geographic Information Systems (GIS)  Geographical information system (GIS) An information system that uses spatial data, such as digitized maps. A GIS is a combination of text, graphics, icons, and symbols on maps Geographic Information Systems (GIS)  As GIS tools become increasingly sophisticated and affordable, they help more companies and governments understand:    Precisely where their trucks, workers, and resources are located Where they need to go to service a customer The best way to get from here to there Geographic Information Systems (GIS)  GIS and decision making  GIS applications are used to improve decision making in the public and private sectors including: • • • • •  Dispatch of emergency vehicles Transit management Facility site selection Drought risk management Wildlife management Local governments use GIS applications for used mapping and other decision-making applications Geographic Information Systems (GIS)  GIS combined with GPS  Global positioning systems (GPS) Wireless devices that use satellites to enable users to detect the position on earth of items (e.g., cars or people) the devices are attached to, with reasonable precision Geographic Information Systems (GIS)  GIS and the Internet/intranets     Most major GIS software vendors provide Web access that hooks directly to their software GIS can help the manager of a retail operation determine where to locate retail outlets Some firms are deploying GIS on the Internet for internal use or for use by their customers (locate the closest store location) http://www.360networks.com/includes/popups/ rate_center_map/map.asp Real-Time BI  The trend toward BI software producing real- time data updates for real-time analysis and realtime decision making is growing rapidly  Part of this push involves getting the right information to operational and tactical personnel so that they can use new BA tools and up-to-theminute results to make decisions Real-Time BI  Concerns about real-time systems    An important issue in real-time computing is that not all data should be updated continuously when reports are generated in real-time because one person’s results may not match another person’s causing confusion Real-time data are necessary in many cases for the creation of ADS systems BA and the Web: Web Intelligence and Web Analytics  Using the Web in BA  Web analytics The application of business analytics activities to Web-based processes, including e-commerce BA and the Web: Web Intelligence and Web Analytics  Clickstream analysis The analysis of data that occur in the Web environment.  Clickstream data Data that provide a trail of the user’s activities and show the user’s browsing patterns (e.g., which sites are visited, which pages, how long) BA and the Web: Web Intelligence and Web Analytics Usage, Benefits, and Success of BA  Usage of BA   Almost all managers and executives can use some BA systems, but some find the tools too complicated to use or they are not trained properly. Most businesses want a greater percentage of the enterprise to leverage analytics; most of the challenges related to technology adoption involve culture, people, and processes Usage, Benefits, and Success of BA  Success and usability of BA  Performance management systems (PMS) are BI tools that provide scorecards and other relevant information that decision makers use to determine their level of success in reaching their goals Usage, Benefits, and Success of BA Why BI/BA projects fail  1. 2. 3. Failure to recognize BI projects as crossorganizational business initiatives and to understand that, as such, they differ from typical standalone solutions Unengaged or weak business sponsors Unavailable or unwilling business representatives from the functional areas Usage, Benefits, and Success of BA  Why BI/BA projects fail 4. 5. 6. Lack of skilled (or available) staff, or suboptimal staff utilization No software release concept (i.e., no iterative development method) No work breakdown structure (i.e., no methodology) Usage, Benefits, and Success of BA  Why BI/BA projects fail 7. 8. 9. 10. No business analysis or standardization activities No appreciation of the negative impact of “dirty data” on business profitability No understanding of the necessity for and the use of metadata Too much reliance on disparate methods and tools Usage, Benefits, and Success of BA  System development and the need for integration   Developing an effective BI decision support application can be fairly complex Integration, whether of applications, data sources, or even development environment, is a major CSF for BI

51,000 تومان