Sound Data Quality for CRM


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Sound Data Quality for CRM

  1. 1. Sound Customer Data Quality for CRM Manoj Tahiliani, Senior Manager, Customer Hub Strategy
  2. 2. 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.
  3. 3. Agenda <ul><li>Data Quality – Pains, Drivers and ROI </li></ul><ul><li>Siebel Data Management Solution </li></ul><ul><li>Data Quality Products </li></ul><ul><li>Best Practices </li></ul><ul><li>Oracle Credentials </li></ul><Insert Picture Here>
  4. 4. “ Data quality is a Business Issue” <ul><li>Virtually all enterprises are experiencing a significant amount of pain directly attributed to data quality issues . </li></ul><ul><li>Significant amounts of wasted labor and lost productivity translate into direct financial losses to the business. </li></ul><ul><li>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. </li></ul>
  5. 5. Velocity of Data Change is Staggering <ul><li>240 businesses will change addresses </li></ul><ul><li>150 business telephone numbers will change or be disconnected </li></ul><ul><li>112 directorship (CEO, CFO, etc.) changes will occur </li></ul><ul><li>20 corporations will fail </li></ul><ul><li>12 new businesses will open their doors </li></ul><ul><li>4 companies will change their name </li></ul>Companies 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. 2006, 3 Quality is Free, Philip Crosby <ul><li>5,769 individuals in the US will change jobs </li></ul><ul><li>2,748 individuals will change address </li></ul><ul><li>515 individuals will get married </li></ul><ul><li>263 individuals will get divorced </li></ul><ul><li>186 individuals will declare a personal bankruptcy </li></ul>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 <ul><li>4.7 Million Marriages </li></ul><ul><li>1.53 Million First Births </li></ul><ul><li>2.04 Million First-time Home Buyers </li></ul><ul><li>1.9 Million Divorces </li></ul><ul><li>43 Million Residential Moves </li></ul><ul><li>1.4 Million Work Retirements </li></ul>In one hour… In one hour… In one year… Master data changes at rate of 2% per month.
  6. 6. 7 Questions About Your Data <ul><li>Have data initiatives failed or been delayed due to unreliable data? </li></ul><ul><li>Do you always deliver the right product to the right customer? </li></ul><ul><li>How many marketing pieces are un-delivered or un-answered? </li></ul><ul><li>How much time is spent in reworking inaccurate data? </li></ul><ul><li>Do you face difficulties with regulatory compliance? </li></ul><ul><li>Is customer satisfaction going down? </li></ul><ul><li>Do you distrust your data to take critical decisions? </li></ul>
  7. 7. 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)
  8. 8. IT Agility <ul><li>Ineffective Cross-sell/Up-sell </li></ul><ul><li>Lower call center productivity </li></ul><ul><li>Increased marketing mailing costs </li></ul><ul><li>Reduced CRM adoption rate </li></ul>Customer Service <ul><li>Increased data management costs </li></ul><ul><li>Increased sales order error </li></ul><ul><li>Delayed sales cycle time (B2B) </li></ul><ul><li>Mediocre campaign response rate </li></ul>Operational Efficiency Risk, Compliance Management <ul><li>Increased integration costs </li></ul><ul><li>Increased the time to bring new projects and services to market </li></ul><ul><li>Proliferation of data problems from silos to more applications </li></ul><ul><li>Heightened credit risk costs </li></ul><ul><li>Potential non-compliance risk </li></ul><ul><li>Increased report generation costs </li></ul>Measuring actual ROI achieved
  9. 9. Example of Customer Data Quality Issue A Simple Customer Table Sample Name Address City State Zip Phone Email Bob Williams 36 Jones Avenue Newton MA 02106 617 555 000 [email_address] Robert Williams 36 Jones Av. MA 02106 617555000 Burkes, Mike and Ilda 38 Jones av. Nweton MA 02106 617-532-9550 [email_address] Jason Bourne, Bourne & Cie. 76 East 51 st Newton MA 617-536-5480 6175541329 … … … … … … … Mis-fielded data Matching Records Typos Mixed business and contact names Multiple Names Non Standard formats Missing Data
  10. 10. 20 Common Errors & Variation (1) Variation or Error Example Sequence errors <ul><li>Mark Douglas or Douglas Mark </li></ul>Involuntary corrections <ul><li>Browne – Brown </li></ul>Concatenated names <ul><li>Mary Anne, Maryanne </li></ul>Nicknames and aliases <ul><li>Chris – Christine, Christopher, Tina </li></ul>Noise <ul><li>Full stops, dashes, slashes, titles, apostrophes </li></ul>Abbreviations <ul><li>Wlm/William, Mfg/Manufacturing </li></ul>Truncations <ul><li>Credit Suisse First Bost </li></ul>Prefix/suffix errors <ul><li>MacDonald/McDonald/Donald </li></ul>Spelling & typing errors <ul><li>P0rter, Beht </li></ul>
  11. 11. 20 Common Errors & Variation (2) Variation or Error Example Transcription mistakes <ul><li>Hannah, Hamah </li></ul>Missing or extra tokens <ul><li>George W Smith, George Smith, Smith </li></ul>Foreign sourced data <ul><li>Khader AL Ghamdi, Khadir A. AlGamdey </li></ul>Unpredictable use of initials <ul><li>John Alan Smith, J A Smith </li></ul>Transposed characters <ul><li>Johnson, Jhonson </li></ul>Localization <ul><li>Stanislav Milosovich – Stan Milo </li></ul>Inaccurate dates <ul><li>12/10/1915, 21/10/1951, 10121951, 00001951 </li></ul>Transliteration differences <ul><li>Gang, Kang, Kwang </li></ul>Phonetic errors <ul><li>Graeme – Graham </li></ul>
  12. 12. Two Facts about Data Quality <ul><li>The Data Quality Challenge is an iceberg </li></ul><ul><ul><li>The biggest DQ threats are the ones we do not see. </li></ul></ul><ul><ul><li>Data Profiling lowers the water line and draws a clear view of the quality issues </li></ul></ul><ul><li>Data value decays </li></ul><ul><ul><li>Data is an asset which value decays over time </li></ul></ul><ul><ul><li>Business events can make this worse </li></ul></ul><ul><ul><ul><li>M&A, new applications, new products, new contact files, etc </li></ul></ul></ul><ul><ul><li>Quality is not a one shot process but a constant effort in the enterprise processes. </li></ul></ul><ul><ul><li>Data Quality needs to be pervasive and continuous . </li></ul></ul>
  13. 13. <Insert Picture Here> Siebel Data Management Solution
  14. 14. Data Management - Deployment Options Middleware Application Integration Architecture Middleware Application Integration Architecture CRM Web site Call Center SFA Partner Fusion App Fusion App Call SCM ERP2 Legacy ERP 1 MDM Fusion App Call SCM ERP2 Legacy ERP 1 Partner Data Mgmt Layer
  15. 15. Components of Siebel Data Management Trusted Customer Data Web Services Library Publish & Subscribe Transports & Connectors Authorization Registry Profile & Correct History & Audit Privacy Mgmt Events & Policies Import Workbench Identification & Cross-Reference Source Data History Survivorship Parse Cleanse & Standardize Enrich Manage Decay Match & Merge / Unmerge Roles & Relationships Party Vertical Variants Related Data Entities Hierarchy Management
  16. 16. <Insert Picture Here> Data Quality Products
  17. 17. Data Quality Functionality in a Glance Profiling Cleansing Matching Enrichment 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/phrase Identify 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 Feature Batch and Real-time
  18. 18. New Data Quality Products <ul><ul><li>Introducing New Products to provide full spectrum of information quality functions: </li></ul></ul><ul><ul><ul><ul><li>Oracle Data Watch & Repair </li></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Ongoing Discovery of Actual state of Master Data </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Data Governance </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><li>Oracle DQ Cleansing Server : </li></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>ASM (Address Standardization Module) </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Integrated single engine– supports all countries </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><li>Oracle DQ Matching Server : </li></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Full Administration Access and increased level of support </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Improved performance and enhanced tuning capability </li></ul></ul></ul></ul></ul>
  19. 19. New Data Quality Products Matching Engine Data Quality Matching Server Data Quality Cleansing Server Administration UI / Rules Editor Improved performance 18 Languages 52 Languages Address Standardization Module 240 Languages support Data Quality Profiling Profiling Console & Engine Old Offering (SSA) New Offering
  20. 20. Profiling Cleansing Matching Enrichment Comprehensive data quality <ul><li>Oracle Data Watch and Repair </li></ul><ul><li>Ongoing auditing prevents data decay, ensures continuous quality </li></ul><ul><li>Non intrusive profiling across existing applications/databases </li></ul><ul><li>Quickly narrow in on anomalies </li></ul><ul><li>Generate rules to repair problems </li></ul><ul><li>Edge Application (no Upgrade impact) </li></ul><ul><li>Out of the box connector to Siebel CRM </li></ul>Profiling Ongoing Discovery of State of your Data
  21. 21. <ul><li>Advanced Validation and Standardization of addresses in more than 240 countries </li></ul><ul><li>Scalable high performance </li></ul><ul><li>Integrated single engine– supports all countries </li></ul><ul><li>Edge application (no upgrade impact) </li></ul>Profiling Cleansing Matching Enrichment Comprehensive data quality >240 Countries One API Oracle DQ Cleansing Server Standardize & Validate against References
  22. 22. <ul><li>Proven Performances </li></ul><ul><li>Not just number of records but also volumes of Txns </li></ul><ul><li>In use on systems with > 800 million records </li></ul><ul><li>> 250,000 txn/hour on large credit systems </li></ul><ul><li>> 1.5 million txn/hour on screening app </li></ul><ul><li>11,000 million index entries on one database </li></ul><ul><li>30,000,000 real time transactions in an hour </li></ul><ul><li>Flexible & Adaptive </li></ul><ul><li>Smart indexing & fuzzy logic to emulate expert reasoning </li></ul><ul><li>Highly configurable </li></ul><ul><li>Edge application (no upgrade impact) </li></ul><ul><li>Unprecedented Global Coverage </li></ul><ul><li>52 Languages/locales </li></ul><ul><li>Cross script matching </li></ul>Oracle DQ Matching Server Records linked to Same or Related Entity Profiling Cleansing Matching Enrichment Comprehensive data quality
  23. 23. Hybrid Algorithm Industry’s Best Matching Technology Heuristic Probabilistic Deterministic Phonetic Linguistic Empirical <ul><li>Best Solution: Hybrid </li></ul><ul><li>“ Which algorithm is the best in solving my searching and matching needs?” </li></ul><ul><li>The answer is “No single algorithm is capable of compensating for all the classes of error and variation present in identity data.”. </li></ul><ul><li>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, </li></ul><ul><li>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 </li></ul>
  24. 24. Oracle Data Quality Matching Server Siebel UCM / CRM Application Object Manager User Interface Data Admin Oracle DQ Matching Server Loader & Utilities Rule Manager Key & Search Strategies Match Purposes Search Server Update Synchronizer Console Server Console Administrative Clients Population Override Mgr Edit Rule Wizard Indexes Rules Base
  25. 25. <ul><li>Acxiom, D&B Integration </li></ul>Data Enrichment Add Details from External Sources Profiling Cleansing Matching Enrichment Comprehensive data quality
  26. 26. Prospect Mastering with Knowledge-Based MDM <ul><li>Perform segmentation within Siebel Marketing application </li></ul><ul><li>Generate prospect selection criteria </li></ul>Campaign Planning <ul><li>Load selected prospect records into Oracle MDM-CDI solution </li></ul><ul><li>Consolidate existing customer info with prospects from other sources </li></ul>Oracle EBS Acxiom/D&B Data Products MDM-CDI Siebel Marketing Load Loading & Matching Siebel CRM On Demand <ul><li>Plug & Play Market Campaign Execution </li></ul>Campaign Execution <ul><li>Send criteria and list of existing cust/prospect to Acxiom/D&B etc </li></ul><ul><li>Acxiom/D&B produces the net new prospect list and send to customer </li></ul><ul><ul><li>Contact information </li></ul></ul><ul><ul><li>Demographic data </li></ul></ul><ul><ul><li>Wealth/income classifications </li></ul></ul><ul><ul><li>Segmentation groupings </li></ul></ul><ul><ul><li>Lifestyle indicators </li></ul></ul>Prospect Acquisition
  27. 27. Next Generation Data Quality <ul><li>Best of Breed Data Quality </li></ul><ul><li>Matching – uses “fuzzy” logic and a unique two-stage approach to overcome the limitations of traditional techniques for 52 languages </li></ul><ul><li>Cleansing – Contains postal address information for 240 countries and territories </li></ul><ul><li>Profiling - discovers the quality, characteristics and potential problems of source data </li></ul><ul><li>Enrichment – integrate with 3 rd party content providers for business & consumer data </li></ul>Embedded best in class Data Quality Open framework & connectors <ul><li>Universal DQ Connector </li></ul><ul><li>End to end connector available for selected vendors </li></ul>
  28. 28. <Insert Picture Here> Best Practices
  29. 29. Formalize a Governance Framework Leadership Policy Definition Planning and Coordination Execution and Decision-Making 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 <ul><li>Central executive leadership </li></ul><ul><li>Enterprise steering committee to arbitrate issues and enforce the rules </li></ul><ul><li>Coordination and compliance </li></ul><ul><li>Define & communicate data quality expectations </li></ul><ul><li>Establish policies, procedures, success metrics and processes to maintain quality data </li></ul><ul><li>Identify all business and application stakeholders across the enterprise – data owners </li></ul><ul><li>Conduct audit and control </li></ul><ul><li>Communication and change management </li></ul>Closed Looped DQ
  30. 30. A Day in the Life of a Data Steward Data Stewardship is a critical component of DQ Process <ul><li>Runs profiling routines to monitor overall DQ within application </li></ul><ul><ul><li>Inspects most crucial or known problem areas </li></ul></ul><ul><ul><li>Gains deep-level understanding of data (e.g. min, max, # nulls..) </li></ul></ul><ul><li>Creates and applies new data rule based on profiling results </li></ul><ul><li>Resolves duplicates and creates links </li></ul><ul><li>Reviews history and audit trail </li></ul><ul><li>Defines compliance rules and policies </li></ul><ul><li>Defines event and policies for ongoing monitoring and management </li></ul><ul><li>Executes corrective action: recover, unmerge, etc. </li></ul><ul><li>Performs ongoing monitoring of data quality </li></ul>
  31. 31. <ul><li>Information Completeness </li></ul><ul><ul><li>Do we have complete profiling information for our accounts / contacts? </li></ul></ul><ul><ul><li>Where are the information holes? </li></ul></ul><ul><li>Information Validity </li></ul><ul><ul><li>Does the customer have valid address, phone number and email? </li></ul></ul><ul><ul><li>Have we been able to communicate to the customer using stored contact point information? </li></ul></ul><ul><li>Information Uniqueness (Duplication) </li></ul><ul><ul><li>What is the duplicate rate in our accounts and contacts? What is the trend over time? </li></ul></ul><ul><ul><li>Which systems creates the most duplicates? </li></ul></ul><ul><li>Information Accuracy </li></ul><ul><ul><li>Is the information still up to date </li></ul></ul><ul><ul><li>Does the information have the proper integrity based on available sources and/or defined business rules? </li></ul></ul>Data Quality Scorecard
  32. 32. <Insert Picture Here> Credentials
  33. 33. Case Study - Lead Telco <ul><li>CHALLENGES / OPPORTUNITIES </li></ul><ul><li>Drive improved customer experience & satisfaction </li></ul><ul><li>Consolidate customer information from disparate systems and multiple lines of businesses </li></ul><ul><li>Improve customer data quality </li></ul><ul><li>Complete understanding of customer hierarchies and relationships </li></ul><ul><li>SOLUTIONS – Oracle MDM & Data Quality </li></ul><ul><li>Enterprise wide customer master to provide a single view of customer </li></ul><ul><li>Match, deduplicate, and consolidated customer information from multiple systems into the customer master </li></ul><ul><li>Built out customer hierarchies and relationships </li></ul><ul><li>RESULTS </li></ul><ul><li>Consolidated ~30 mil customer records from 10+ applications into customer master </li></ul><ul><li>Improved customer data accuracy and completeness </li></ul><ul><li>Provided consistency and integrity of data across multiple operational systems </li></ul>
  34. 34. Selected Oracle Data Quality Customers Human Resources