software consolidation and its impact on data management

DATA INFORMATION INSIGHT STRATEGY ACTION

AmberLeaf specializes in quickly breaking down the barriers that stand between your data and action.

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 Warehouse/Data Mart strategy, design and development

•Data Integration (ETL on Demand)

•Data Quality•Customer

Information Hygiene

Information

•Reporting Architectures

•Management Reporting

•Business Performance Management

•Analytical Applications

•Measurement and metrics

Insight

•Loyalty Programs•Advanced Analytics•Customer Valuation•Segmentation•Marketing

Optimization•Customer

Experience Strategy• Sales• Service• Marketing

Strategy

•Customer Experience Optimization

•Marketing Automation

•Personalization•Process Design•Change Agents•Service

Optimization•Sales Effectiveness

Action

Urgency, Speed, Focus, Expertise, ResultsUrgency, Speed, Focus, Expertise, ResultsUrgency, Speed, Focus, Expertise, ResultsUrgency, Speed, Focus, Expertise, Results

Breaking Down the Barriers







Agenda


ETL ArchitectureMetadata

ETL Architecture

Access Types

InformationMetadata

Data Reconciliation Processing / Data Watching

Communicate IssuesValidate DataInitiate Data Load

DataFiles

Data MartsStage (TMP & PS) Conform EDWUnix Directories

ExternalParties

Source/LegacySystems

SupportPersonnel

InformationConsumers

CMDM_AUDITAUDIT_KEY

PROC_KEY (FK)PROC_STATUS_CODEPHYQA_STATUS_CODELOGQA_STATUS_CODEPROC_START_DTSPROC_END_DTS

CMDM_PROCESS_PARMPROC_KEYPROC_NAMEPROC_DESCCMDM_PHYQA_ERR_PARM

PHYQA_ERR_KEYINFA_SESS_KEY (FK)TOLERANCE_TYPE_CODENUM_RECS_TOLERANCEPCTG_TOLERANCE

CMDM_LOGQA_ERR_PARMLOGQA_ERR_KEYINFA_SESS_EXP_KEY (FK)TOLERANCE_TYPE_CODENUM_RECS_TOLERANCEPCTG_TOLERANCE

CMDM_ERR_LOGERR_LOG_KEYINFA_SESS_KEY (FK)INFA_SESS_EXP_KEY (FK)AUDIT_KEY (FK)ERR_TYPE_CODEERR_DESCLOGQA_INPUT_VALUEOPER_RESOLUTIONOPER_COMMENTSERR_STATUS_CODEERR_CALLING_MODULE

CMDM_INFA_SESS_STATINFA_SESS_STAT_KEYPHYQA_ERR_KEY (FK)INFA_SESS_KEY (FK)AUDIT_KEY (FK)AUDIT_STAT_DTSNUM_RECS_SUCCESSNUM_RECS_FAILEDNUM_RECS_TOTALPCTG_FAILED_RECSSESS_STATUS_CODESESS_START_DTSSESS_END_DTS

CMDM_INFA_SESS_PARMINFA_SESS_KEYPROC_KEY (FK)INFA_FOLDER_NAMEINFA_WORKFLOW_NAMEINFA_SESS_NAME

CMDM_INFA_SESS_EXP_PARMINFA_SESS_EXP_KEY

INFA_SESS_KEY (FK)EXP_NAMEEXP_DESCLKP_TABLE_NAMESOURCE_NAMETARGET_NAME

Audit Tables

CMDM_AUDITAUDIT_KEYPROC_KEY (FK)PROC_STATUS_CODEPHYQA_STATUS_CODELOGQA_STATUS_CODEPROC_START_DTSPROC_END_DTS






CMDM_INFA_SESS_PARMINFA_SESS_KEY

PROC_KEY (FK)INFA_FOLDER_NAMEINFA_WORKFLOW_NAMEINFA_SESS_NAME



Meta DataRepository

• 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


Dynamic Data Warehousing/MDM

SFASFA Order Entry

Order Entry

StagingStaging

CustomerService

CustomerServiceWebWeb InventoryInventory

ETL EngineETL Engine

CustomerData

CustomerData

ProductData

ProductData

MarketingPerformance

MarketingPerformance

SalesAnalysis

SalesAnalysis

Active Meta DataActive Meta Data

• 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


Business Intelligence Architecture

Decision Support Framework

Metadata

ETL Architecture

Delivery Methods

FTP Client App

Access Types

InformationMetadata

ProcessMetadata

Data Extracts

Data Reconciliation Processing / Data Watching

Communicate IssuesValidate DataInitiate Data Load

DataFiles

Data MartsStage (TMP & PS) Conform EDWUnix Directories

ExternalParties

Source/LegacySystems

SupportPersonnel

InformationConsumers

Web Email

CMDM_AUDITAUDIT_KEY

PROC_KEY (FK)PROC_STATUS_CODEPHYQA_STATUS_CODELOGQA_STATUS_CODEPROC_START_DTSPROC_END_DTS






CMDM_INFA_SESS_PARMINFA_SESS_KEYPROC_KEY (FK)INFA_FOLDER_NAMEINFA_WORKFLOW_NAMEINFA_SESS_NAME



Audit Tables

CMDM_AUDITAUDIT_KEYPROC_KEY (FK)PROC_STATUS_CODEPHYQA_STATUS_CODELOGQA_STATUS_CODEPROC_START_DTSPROC_END_DTS






CMDM_INFA_SESS_PARMINFA_SESS_KEY

PROC_KEY (FK)INFA_FOLDER_NAMEINFA_WORKFLOW_NAMEINFA_SESS_NAME



Meta DataRepository

SUBSCRIPTION STATUS HISTORYSUB IDBILL ACCT IDEFF DTSEND DTSSUB STATUS ID

SUBSCRIPTIONSUB IDBILL ACCT IDBILL DAYSUB FNAMESUB MNAMESUB LNAMESUB IN CARE OF NAMESUB ADDR LINE1SUB ADDR LINE2SUB ADDR LINE3SUB CITYSUB ZIPSUB STATESUB COUNTRYGENDERESTABLISH DATEMINMDNESNWORK PHONEHOME PHONECANCELLATION DATEVOL DISC FLAGIN CONTRACT FLAGCDPD NEIBILL STATUS CODEMAKE MODEL IDRATE PLAN CODERATE PLAN PRD CODERATE PLAN PRD GRP CODERATE PLAN PRD SUB GRP CODECREDIT CLASS IDCREDIT CODE IDCREDIT CODE DESCTYPIST IDSLS REP IDDIST CHAN CODESUB STATUS IDMKT NUMMKT AREA CODEREGION CODESITE CODESUB SITE CODESVC TYPE CODEPYMT TYPE CODESUB TYPE IDBILL SYSTEM CODE

SUBSCRIPTION RATE PLAN HISTORYSUB IDBILL ACCT IDRATE PLAN CODERATE PLAN PRD CODERATE PLAN PRD GRP CODERATE PLAN PRD SUB GRP CODEVALUESHARE FLAGSVC TYPE CODEPYMT TYPE CODEEFF DTSEND DTS

SUBSCRIPTION HANDSET HISTORYSUB IDBILL ACCT IDMAKEMODELHS IDHS SLS DATEESNMAKE MODEL IDHS CHG REASON CODEITEM NUMITEM QTYUNIT COSTRETAIL AMTSUBSIDY AMTORDER IDCOAM CPE FLAGSHIP FEE AMTSHIP COST AMTFULLFILLMT COSTSLS REP IDRETURN FLAGSRC SYSTEM CODEBILL SYSTEM CODEEFF DTSEND DTS

DISTRIBUTION CHANNELDIST CHAN CODEDIST CHAN CODE NAMEDIST CHAN SUB GRP CODEDIST CHAN SUB GRP NAMEDIST CHAN GRP CODEDIST CHAN GRP NAME

TYPIST- Sub Handset- SubscriptionTYPIST IDEMPL DISPLAY NAMEBILL SYSTEM CODE

SALES REP HISTORY- Sub Handset- SubscriptionSLS REP IDEMPL DISPLAY NAMEHRIS MKT NUMBILL SYSTEM CODEREP DCIDEFF DTSEND DTS

RATE PLAN HISTORYRATE PLAN CODERATE PLAN DESCSITE CODEMKT NUMPRODUCT CODEPRODUCT CODE DESCPRODUCT CODE TYPESVC TYPE CODEPYMT TYPE CODEVARIANCE 1VARIANCE 2VARIANCE 3VARIANCE 4ACCESS CHARGEACCT LEVEL INDCOMMISSION FLAGPACKAGE MINSBILL SYSTEM CODESRC SYSTEM CODEEFF DTSEND DTSOFFPEAK MINPEAK MINANYTIME MINM2M MINCALL DELIVERY RATEPOST PKG PEAK RATEPOST PKG OFFPEAK RATEPOST PKG M2M RATEREV ACCT CODEROAMING MINS

SUBSCRIPTION CONTRACT HISTORYSUB IDCONTRACT START DATEBILL ACCT IDCONTRACT END DATECONTRACT LENGTHCONTRACT TYPEEARLY DISC PENALTYEFF DTSEND DTS

SALES AREA CURRENT- Bill Account- SubscriptionSITE CODESITE NAMESUB SITE CODECONSOLIDATED CODESAC NAMETIER NUMHRIS MKT NUMJDE COMPANY NAMEJDE WLS CO NUMJDE WLN CO NUMJDE LD CO NUMJDE PREPAY CO NUMJDE INT CO NUMJDE PAGE CO NUMVPGM FNAMEVPGM MNAMEVPGM LNAMEVPGM EMPL POSTN NUMMAP FNAMEMAP MNAMEMAP LNAMEMAP EMPL POSTN NUMRP FNAMERP MNAMERP LNAMERP EMPL POSTN NUMCOO FNAMEMKT CODECOO MNAMEMKT NUMCOO LNAMEMKT NAMECOO EMPL POSTN NUMCEO FNAMERDM MKT CODEMKT AREA CODECEO MNAMEMKT AREA NAMECEO LNAMEREGION CODECEO EMPL POSTN NUMREGION NAMESRC SYSTEM CODEBILL SYSTEM CODE

MAKE MODELMAKE MODEL IDMAKEMODELMANUFACTURERLIFE CYCLE CODETECHNOLOGY TYPE CODECOMPARABLE MAKECOMPARABLE MODELPHONE WEIGHTSOFTWARE VERWUW SOFTWARE VERDIG STANDBY TIMEDIG TALK TIMEBATTERY TYPE CODEVIB ALERT FLAGRING TONE FLAGRING TONE DOWNLOAD FLAGDATA FAX FLAGBLUETOOTH FLAGVOICE DIAL FLAGSPEAKER PHONE FLAGWUW FLAGTTY FLAGGPS FLAGAXCESS SHOP FLAGONE TOUCH DIAL FLAGMEMORY LOC CTCALLER ID FLAGNAME DISPLAY FLAGPRL FLAG

HANDSETHS IDITEM DESCRETAIL PRICEMAKEMODEL

CALENDARCALENDAR DATEDAY OF WEEK NAMEDAY OF WEEK SHORT NAMEDAY IN WEEKDAY IN MONTHDAY IN QUARTERDAY IN YEARWEEK IN MONTHWEEK IN QUARTERWEEK IN YEARMONTHMONTH NAMEMONTH IN QUARTERQUARTERYEARHOLIDAY FLAGWEEKEND FLAGLAST DAY IN MONTH FLAGSALES DAY OF WEEKSALES WEEKMID MTH COMMISSION MTHMID MTH COMMISSION QTR

BILL ACCOUNT HISTORYBILL ACCT IDBILL FNAMEBILL MNAMEBILL LNAMEBILL IN CARE OF NAMEACCT FNAMEACCT MNAMEACCT LNAMEACCT IN CARE OF NAMECO NAMEESTABLISH DATECOST CENTERBIRTH DATESOCIAL SECURITY NUMCREDIT BUREAU SCORE RISKDEPOSIT REQUIRED FLAGDEPOSIT REQUIRED AMTDEPOSIT TAKEN AMTPICTURE IDPICTURE ID STATEGEO CODELAST HOTLINED DATELAST NPD DATECPNICREDIT REFERENCE NUMDECISION CODENEXT BILL DATEMAJOR ACCT FLAGMAJOR ACCT CODEMAJOR ACCT IDACCT STATUS IDACCT TYPE IDCREDIT CLASS IDCREDIT CODE IDCREDIT CODE DESCBILL DAYBILL CYCLE IDBILL SYSTEM CODEBILL HOLDBILL CTBILL IN ARREARS FLAGTREAT CONTROLCURR BAL DUEAMT30AMT60AMT90AMT120AMT150AMT180EFF DTSEND DTS

BILL ACCOUNT ADDRESSBILL ACCT IDBILL ADDR LINE1BILL ADDR LINE2BILL ADDR LINE3BILL CITYBILL ZIPBILL STATEBILL COUNTRYACCT ADDR LINE1ACCT ADDR LINE2ACCT ADDR LINE3ACCT CITYACCT ZIPACCT STATEACCT COUNTRYHOME PHONEWORK PHONE

CREDIT CLASSBILL SYSTEM CODECREDIT CLASS IDCREDIT CLASS DESC

Data Model

StandardReporting

Ad-HocReporting

Data Dictionary

DashboardsStatistics


RM Home Page

Brett Hively’s Home Page

Brett Hively – BRM Agent DesktopMy Lounge Windows Help LogoutSearchHome Page My Rankings

RM Territory Summary

PeriodPeriod FundedFunded Never FundedNever Funded

60 Days

90 Days

120 Days

180 Days

100 60

50 30

25 15

10 5

Broker Swap CandidatesBroker Swap Candidates

RM Alert Summary

StatusStatus NewNew AttentionAttention25% Submission Drop

40 % Below Funding Ratio

New Loan Officers

Docs in Past 3 Days

100 60

50 30

25 15

10 5

185185 110110Total AlertsTotal Alerts

RM Lead Summary

StatusStatus AssignedAssigned UnassignedUnassigned

Approved

Pending

Qualified

Validated

100 60

50 30

25 15

10 5

185185 110110Total LeadsTotal Leads

Terminated 5 -

AE Home Page

Scorecard Summary RM Scorecard Detail

FundedVolume

FundedUnits

BrokerPenetration

WeightedDeviation

FundingRatio

Share ofWallet

Cost per1st Lien Units

Loans perAE

FastQual%

Yesterday

Same Day Last Month

$119,830,703

BrokersFunded in90 Days

$120,543,987

527

400

14.5%

15%

.53

.60

66.8%

60.4%

34%

30%

$2,680

$2,589

17.4

16.5

33.3%

27%

178

165

Quarter to Date $119,830,703 527 14.5% .53 66.8% 34% $2,680 17.4 33.3% 178

Key Metrics

Internal Performance

InternalPerformance

LoanCount

Average DaysTo Next Stage

Underwriting 40 2

Account Manager 250 8

Doc’s Out 157 4

Doc’s Out 140 5

Total Days Sub to Close 13.5

Loan Performance

Potential FPD’s 8

30 Day 1

60 Day 1

90 Day 0

REO’s 1

Investor Rejects 2

InternalPerformance

LoanCount


Lapse Likelihood by Sales Deciles

BEST CUSTOMERS ARE LESS LIKELY TO BE LAPSED CUSTOMERS. HOWEVER, SIGNIFICANT PROPORTIONS OF BEST CUSTOMERS ARE STILL LAPSED.

Lapse Likelihood Index by Sales Decile (100 = Average Likelihood)



Seamless Transition From…

• Strategy

• Tactical Plans

• Dashboards

• Management Reporting

• Ad-hoc Reporting

• Multi-dimensional Analysis

• Statistical Analysis


Real-Time Coordination

SFASFA Order Entry

Order Entry

OperationalData Store

OperationalData Store

CustomerService

CustomerServiceWebWeb InventoryInventory

DistributionArchitectureDistributionArchitecture

DecisionSupport

DecisionSupport

ETLETL

• Some architectures have chosen a distributed methodology

• Others have chosen ODS

• Others have chosen hybrids


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


Assign BrokerBrett Hively’s Broker List


• Dispute

• Undispute

• Assign

• AE Assignment History

• Broker Scorecard

• AE Scorecard

• Broker Relationships

All Funded Never Funded 60 DaysSwap Candidate

Broker List

Broker ABC

Broker DEF

Broker GHI

Broker JKL

Broker MNO

Broker PQR

At Risk

At Risk

At Risk

At Risk

At Risk

At Risk

10

5

25

30

20

15

Frank Morley

Jim Smith

Brian Slucki

Pete Berchich

Mike Masters

Todd Walsh

24

3

39

22

12

33

Un Happy

Un Happy

Un Happy

Un Happy

Un Happy

Un Happy

1/2/06

2/8/06

1/12/06

2/11/06

2/3/06

1/31/06

Broker NameBroker

Stage Code# of Loan Officers

Assigned AE# of Funded

UnitsBroker Temp

Last Assign Date

Select All

Disp-ute.

Broker STU

Broker VWX

Broker YZ

At Risk

At Risk

At Risk

20

50

10

Joe Widlowski

Dustin Lagar

Bob Purchase

4

6

14

Un Happy

Un Happy

Un Happy

2/8/06

2/4/06

1/11/06

Broker ABC

Broker DEF

Broker GHI

Broker JKL

Broker MNO

Broker PQR

At Risk

At Risk

At Risk

At Risk

At Risk

At Risk

10

5

25

30

20

15

Frank Morley

Jim Smith

Brian Slucki

Pete Berchich

Mike Masters

Todd Walsh

24

3

39

22

12

33

Un Happy

Un Happy

Un Happy

Un Happy

Un Happy

Un Happy

1/2/06

2/8/06

1/12/06

2/11/06

2/3/06

1/31/06

Broker STU

Broker VWX

Broker YZ

At Risk

At Risk

At Risk

20

50

10

Joe Widlowski

Dustin Lagar

Bob Purchase

4

6

14

Un Happy

Un Happy

Un Happy

2/8/06

2/4/06

1/11/06

Broker STU

Broker VWX

Broker YZ

At Risk

At Risk

At Risk

20

50

10

Joe Widlowski

Dustin Lagar

Bob Purchase

4

6

14

Un Happy

Un Happy

Un Happy

2/8/06

2/4/06

1/11/06

Broker YZ At Risk 10Bob Purchase 14 Un Happy 1/11/06

Assign Broker

Assignment History

Frank Morley

Jim Smith

Brian Slucki

Pete Berchich

Mike Masters

1/2/06

2/8/06

1/12/06

2/11/06

2/3/06

Assigned AELast Assign

Date

Broker ABC CA 10Frank Morley 24 Un Happy 1/2/06

Broker Name State# of Loan Officers

Assigned AE# of Funded

Units

Broker Satisfaction

Reason

Last Assign Date

• Assign

• Broker Scorecard

• AE Scorecard

• Broker Relationships

• Cancel

Assignee

Frank Morley

Broker Assignment

Assigned Jr. AE

Tim Smith

Jr. AE Assignment

Dennis Ugolini

Eddie Olson

Assigned Jr. AE

Irvine

City


AE Ranking


Current Ranking

Rankings

Current Metric ValueConversion Ratio

Frank Morley’s

Ranking Same Day

Last Month

Same Day Last Month Metric Value

Scott Soga 1 23 15 9

Brian Pavur 2 22 14 10

Tony Bafano 3 21 13 11

Rick Harris 4 20 12 12

Mark Liskewicz 5 19 11 13

Frank Morley 6 18 10 14

Frank Melella 7 17 9 15

Joanna Basil 8 16 8 16

Robin Budz 9 15 7 17

Kristi Volger 10 14 6 18

Steve Tangenhorse 11 13 5 19

Ron Chester 12 12 4 20

Heather Thompson 13 11 3 21

Anthony Astrowski 14 10 2 22

Frank Morley’s Ranking for Funded Units as of April 4th, 2008

Terra Arthur 15 9 1 23


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?







Agenda


SAP Acquisitions

Crystal, Infommersion, Cartesis, Nsite,SRC, OLAP@work, Acta, Medience, FirstLogic,

Inxight, Informatik

Business Objects

Business Objects, OutlookSoft, Pilot Software, Callixa, A2i

Indicates duplicate product sets due to acquisition


Oracle Acquisitions

Nquire

Siebel

Siebel, Hyperion, IRI, Thinking Machines,Sunopsis, One Meaning, TimesTen, Stellent,

InnoDB, PeopleSoft

Hyperion

Upstream, Brio, Razza,Decisioneering



Microsoft Acquisitions

ProClarity, Stratature


IBM Acquisitions

Celequest, Applix, Adaytum, DecisionStream, Frango

Cognos

Cognos, Informix, Ascential, AlphaBlox,DWL, FileNet, DataMirror, Venetica, Trigo,

Green Pasture, SRD



Marketing Automation

Marketing Central, MarketSoft, Sane Solutions

Unica

DoubleClick

Aprimo



Marketing Automation (cont’d)

Lyris, EmailLabs, ClickTrack, Hot Banana

Lyris







Agenda


Did It Make Sense? - BI

ETL MDM Analytical Applications

Reporting OLAP Stats Dashboard Operation-al BI

Oracle X X (major duplication)

X (major duplication)



X (not well

known)


SAP X(duplication)

X X X (duplication

)

X (duplication

)

X (duplication

)

X

Microsoft X X X X

IBM X X X X X X


Did It Make Sense? – Marketing

Campaign Marketing Resource Management

Statistics Bi-directional Reporting

Real-Time Offer/Event Detection

Lead Management

Web Analytics

Unica X X (duplication)

X X X X X

Aprimo X X X X X

Lyris X X


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, [email protected]


AmberLeaf specializes in quickly breaking down the barriers that stand between your data and action.

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


360 Degree of the Customer - Example

Books(UPM/SPIN)

Organizations (UCM/MDR)

Customer

Adoptions (ATG,CISPUB/PICK/FRS)

Orders/Quotes/Usage (CISPUB/

PICK/ARGI/MS CRM,MPS)

Responses(CISPUB/PICK/FRS/ARGI/SJO/

ATG, MPS, Unica,SPIN)

Journals (UPM)

Geography(Excel)

Web Activity(Web Trends)

Course (MDR/FRS/PICK/ATG)

Alerts (ATG, SJO)


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

CompetitiveWeb Behavior

Look-a-likeProspects

Customer

Product/CompanyRatings

Brand Attributes

Reviewed Products

CredibleReviewers

Business to Business Contacts

Non-verticalWeb Behavior

Net Promoter

Cross Brand Analysis



• Players

• New Structures and Challenges

• Example


Agenda


Competitive Web Metrics

Element Definition

Sources of Information ISP’s, Corporate licenses, proprietary toolbars/panels

Panel size Vendors range between 500K to over 3million users

Outliers Does the vendor correct for non-standard activity or coupon grabbing?

Algorithm to understand overall audience size Typically, random dialing and surveys to understand the general population of the internet

Projection Algorithm How does the vendor forecast actual web metrics based on their sample data?

Search How detailed does the vendor capture search referrals and results?

Auxiliary information Does the vendor supplement the click stream with attitudinal or demographic data?

Regional organizations Is the information national in perspective or does it have enough representation locally?

Vertical expertise Ability to compare functionality across different sites in the same vertical?


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

Element Definition

Sources of Information How many blogs, how many sites, how far back?

Scraping Technology How does the vendor scrape sites and can they extract individual fields, ratings, and recommendations? How does the technology react to site changes?

Language Parser What is the origin of the text parser? Does it come from the off-line world? How was it trained?

Sentiment Analysis Can the technology understand the difference between a positive and negative statement?

Categorization Can you categorize different key words together as a specific concept? Can you customize the categories?

Influencers Can you identify who is influencing the crowd as opposed to following the flock?

Access to Detail Do you have access to the detail and ad-hoc query?

Custom Roll Ups How does the analysis group roll up products and services?


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.



• Players


• Example


Agenda


New Data Sources/ETL

InternalSystemsInternalSystems

Third PartyDemographics

Third PartyDemographics

EDWEDW

CompetitiveWeb

CompetitiveWeb

OutsourcedSystems

OutsourcedSystems

Blogs,Community

Blogs,Community

Standard ETLStandard ETL

Business intelligenceBusiness

intelligence

• New outside vendor forcompetitive web analysis

• Some vendors will send extracts and others will only allow access

• New technology for interpretingunstructured Internet data

Scraping,LanguageParsing

Scraping,LanguageParsing


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

Program Management Group· Project Manager· Project Analyst

Program Management Office

Data Management Group· Data Warehouse Architect· Data Administrator

Steering Committee

Project Sponsor

Project

Source System

Data StewardResponsible for a Data

Category

Data Liaison

Tech Liaison

• New subject areas– Competitive organizations– Competitive products– Competitive sites– Competitive behavior– Social Networking transactions



• Players


• Example


Agenda


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

DATA INFORMATION INSIGHT STRATEGY ACTION DATA INFORMATION INSIGHT STRATEGY ACTION

Creating Competitive Categories


Pageviews by Competitor


Looking At Browsing Demographics


Customer Browsing Loyalty


Key Social Networking Metrics


Master Data Meta Data


Cross Master Data Meta Data


Competitive Benchmarking


Text Parsing and Mining


Thank you.

Larry Goldman, [email protected]

software consolidation and its impact on data management

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