Software Consolidation and Its Impact on Data Management

Software Consolidation and Its Impact on Data Management Presented by: Larry Goldman DAMA–MN April 16, 2008

Introduction to AmberLeaf Business Intelligence Vision Consolidation History Customer Benefits and Impacts Agenda DATA INFORMATION INSIGHT STRATEGY ACTION

Breaking Down the Barriers Urgency, Speed, Focus, Expertise, Results Action Strategy Information Insight • Customer Experience Optimization • Marketing Automation • Personalization • Process Design • Change Agents • Service Optimization • Sales Effectiveness • Loyalty Programs • Advanced Analytics • Customer Valuation • Segmentation • Marketing Optimization • Customer Experience Strategy • Sales • Service • Marketing • Data Warehouse/Data Mart strategy, design and development • Data Integration (ETL on Demand) • Data Quality • Customer Information Hygiene • Reporting Architectures • Management Reporting • Business Performance Management • Analytical Applications • Measurement and metrics

External Parties Data Files CMDM_PROCESS_PARM PROC_KEY PROC_NAME PROC_DESC CMDM_PHYQA_ERR_PARM CMDM_AUDIT PHYQA_ERR_KEY AUDIT_KEY INFA_SESS_KEY (FK) PROC_KEY (FK) TOLERANCE_TYPE_CODE CMDM_INFA_SESS_PARM PROC_STATUS_CODE NUM_RECS_TOLERANCE INFA_SESS_KEY PHYQA_STATUS_CODE PCTG_TOLERANCE Source/Legacy LOGQA_STATUS_CODE PROC_KEY (FK) PROC_START_DTS INFA_FOLDER_NAME PROC_END_DTS INFA_WORKFLOW_NAME INFA_SESS_NAME CMDM_INFA_SESS_EXP_PARM INFA_SESS_EXP_KEY INFA_SESS_KEY (FK) CMDM_INFA_SESS_STAT EXP_NAME EXP_DESC INFA_SESS_STAT_KEY Systems LKP_TABLE_NAME PHYQA_ERR_KEY (FK) SOURCE_NAME CMDM_ERR_LOG INFA_SESS_KEY (FK) TARGET_NAME ERR_LOG_KEY AUDIT_KEY (FK) AUDIT_STAT_DTS INFA_SESS_KEY (FK) NUM_RECS_SUCCESS INFA_SESS_EXP_KEY (FK) NUM_RECS_FAILED AUDIT_KEY (FK) CMDM_LOGQA_ERR_PARM NUM_RECS_TOTAL ERR_TYPE_CODE PCTG_FAILED_RECS LOGQA_ERR_KEY ERR_DESC SESS_STATUS_CODE LOGQA_INPUT_VALUE INFA_SESS_EXP_KEY (FK) SESS_START_DTS OPER_RESOLUTION TOLERANCE_TYPE_CODE SESS_END_DTS OPER_COMMENTS NUM_RECS_TOLERANCE ERR_STATUS_CODE PCTG_TOLERANCE ERR_CALLING_MODULE Audit Tables Support CMDM_PROCESS_PARM PROC_KEY PROC_NAME PROC_DESC CMDM_PHYQA_ERR_PARM CMDM_AUDIT PHYQA_ERR_KEY AUDIT_KEY Personnel INFA_SESS_KEY (FK) PROC_KEY (FK) TOLERANCE_TYPE_CODE CMDM_INFA_SESS_PARM PROC_STATUS_CODE NUM_RECS_TOLERANCE INFA_SESS_KEY PHYQA_STATUS_CODE PCTG_TOLERANCE LOGQA_STATUS_CODE PROC_KEY (FK) PROC_START_DTS INFA_FOLDER_NAME PROC_END_DTS INFA_WORKFLOW_NAME Conform EDW Data Marts Unix Directories Stage (TMP & PS) INFA_SESS_NAME CMDM_INFA_SESS_EXP_PARM INFA_SESS_EXP_KEY INFA_SESS_KEY (FK) CMDM_INFA_SESS_STAT EXP_NAME INFA_SESS_STAT_KEY EXP_DESC LKP_TABLE_NAME PHYQA_ERR_KEY (FK) SOURCE_NAME CMDM_ERR_LOG INFA_SESS_KEY (FK) TARGET_NAME ERR_LOG_KEY AUDIT_KEY (FK) AUDIT_STAT_DTS INFA_SESS_KEY (FK) NUM_RECS_SUCCESS INFA_SESS_EXP_KEY (FK) NUM_RECS_FAILED AUDIT_KEY (FK) CMDM_LOGQA_ERR_PARM NUM_RECS_TOTAL ERR_TYPE_CODE PCTG_FAILED_RECS LOGQA_ERR_KEY ERR_DESC SESS_STATUS_CODE LOGQA_INPUT_VALUE INFA_SESS_EXP_KEY (FK) SESS_START_DTS OPER_RESOLUTION TOLERANCE_TYPE_CODE SESS_END_DTS OPER_COMMENTS NUM_RECS_TOLERANCE ERR_STATUS_CODE PCTG_TOLERANCE ERR_CALLING_MODULE Meta Data Repository Initiate Data Load Validate Data Information Metadata Access Types ETL Architecture Metadata • It has been estimated that ETL and data integration make up anywhere from 50% to 80% of all data warehouse efforts • ETL technology from leading vendors like DataStage (IBM) and Informatica continue to make their applications easier to use, more scalable, more interoperable, and able to capture better meta-data • However, for the most part, many users find that these tools have not had any revolutionary new functionality in the last couple releases ETL Architecture Data Reconciliation Processing / Data Watching Information Consumers Communicate Issues

Dynamic Data Warehousing/MDM • None of the hard parts of ETL go away • But some of these technologies bufferthe end result (data mart) from changes in sources, business rules, or business models • Some generate BI meta data models Inventory CustomerService Web SFA Order Entry ETL Engine Staging Active Meta Data MarketingPerformance SalesAnalysis CustomerData ProductData

Metadata Process External Metadata ETL Architecture Parties Data Files CMDM_PROCESS_PARM PROC_KEY PROC_NAME Source/Legacy PROC_DESC CMDM_PHYQA_ERR_PARM CMDM_AUDIT PHYQA_ERR_KEY AUDIT_KEY INFA_SESS_KEY (FK) PROC_KEY (FK) TOLERANCE_TYPE_CODE CMDM_INFA_SESS_PARM PROC_STATUS_CODE NUM_RECS_TOLERANCE INFA_SESS_KEY PHYQA_STATUS_CODE PCTG_TOLERANCE LOGQA_STATUS_CODE PROC_KEY (FK) PROC_START_DTS INFA_FOLDER_NAME PROC_END_DTS INFA_WORKFLOW_NAME INFA_SESS_NAME CMDM_INFA_SESS_EXP_PARM INFA_SESS_EXP_KEY Systems INFA_SESS_KEY (FK) CMDM_INFA_SESS_STAT EXP_NAME EXP_DESC INFA_SESS_STAT_KEY LKP_TABLE_NAME PHYQA_ERR_KEY (FK) SOURCE_NAME CMDM_ERR_LOG INFA_SESS_KEY (FK) TARGET_NAME ERR_LOG_KEY AUDIT_KEY (FK) AUDIT_STAT_DTS INFA_SESS_KEY (FK) NUM_RECS_SUCCESS INFA_SESS_EXP_KEY (FK) NUM_RECS_FAILED AUDIT_KEY (FK) CMDM_LOGQA_ERR_PARM NUM_RECS_TOTAL ERR_TYPE_CODE PCTG_FAILED_RECS LOGQA_ERR_KEY ERR_DESC SESS_STATUS_CODE LOGQA_INPUT_VALUE INFA_SESS_EXP_KEY (FK) SESS_START_DTS OPER_RESOLUTION TOLERANCE_TYPE_CODE SESS_END_DTS OPER_COMMENTS NUM_RECS_TOLERANCE ERR_STATUS_CODE PCTG_TOLERANCE ERR_CALLING_MODULE Audit Tables Support CMDM_PROCESS_PARM PROC_KEY PROC_NAME PROC_DESC CMDM_PHYQA_ERR_PARM CMDM_AUDIT PHYQA_ERR_KEY AUDIT_KEY Personnel INFA_SESS_KEY (FK) PROC_KEY (FK) TOLERANCE_TYPE_CODE CMDM_INFA_SESS_PARM PROC_STATUS_CODE NUM_RECS_TOLERANCE INFA_SESS_KEY PHYQA_STATUS_CODE PCTG_TOLERANCE LOGQA_STATUS_CODE PROC_KEY (FK) PROC_START_DTS INFA_FOLDER_NAME PROC_END_DTS INFA_WORKFLOW_NAME Conform EDW Data Marts Unix Directories Stage (TMP & PS) INFA_SESS_NAME CMDM_INFA_SESS_EXP_PARM INFA_SESS_EXP_KEY INFA_SESS_KEY (FK) CMDM_INFA_SESS_STAT EXP_NAME INFA_SESS_STAT_KEY EXP_DESC LKP_TABLE_NAME PHYQA_ERR_KEY (FK) SOURCE_NAME CMDM_ERR_LOG INFA_SESS_KEY (FK) TARGET_NAME ERR_LOG_KEY AUDIT_KEY (FK) AUDIT_STAT_DTS INFA_SESS_KEY (FK) NUM_RECS_SUCCESS INFA_SESS_EXP_KEY (FK) NUM_RECS_FAILED AUDIT_KEY (FK) CMDM_LOGQA_ERR_PARM NUM_RECS_TOTAL ERR_TYPE_CODE PCTG_FAILED_RECS LOGQA_ERR_KEY ERR_DESC SESS_STATUS_CODE LOGQA_INPUT_VALUE INFA_SESS_EXP_KEY (FK) SESS_START_DTS OPER_RESOLUTION TOLERANCE_TYPE_CODE SESS_END_DTS OPER_COMMENTS NUM_RECS_TOLERANCE ERR_STATUS_CODE PCTG_TOLERANCE ERR_CALLING_MODULE Meta Data Repository Initiate Data Load Validate Data Information Metadata Decision Support Framework Access Types Ad-Hoc Standard Data Extracts Statistics Reporting Reporting Dashboards RATE PLAN HISTORY SUBSCRIPTION RATE PLAN HISTORY RATE PLAN CODE BILL ACCOUNT HISTORY RATE PLAN DESC SUB ID SITE CODE BILL ACCT ID BILL ACCT ID MKT NUM RATE PLAN CODE BILL FNAME SALES AREA CURRENT PRODUCT CODE SUBSCRIPTION RATE PLAN PRD CODE BILL MNAME - Bill Account PRODUCT CODE DESC RATE PLAN PRD GRP CODE BILL LNAME - Subscription PRODUCT CODE TYPE SUB ID RATE PLAN PRD SUB GRP CODE BILL IN CARE OF NAME SVC TYPE CODE BILL ACCT ID VALUESHARE FLAG ACCT FNAME SITE CODE PYMT TYPE CODE BILL DAY SVC TYPE CODE ACCT MNAME SITE NAME VARIANCE 1 SUB FNAME PYMT TYPE CODE ACCT LNAME SUB SITE CODE VARIANCE 2 SUB MNAME EFF DTS ACCT IN CARE OF NAME CONSOLIDATED CODE VARIANCE 3 CO NAME SUB LNAME END DTS SAC NAME VARIANCE 4 ESTABLISH DATE SUB IN CARE OF NAME TIER NUM ACCESS CHARGE COST CENTER SUB ADDR LINE1 HRIS MKT NUM ACCT LEVEL IND Delivery Methods BIRTH DATE SUB ADDR LINE2 SUBSCRIPTION STATUS HISTORY JDE COMPANY NAME COMMISSION FLAG SOCIAL SECURITY NUM SUB ADDR LINE3 JDE WLS CO NUM PACKAGE MINS CREDIT BUREAU SCORE RISK JDE WLN CO NUM SUB CITY SUB ID BILL SYSTEM CODE DEPOSIT REQUIRED FLAG JDE LD CO NUM SUB ZIP BILL ACCT ID SRC SYSTEM CODE DEPOSIT REQUIRED AMT JDE PREPAY CO NUM SUB STATE EFF DTS DISTRIBUTION CHANNEL EFF DTS DEPOSIT TAKEN AMT JDE INT CO NUM SUB COUNTRY END DTS END DTS PICTURE ID JDE PAGE CO NUM GENDER SUB STATUS ID DIST CHAN CODE OFFPEAK MIN PICTURE ID STATE VPGM FNAME ESTABLISH DATE DIST CHAN CODE NAME PEAK MIN GEO CODE VPGM MNAME MIN DIST CHAN SUB GRP CODE ANYTIME MIN LAST HOTLINED DATE VPGM LNAME MDN DIST CHAN SUB GRP NAME M2M MIN LAST NPD DATE VPGM EMPL POSTN NUM ESN DIST CHAN GRP CODE CALL DELIVERY RATE CPNI MAP FNAME WORK PHONE DIST CHAN GRP NAME POST PKG PEAK RATE CREDIT REFERENCE NUM MAP MNAME HOME PHONE CANCELLATION DATE POST PKG OFFPEAK RATE DECISION CODE MAP LNAME VOL DISC FLAG POST PKG M2M RATE NEXT BILL DATE MAP EMPL POSTN NUM IN CONTRACT FLAG CALENDAR REV ACCT CODE MAJOR ACCT FLAG RP FNAME CDPD NEI ROAMING MINS MAJOR ACCT CODE RP MNAME SALES REP HISTORY BILL STATUS CODE CALENDAR DATE MAJOR ACCT ID RP LNAME - Sub Handset MAKE MODEL ID DAY OF WEEK NAME ACCT STATUS ID RP EMPL POSTN NUM - Subscription RATE PLAN CODE DAY OF WEEK SHORT NAME ACCT TYPE ID COO FNAME MAKE MODEL RATE PLAN PRD CODE DAY IN WEEK CREDIT CLASS ID MKT CODE SLS REP ID RATE PLAN PRD GRP CODE DAY IN MONTH CREDIT CODE ID COO MNAME MAKE MODEL ID EMPL DISPLAY NAME RATE PLAN PRD SUB GRP CODE DAY IN QUARTER CREDIT CODE DESC MKT NUM MAKE HRIS MKT NUM CREDIT CLASS ID DAY IN YEAR BILL DAY COO LNAME MODEL BILL SYSTEM CODE CREDIT CODE ID WEEK IN MONTH BILL CYCLE ID MKT NAME MANUFACTURER REP DCID CREDIT CODE DESC WEEK IN QUARTER BILL SYSTEM CODE COO EMPL POSTN NUM LIFE CYCLE CODE EFF DTS TYPIST ID WEEK IN YEAR BILL HOLD CEO FNAME TECHNOLOGY TYPE CODE END DTS SLS REP ID MONTH BILL CT RDM MKT CODE COMPARABLE MAKE DIST CHAN CODE MONTH NAME BILL IN ARREARS FLAG MKT AREA CODE COMPARABLE MODEL SUB STATUS ID MONTH IN QUARTER TREAT CONTROL CEO MNAME PHONE WEIGHT CURR BAL DUE MKT NUM QUARTER SOFTWARE VER MKT AREA NAME AMT30 MKT AREA CODE YEAR WUW SOFTWARE VER CEO LNAME AMT60 REGION CODE SUBSCRIPTION HANDSET HISTORY HOLIDAY FLAG DIG STANDBY TIME REGION CODE AMT90 SITE CODE WEEKEND FLAG DIG TALK TIME CEO EMPL POSTN NUM AMT120 SUB SITE CODE SUB ID LAST DAY IN MONTH FLAG BATTERY TYPE CODE REGION NAME AMT150 SVC TYPE CODE BILL ACCT ID SALES DAY OF WEEK VIB ALERT FLAG SRC SYSTEM CODE AMT180 BILL SYSTEM CODE PYMT TYPE CODE MAKE SALES WEEK RING TONE FLAG MODEL EFF DTS SUB TYPE ID MID MTH COMMISSION MTH RING TONE DOWNLOAD FLAG HS ID END DTS BILL SYSTEM CODE MID MTH COMMISSION QTR DATA FAX FLAG HS SLS DATE BLUETOOTH FLAG ESN VOICE DIAL FLAG MAKE MODEL ID SPEAKER PHONE FLAG BILL ACCOUNT ADDRESS HS CHG REASON CODE WUW FLAG ITEM NUM TTY FLAG BILL ACCT ID ITEM QTY GPS FLAG BILL ADDR LINE1 UNIT COST AXCESS SHOP FLAG BILL ADDR LINE2 RETAIL AMT ONE TOUCH DIAL FLAG BILL ADDR LINE3 SUBSIDY AMT MEMORY LOC CT BILL CITY ORDER ID CALLER ID FLAG BILL ZIP COAM CPE FLAG NAME DISPLAY FLAG BILL STATE SUBSCRIPTION CONTRACT HISTORY SHIP FEE AMT PRL FLAG BILL COUNTRY SHIP COST AMT ACCT ADDR LINE1 SUB ID FULLFILLMT COST ACCT ADDR LINE2 CONTRACT START DATE SLS REP ID ACCT ADDR LINE3 HANDSET BILL ACCT ID RETURN FLAG TYPIST CREDIT CLASS ACCT CITY CONTRACT END DATE SRC SYSTEM CODE - Sub Handset ACCT ZIP HS ID CONTRACT LENGTH BILL SYSTEM CODE - Subscription BILL SYSTEM CODE ACCT STATE ITEM DESC CONTRACT TYPE EFF DTS CREDIT CLASS ID ACCT COUNTRY RETAIL PRICE EARLY DISC PENALTY END DTS TYPIST ID CREDIT CLASS DESC HOME PHONE MAKE EFF DTS EMPL DISPLAY NAME WORK PHONE MODEL END DTS BILL SYSTEM CODE Data Model Web Client App Email FTP Business Intelligence Architecture Data Reconciliation Processing / Data Watching Information Consumers Communicate Issues Data Dictionary

RM Home Page Brett Hively – BRM Agent Desktop AE Home Page Brett Hively’s Home Page Funded Volume Funded Units Broker Penetration Weighted Deviation Funding Ratio Share of Wallet Cost per 1st Lien Units Loans per AE FastQual % Brokers Funded in 90 Days Loan Performance Internal Performance Key Metrics Yesterday $119,830,703 527 14.5% .53 66.8% 34% $2,680 17.4 33.3% 178 Same Day Last Month $120,543,987 400 15% .60 60.4% 30% $2,589 16.5 27% 165 Quarter to Date 178 $119,830,703 527 14.5% .53 66.8% 34% $2,680 17.4 33.3% RM Territory Summary Total Leads 185 110 Internal Performance Loan Count Average Days To Next Stage Internal Performance Loan Count Status Assigned Unassigned Approved 100 60 Pending 50 30 Potential FPD’s 8 Underwriting 40 2 30 Day 1 Account Manager 250 8 Qualified 25 15 60 Day 1 Doc’s Out 157 4 Validated 10 5 90 Day 0 Doc’s Out 140 5 REO’s 1 Total Days Sub to Close 13.5 Investor Rejects 2 RM Alert Summary RM Lead Summary Total Alerts 185 110 Broker Swap Candidates Status New Attention Period Funded Never Funded 25% Submission Drop 100 60 60 Days 100 60 40 % Below Funding Ratio 50 30 90 Days 50 30 New Loan Officers 25 15 120 Days 25 15 Docs in Past 3 Days 10 5 180 Days 10 5 Terminated 5 - Home Page My Lounge My Rankings Search Windows Help Logout RM Scorecard Detail Scorecard Summary

Lapse Likelihood by Sales Deciles Lapse Likelihood Index by Sales Decile (100 = Average Likelihood) BEST CUSTOMERS ARE LESS LIKELY TO BE LAPSED CUSTOMERS. HOWEVER, SIGNIFICANT PROPORTIONS OF BEST CUSTOMERS ARE STILL LAPSED. DATA INFORMATION INSIGHT STRATEGY ACTION

Seamless Transition From… • Strategy • Tactical Plans • Dashboards • Management Reporting • Ad-hoc Reporting • Multi-dimensional Analysis • Statistical Analysis

Real-Time Coordination • Some architectures have chosen a distributed methodology • Others have chosen ODS • Others have chosen hybrids Inventory CustomerService Web SFA Order Entry DistributionArchitecture OperationalData Store ETL DecisionSupport

Operational Business Intelligence Continuous visibility • For critical, intra-day measurements • Aggregated across multiple systems Flexible user interface • Self-service model • User-defined thresholds and alerts • Graphical watch points Role-based delivery • Role based data level security • Customization by end users Collaboration • Built-in workflow/collaboration • Complete loop from discovery to action

Brett Hively – BRM Agent Desktop Brett Hively’s Broker List Assigned Jr. AE Assignee 60 Days Broker List Select All Disp- ute. Broker Name Broker Stage Code Assigned AE # of Loan Officers # of Funded Units Broker Temp Last Assign Date Assign Broker Broker ABC At Risk Frank Morley 10 24 Un Happy 1/2/06 Broker DEF At Risk Jim Smith 5 3 Un Happy 2/8/06 Broker GHI At Risk Brian Slucki 25 39 Un Happy 1/12/06 Broker JKL At Risk Pete Berchich 30 22 Un Happy 2/11/06 Broker MNO At Risk Mike Masters 20 12 Un Happy 2/3/06 Broker PQR At Risk Todd Walsh 15 33 Un Happy 1/31/06 Broker Assignment Jr. AE Assignment Broker STU At Risk Joe Widlowski 20 4 Un Happy 2/8/06 Broker VWX At Risk Dustin Lagar 50 6 Un Happy 2/4/06 Tim Smith Frank Morley Broker YZ At Risk Bob Purchase 10 14 Un Happy 1/11/06 Broker ABC At Risk Frank Morley 10 24 Un Happy 1/2/06 Broker DEF At Risk Jim Smith 5 3 Un Happy 2/8/06 Broker GHI At Risk Brian Slucki 25 39 Un Happy 1/12/06 Broker JKL At Risk Pete Berchich 30 22 Un Happy 2/11/06 Broker MNO At Risk Mike Masters 20 12 Un Happy 2/3/06 Broker PQR At Risk Todd Walsh 15 33 Un Happy 1/31/06 Broker STU At Risk Joe Widlowski 20 4 Un Happy 2/8/06 Broker VWX At Risk Dustin Lagar 50 6 Un Happy 2/4/06 Broker YZ At Risk Bob Purchase 10 14 Un Happy 1/11/06 Broker STU At Risk Joe Widlowski 20 4 Un Happy 2/8/06 Broker VWX At Risk Dustin Lagar 50 6 Un Happy 2/4/06 Broker YZ At Risk Bob Purchase 10 14 Un Happy 1/11/06 Broker YZ At Risk Bob Purchase 10 14 Un Happy 1/11/06 Assign Broker Home Page My Lounge My Rankings Search Windows Help Logout All Funded Never Funded Swap Candidate • Dispute • Undispute • Assign • AE Assignment History • Broker Scorecard • AE Scorecard • Broker Relationships Broker Name City State Assigned AE # of Loan Officers # of Funded Units Broker Satisfaction Reason Last Assign Date Broker ABC Irvine CA Frank Morley 10 24 Un Happy 1/2/06 Assignment History Assigned AE Last Assign Date Assigned Jr. AE • Assign • Broker Scorecard • AE Scorecard • Broker Relationships • Cancel Frank Morley 1/2/06 Jim Smith 2/8/06 Brian Slucki 1/12/06 Dennis Ugolini Pete Berchich 2/11/06 Mike Masters 2/3/06 Eddie Olson

AE Ranking Brett Hively – BRM Agent Desktop Conversion Ratio Frank Morley’s Frank Melella Steve Tangenhorse Heather Thompson Robin Budz Mark Liskewicz Terra Arthur Tony Bafano Scott Soga 13 5 15 3 9 11 7 1 19 9 11 21 13 17 15 23 1 9 11 13 7 5 3 15 17 19 21 15 23 13 11 9 Rick Harris Frank Morley Joanna Basil Ron Chester Kristi Volger Brian Pavur Anthony Astrowski 12 10 14 2 8 4 6 18 14 10 22 16 20 12 12 4 10 8 2 14 6 22 20 14 12 18 10 16 Home Page My Lounge My Rankings Search Windows Help Logout Rankings Frank Morley’s Ranking for Funded Units as of April 4th, 2008 Current Ranking Current Metric Value Ranking Same Day Last Month Same Day Last Month Metric Value

What Do We Want? • Save us time? • Save us money? • Easier maintenance? • Faster development? • Better integration between ETL and Business Intelligence? • Easier impact analysis across the ecosystem? • Sharing business rules between real-time and ETL data integration? • Ability to seamlessly work between BI technologies? • Ability to embed BI in our CRM/ERP applications?

Business Objects,OutlookSoft,Pilot Software,Callixa, A2i SAP Acquisitions Business Objects Crystal, Infommersion, Cartesis, Nsite,SRC, OLAP@work, Acta, Medience, FirstLogic,Inxight, Informatik Indicates duplicate product sets due to acquisition

Siebel,Hyperion,IRI,Thinking Machines,Sunopsis, One Meaning, TimesTen, Stellent,InnoDB, PeopleSoft Oracle Acquisitions Hyperion Siebel Upstream, Brio, Razza,Decisioneering Nquire Indicates duplicate product sets due to acquisition

ProClarity, Stratature Microsoft Acquisitions

Cognos, Informix,Ascential,AlphaBlox,DWL, FileNet, DataMirror, Venetica, Trigo,Green Pasture, SRD IBM Acquisitions Cognos Celequest,Applix,Adaytum, DecisionStream, Frango Indicates duplicate product sets due to acquisition

Aprimo Marketing Automation Unica DoubleClick Marketing Central, MarketSoft, Sane Solutions Indicates duplicate product sets due to acquisition

Lyris Marketing Automation (cont’d) Lyris, EmailLabs, ClickTrack, Hot Banana

Did It Make Sense? - BI

Did It Make Sense? – Marketing

Conclusions • Most of the acquisitions over the past few years have propelled the “aquirers” into marketplaces where they were lacking or had no previous capabilities • Some of these companies have moved into fairly new areas • IBM in user facing tools • SAP as an independent tool vendor • Though this helps the software company, it has rarely had, or is having, a positive impact on the user base • The most immediate impact would be one-stop shopping and pricing • Except for consolidating the number of vendors you need to call, no real new capability has come (or has even been announced) from these organizations • Examples of successful BI acquisitions: • Business Objects and Acta: Provides a full data warehouse suite for some BO clients • Oracle and IRI: Provides OLAP and relational database from one company • Unica and Marketsoft: Provides the bridge between sales and marketing • Siebel and Paragren and nQuire: Provides the most powerful CRM suite, analytical and marketing capabilities

What May Be Next • Hopefully, we will see some innovation down the road: • True operational BI from Oracle by combining Oracle ERP, Siebel CRM, and Siebel Analytics (and possibly Thinking Machines). • True operational BI from Microsoft by combining Business Intelligence Services and Microsoft Dynamics. The key for Microsoft will be to make analytics as simple as Microsoft Word for the masses: Guided Analytics, pre-canned solutions, and vertical integration. • Pre-packaged, right-time BI engine from IBM by combining Websphere, Ascential, and Cognos.

Thank you. Larry Goldman, President 773.456.3996 larry@amberleaf.net

Social Networking and Customer Feedback – It’s all about the Data Presented by: Larry Goldman DAMA–MN April 16, 2008

The New Data Hunger Players New Structures Example Agenda DATA INFORMATION INSIGHT STRATEGY ACTION

360 Degree of the Customer - Example Organizations (UCM/MDR) Geography (Excel) Web Activity(Web Trends) Responses (CISPUB/PICK/FRS/ARGI/SJO/ATG, MPS, Unica,SPIN) Books (UPM/SPIN) Customer Adoptions (ATG,CISPUB/PICK/FRS) Journals (UPM) Alerts (ATG, SJO) Orders/Quotes/Usage (CISPUB/PICK/ARGI/MS CRM,MPS) Course (MDR/FRS/PICK/ATG)

Internal Web Tracking Software like Omniture, Unica NetInsight, and WebTrends provide metrics about your site: Site Usage and Optimization Standard metrics: page views, visits, and unique visitors Marketing Conversion Referral sites Repeat visitor analysis • Traditional software does not tell you: • Are your metrics good compared to your competitors? • Where are your customers when they are not on your site? • What are clients saying about you when they are not filling out your surveys? • What are your customers interested in beyond your products and services? • What are the major changes in the behavior of your industry’s on-line customers? • What are the major changes in the perspective of your customer base regarding your brand, new products, or service?

DW’s Start Chasing The Dream Again Look-a-likeProspects Business to Business Contacts Non-verticalWeb Behavior Reviewed Products Competitive Web Behavior Customer Product/CompanyRatings CredibleReviewers Cross Brand Analysis Brand Attributes Net Promoter

The New Data Hunger Players New Structures and Challenges Example Agenda DATA INFORMATION INSIGHT STRATEGY ACTION

Competitive Web Metrics

Vendors Nielsen: This advertising rating company collects information on the Web mostly focused on Media buyers to help organizations understand reach of different sites and where to place their media. Query tools are not great and custom reports are expensive. Nielsen contains B-to-B data as well as home PC user data. Comscore: Comscore offers almost the exact same services as Nielsen. The focus is on media buying and reach rankings. Comscore contains international and domestic panels. Hitwise: Hitwise tries to commoditize this marketplace by offering a large amount of canned reports across many different industries. Brittle and not much customization outside of the canned reports. Compete: Boasts the largest panel of all the vendors and provides deep analytics in a subset of verticals across national ISP’s. Compete has in-depth analysis on Search data as well as provides free general metrics on their web site.

Web 2.0 Metrics

Vendors Umbria: Does not provide direct access to the data, but provides deep dive analysis on a per project basis. Chatter Guard: Focuses on the travel industry by reading all blog and feedback sites and manually scoring, rating, and categorizing the different items. Clarabridge: Less a service than an ETL tool for unstructured information. Combines sentiment and language parser in order to provide key performance indicators on social networking sites.

New Data Sources/ETL New outside vendor forcompetitive web analysis Some vendors will send extracts and others will only allow access New technology for interpretingunstructured Internet data Blogs,Community CompetitiveWeb OutsourcedSystems InternalSystems Third PartyDemographics Standard ETL Scraping,LanguageParsing EDW Business intelligence

New Structures New ETL challenges arise on how to match up the various customer level information Internal data is at a very low level External data will be aggregated Screen names and e-mails may be available to consolidate in some cases Data Modeling Efforts Competitor. Need to normalize against your organization structure Web behavior. Need to normalize against your site sections or taxonomy Competitive Products. Need to normalize against your product hierarchy New Customer Transactions. Ratings, reviews, pageviews, visits

Data Quality Concerns New subject areas Competitive organizations Competitive products Competitive sites Competitive behavior Social Networking transactions

Available Insight Hotel City Name Chain (where applicable) Owner (where applicable) Author Author Channel Score – how many postings and author makes Credibility – other on-line members’ ratings on how helpful the author’s comments are Velocity – how often an author posts Influence – identifies if the author is leading or following the discussion Reviews Author Hotel Date Overall Rating for the hotel Would the author recommend the hotel? Rating for how good of a value the hotel was

Available Insight (Cont’d) Hotel Metrics Awareness – How much is being discussed Favorability – Score that is a proxy for loyalty and how often the Hotel would be recommended by authors Satisfaction - Based on overall ratings from reviews Value – Based on value ratings from review Net Promoter – based on promoters and detractors from the last year Metrics can be sliced for influencers only or the entire Author base Key Words Score of how influential the key word is for that hotel Metrics can be sliced for influencers only or the entire Author base

Creating Competitive Categories DATA INFORMATION INSIGHT STRATEGY ACTION

Pageviews by Competitor DATA INFORMATION INSIGHT STRATEGY ACTION

Looking At Browsing Demographics DATA INFORMATION INSIGHT STRATEGY ACTION

Customer Browsing Loyalty DATA INFORMATION INSIGHT STRATEGY ACTION

Key Social Networking Metrics

Software Consolidation and Its Impact on Data Management