Transcript
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Sound Customer Data Quality for CRMManoj Tahiliani, Senior Manager, Customer Hub Strategy
The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions.The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.
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Agenda
• Data Quality – Pains, Drivers and ROI• Siebel Data Management Solution• Data Quality Products• Best Practices• Oracle Credentials
“Data quality is a Business Issue”
• Virtually all enterprises are experiencing a significant amount of pain directly attributed to data quality issues.
• Significant amounts of wasted labor and lost productivity translate into direct financial losses to the business.
• Some enterprises that have measured the impact have found they are losing multiple millions of dollars each year as a result of poor data quality.
Companies
Velocity of Data Change is Staggering
• 240 businesses will change addresses
• 150 business telephone numbers will change or be disconnected
• 112 directorship (CEO, CFO, etc.) changes will occur
• 20 corporations will fail
• 12 new businesses will open their doors
• 4 companies will change their name
Source: D&B, US Census Bureau, US Department of Health and Human Services, Administrative Office of the US Courts, Bureau of Labor Statistics, Gartner, A.T Kearney, GMA Invoice Accuracy Study; 2 Data: An Unfolding Quality Disaster, Thomas C Redman, DM Review Magazine August 2004, Mintel Global New Products Database (GNPD), 2007. CNNMoney.com 2006, 3Quality is Free, Philip Crosby
• 5,769 individuals in the US will change jobs
• 2,748 individuals will change address
• 515 individuals will get married
• 263 individuals will get divorced
• 186 individuals will declare a personal bankruptcy
Individuals
“If bad data impacts an operation only 5% of the time, it adds a staggering 45% to the cost of operations.”2
“Poor data quality cost business’ 10% to 20% of revenue!”3
Change of Circumstances
• 4.7 Million Marriages• 1.53 Million First Births • 2.04 Million First-time
Home Buyers• 1.9 Million Divorces• 43 Million Residential
Moves• 1.4 Million Work
Retirements
In one hour… In one hour… In one year…
Master data changes at rate of 2% per month.
7 Questions About Your Data
1. Have data initiatives failed or been delayed due to unreliable data?
2. Do you always deliver the right product to the right customer?
3. How many marketing pieces are un-delivered or un-answered?
4. How much time is spent in reworking inaccurate data?
5. Do you face difficulties with regulatory compliance?
6. Is customer satisfaction going down?
7. Do you distrust your data to take critical decisions?
Poor Data Quality is the #1 enemy of CRM Solutions
Out of Date
Rapid changes in a dynamic society: marriages, divorces,
births, deaths, moves
Garbage
Typos, misspellings, transposed numbers, etc.
Fraud
Purposeful misrepresentation of data:
identity theft, wrong information (bankruptcies, occupation, education, etc)
Missed Opportunities
Information that we do not know about (customer
relationships, up-sells, cross-sells)
IT Agility
Ineffective Cross-sell/Up-sell
Lower call center productivity
Increased marketing mailing costs
Reduced CRM adoption rate
Customer Service
Increased data management costs
Increased sales order error
Delayed sales cycle time (B2B)
Mediocre campaign response rate
Operational Efficiency
Risk, Compliance Management
Increased integration costs
Increased the time to bring new projects and services to market
Proliferation of data problems from silos to more applications
Heightened credit risk costs
Potential non-compliance risk
Increased report generation costs
Measuring actual ROI achieved
Example of Customer Data Quality IssueA Simple Customer Table Sample
Name Address City State Zip Phone Email
Bob Williams 36 Jones Avenue Newton MA 02106 617 555 000 bob.williams@yahoo.com
Robert Williams 36 Jones Av. MA 02106 617555000
Burkes, Mike and Ilda 38 Jones av. Nweton MA 02106 617-532-9550 mburkes@gmail.com
Jason Bourne, Bourne & Cie.
76 East 51st Newton MA 617-536-5480 6175541329
… … … … … … …
Mis-fielded data
Matching Records
TyposMixed business and contact names
Multiple Names
Non Standard formats
Missing Data
20 Common Errors & Variation (1)
Variation or Error Example
Sequence errors • Mark Douglas or Douglas Mark
Involuntary corrections • Browne – Brown
Concatenated names • Mary Anne, Maryanne
Nicknames and aliases • Chris – Christine, Christopher, Tina
Noise• Full stops, dashes, slashes, titles,
apostrophes
Abbreviations • Wlm/William, Mfg/Manufacturing
Truncations • Credit Suisse First Bost
Prefix/suffix errors • MacDonald/McDonald/Donald
Spelling & typing errors • P0rter, Beht
Variation or Error Example
Transcription mistakes • Hannah, Hamah
Missing or extra tokens • George W Smith, George Smith, Smith
Foreign sourced data • Khader AL Ghamdi, Khadir A. AlGamdey
Unpredictable use of initials
• John Alan Smith, J A Smith
Transposed characters • Johnson, Jhonson
Localization • Stanislav Milosovich – Stan Milo
Inaccurate dates• 12/10/1915, 21/10/1951,
10121951, 00001951
Transliteration differences • Gang, Kang, Kwang
Phonetic errors • Graeme – Graham
20 Common Errors & Variation (2)
Two Facts about Data Quality
• The Data Quality Challenge is an iceberg• The biggest DQ threats are the ones we do not see.Data Profiling lowers the water line and draws a clear view
of the quality issues
• Data value decays• Data is an asset which value decays over time• Business events can make this worse
• M&A, new applications, new products, new contact files, etc
• Quality is not a one shot process but a constant effort in the enterprise processes.
Data Quality needs to be pervasive and continuous.
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Siebel Data Management Solution
Data Management - Deployment Options
Middleware Application Integration Architecture
Web site
Call Center
SFA PartnerFusionApp
FusionApp
Call SCM
ERP2 LegacyERP 1
MDM
Middleware Application Integration Architecture
FusionApp
Call SCM
ERP2 LegacyERP 1
PartnerData Mgmt Layer
CRM
Trusted Customer Data
Trusted Customer Data
Components of Siebel Data Management
Web ServicesLibrary
Web ServicesLibrary
Publish &SubscribePublish &Subscribe
Transports &Connectors
Transports &Connectors
AuthorizationAuthorization
RegistryRegistry
Profile & Correct
Profile & Correct
History& AuditHistory& Audit
PrivacyMgmt
PrivacyMgmt
Events & Policies
Events & Policies
Import WorkbenchImport Workbench
Identification & Cross-Reference
Identification & Cross-Reference
Source DataHistorySource DataHistory
SurvivorshipSurvivorship
ParseParse
Cleanse & StandardizeCleanse &
StandardizeEnrichEnrich
Manage Decay
Manage Decay
Match & Merge/ Unmerge
Match & Merge/ Unmerge
Roles & Relationships Party
Vertical VariantsRelated Data
Entities
Roles & Relationships Party
Vertical VariantsRelated Data
Entities
Hierarchy ManagementHierarchy
Management
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Data Quality Products
Data Quality Functionality in a Glance
ProfilingProfiling
CleansingCleansing
MatchingMatching
EnrichmentEnrichment
Understand data status, deduce patterns
Tel# is null 30% LName + FName (Asian Countries); FN+MN+PN+LN (Latin);
Addr = #, street, city, state, zip, country; St, Str = Street (ENU/DEU);
Spot and correct data errors; transform to std format/phraseIdentify and eliminate duplicates
Haidong Song = 宋海东 =
Attach additional attributes and categorizations
Haidong Song: “single, 1 child, Summit Estate, DoNot Mail”
Functionality Customer Data example
Comprehensive
data quality
Comprehensive
data quality
Feature
Batch and Real-time
New Data Quality Products
Introducing New Products to provide full spectrum of information quality functions:
• Oracle Data Watch & Repair• Ongoing Discovery of Actual state of Master Data
• Data Governance
• Oracle DQ Cleansing Server: • ASM (Address Standardization Module)• Integrated single engine– supports all countries
• Oracle DQ Matching Server: • Full Administration Access and increased level of support
• Improved performance and enhanced tuning capability
New Data Quality Products
Matching EngineData Quality
Matching Server
Data Quality
Cleansing Server
Administration UI / Rules Editor
Improved performance
18 Languages
52 Languages
Address Standardization Module
240 Languages support
Data QualityProfiling
Profiling Console & Engine
OldOffering (SSA)
NewOffering
ProfilingProfiling
CleansingCleansing
MatchingMatching
EnrichmentEnrichment
Comprehensive
data quality
Comprehensive
data quality
Oracle Data Watch and Repair• Ongoing auditing prevents data
decay, ensures continuous quality• Non intrusive profiling across
existing applications/databases • Quickly narrow in on anomalies• Generate rules to repair problems• Edge Application (no Upgrade
impact)• Out of the box connector to Siebel
CRM
ProfilingOngoing Discovery of State of your Data
• Advanced Validation and Standardization of addresses in more than 240 countries
• Scalable high performance• Integrated single engine– supports all countries• Edge application (no upgrade impact)
ProfilingProfiling
CleansingCleansing
MatchingMatching
EnrichmentEnrichment
Comprehensive
data quality
Comprehensive
data quality
>240 Countries
One API
Oracle DQ Cleansing ServerStandardize & Validate against References
Proven Performances•Not just number of records but also volumes of Txns
•In use on systems with > 800 million records
•> 250,000 txn/hour on large credit systems
•> 1.5 million txn/hour on screening app
•11,000 million index entries on one database
•30,000,000 real time transactions in an hour
Flexible & Adaptive•Smart indexing & fuzzy logic to emulate expert reasoning•Highly configurable•Edge application (no upgrade impact)
Unprecedented Global Coverage
•52 Languages/locales•Cross script matching
Oracle DQ Matching ServerRecords linked to Same or Related Entity
ProfilingProfiling
CleansingCleansing
MatchingMatching
EnrichmentEnrichment
Comprehensive
data quality
Comprehensive
data quality
Hybrid Algorithm Industry’s Best Matching Technology
Heuristic
Probabilistic
Deterministic
PhoneticLinguistic
Empirical
• Best Solution: Hybrid• “Which algorithm is the best in solving
my searching and matching needs?” • The answer is “No single algorithm is
capable of compensating for all the classes of error and variation present in identity data.”.
• In order to achieve a consolidated view of your identity data, you will need a combination of these algorithms, and more, each one addressing a particular class of problem,
• Oracle Matching Server uses a variety of techniques, including the six mentioned here and many more, to address different classes of error and variation in identities
Oracle Data Quality Matching Server
Siebel UCM / CRM
ApplicationObject ManagerUser Interface
Data Admin
Oracle DQ Matching Server
Loader & Utilities
Rule Manager
Key & SearchStrategies
MatchPurposes
Search Server
Update Synchronizer
Console Server
Console
AdministrativeClients
PopulationOverride Mgr
Edit RuleWizard
• Acxiom, D&B Integration
Data EnrichmentAdd Details from External Sources
SCM
Marketing
Web site
legacy
Call Center
SCM
SFA
Acxiom Knowledge Base
Batch or Interactive Delivery
Acxiom Customer Data Integration Services
Clean Recognize Enrich Protect
Integration LayerIntegration Layer
Oracle MDM Schema
Oracle MDM Web ServicesO
racle
M
DM
S
erv
ices
ConsolidateCross
ReferenceAudit & Control
Manage Events GovernPublish
Integration LayerIntegration Layer
ProfilingProfiling
CleansingCleansing
MatchingMatching
EnrichmentEnrichment
Comprehensive
data quality
Comprehensive
data quality
Prospect Mastering with Knowledge-Based MDM
Perform segmentation within Siebel Marketing application
Generate prospect selection criteria
Campaign Planning
Load selected prospect records into Oracle MDM-CDI solution
Consolidate existing customer info with prospects from other sources
Oracle EBSAcxiom/D&BAcxiom/D&B
DataDataProductsProductsMDM-CDIMDM-CDI
SiebelSiebelMarketingMarketing
Load
Loading & Matching
Siebel CRM On Demand Plug & Play Market
Campaign Execution
Campaign Execution
Send criteria and list of existing cust/prospect to Acxiom/D&B etc
Acxiom/D&B produces the net new prospect list and send to customer
Contact informationDemographic dataWealth/income classificationsSegmentation groupingsLifestyle indicators
Prospect Acquisition
Next Generation Data Quality
• Best of Breed Data Quality
• Matching – uses “fuzzy” logic and a unique two-stage approach to overcome the limitations of traditional techniques for 52 languages
• Cleansing – Contains postal address information for 240 countries and territories
• Profiling - discovers the quality, characteristics and potential problems of source data
• Enrichment – integrate with 3rd party content providers for business & consumer data
Embedded best in class Data Quality Open framework & connectors
• Universal DQ Connector
• End to end connector available for selected vendors
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Best Practices
LeadershipLeadership
Policy DefinitionPolicy Definition
Planning and CoordinationPlanning and Coordination
Execution and Decision-MakingExecution and
Decision-Making
Compliance Monitoring and
Enforcement
Compliance Monitoring and
Enforcement
Master Data
Data Management Governance
Record Definition
Data Quality Assessment
Initial Data Quality and Load
Ongoing Data Cleansing and Conversion
Data Management Processes
• Central executive leadership• Enterprise steering committee
to arbitrate issues and enforce the rules
• Coordination and compliance• Define & communicate data
quality expectations • Establish policies, procedures,
success metrics and processes to maintain quality data
• Identify all business and application stakeholders across the enterprise – data owners
• Conduct audit and control• Communication and change
management
Formalize a Governance Framework
Closed Looped DQ
A Day in the Life of a Data StewardData Stewardship is a critical component of DQ Process
1. Runs profiling routines to monitor overall DQ within application• Inspects most crucial or known problem areas
• Gains deep-level understanding of data (e.g. min, max, # nulls..)
2. Creates and applies new data rule based on profiling results
3. Resolves duplicates and creates links
4. Reviews history and audit trail
5. Defines compliance rules and policies
6. Defines event and policies for ongoing monitoring and management
7. Executes corrective action: recover, unmerge, etc.
8. Performs ongoing monitoring of data quality
• Information Completeness– Do we have complete profiling information for our accounts / contacts?– Where are the information holes?
• Information Validity– Does the customer have valid address, phone number and email?– Have we been able to communicate to the customer using stored contact
point information?
• Information Uniqueness (Duplication)– What is the duplicate rate in our accounts and contacts? What is the trend
over time? – Which systems creates the most duplicates?
• Information Accuracy– Is the information still up to date– Does the information have the proper integrity based on available sources
and/or defined business rules?
Data Quality Scorecard
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Credentials
Case Study - Lead Telco
CHALLENGES / OPPORTUNITIES• Drive improved customer experience &
satisfaction• Consolidate customer information from
disparate systems and multiple lines of businesses
• Improve customer data quality• Complete understanding of customer
hierarchies and relationships
SOLUTIONS – Oracle MDM & Data Quality• Enterprise wide customer master to provide
a single view of customer• Match, deduplicate, and consolidated
customer information from multiple systems into the customer master
• Built out customer hierarchies and relationships
RESULTS• Consolidated ~30 mil customer
records from 10+ applications into customer master
• Improved customer data accuracy and completeness
• Provided consistency and integrity of data across multiple operational systems
Selected Oracle Data Quality Customers
Human Resources
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