Trillium Software Safeguard MDM Success with Data Quality and Data Governance

Transcription

1 Trillium Software Safeguard MDM Success with Data Quality and Data Governance

2 Today s Speakers Fil Sanna Manager Customer MDM, Millipore Corporation Ed Wrazen VP Product Marketing Trillium Software Harte-Hanks Trillium Software 25 Linnell Circle Billerica, MA

3 Agenda Millipore Customer MDM Project Trillium Software Overview Trillium Software MDM Solutions Q&A Harte-Hanks Trillium Software 25 Linnell Circle Billerica, MA

4 Millipore at a Glance Headquartered in Billerica, MA With 5,900 employees worldwide, Millipore is an S&P 500 company with offices in more than 31 countries 2008 Revenue: $1.6B

5 Bioscience Capabilities To make it easier for customers to find our products and services, we ve organized our Bioscience Division offerings into the following capabilities:

6 Bioprocess Capabilities Our Bioprocess Division products and services are organized into the following three capabilities:

7 Millipore s Global Footprint SCOTLAND DUNDEE LIVINGSTON THE NETHERLANDS AMSTERDAM LEIDEN ESPOO ENGLAND WATFORD MOSCOW ONTARIO KANKAKEE, IL JAFFREY, NH CORK WARSAW SCHWALBACH CALIFORNIA HAYWARD RAMONA TEMECULA ST. CHARLES, MO NEW JERSEY PHILLIPSBURG MASSACHUSETTS BEDFORD BILLERICA BURLINGTON DANVERS MADRID MILAN FRANCE MOLSHEIM ST. QUENTIN BEIJING SHANGHAI SEOUL TOKYO MEXICO CITY BANGALORE TAIPEI SINGAPORE SAO PAULO SYDNEY MELBOURNE

8 Leverage Technology to Solve Business Problems Focus effort on solving business problems, not on fixing systems Improve quoting process Customer selfservice Improve efficiency of customer service reps Improve tracking of installed base Automate customer segmenta -tion Integration Layer Customer Hub Oracle Webstore SFDC Fisher D&B

9 CDH Program Timeline Q3-08 Q4-08 Q1-09 Q2-09 Q3-09 Q4-09 Start Data Governance (DG) Build data governance organization Data analysis data dictionary & standards Build Pilot Hub (CDH) ERP, CRM & webstore for North America External distributor feed D&B enrichment for hierarchies Enhanced reporting CDH Hypercare Start DG in Europe Extend data dictionary & business rules CDH 2 Europe Include ERP, webstore & D&B for Europe Expand reporting to include Europe Transactional Integration Reqs CDH 3 New Sources Include B2B & CRM for Europe Include Field Service Quote to Order E-Commerce Field Service Other

10 Data Governance Leads MDM Enterprise Data Governance Council Responsible for providing overall direction and Responsible for providing overall direction and vision for Millipore s Enterprise Data initiative vision for Millipore s Enterprise Data initiative Legal and Compliance Advisors Provide legal, regulatory and other insights regarding Enterprise Data External Data Standards EDI, HL7, XML and other external data requirements Enterprise Enterprise Data Data Governor Governor Responsible for identifying enterprise Responsible for identifying enterprise data goals and definitions by Business data goals and definitions by Business Unit while conducting the data steward Unit while conducting the data steward working group. Responsible for working group. Responsible for execution of the Enterprise Data execution of the Enterprise Data Strategy and provides guidance on Strategy and provides guidance on what Enterprise business data is what Enterprise business data is required to support business needs required to support business needs Data Data Steward Steward Working Working Group Group Defines policies by which Enterprise Data is Defines policies by which Enterprise Data is managed, accessed and repaired, and monitors managed, accessed and repaired, and monitors data quality and stewardship activity via KPIs. data quality and stewardship activity via KPIs. Manages data quality within specific domains, and Manages data quality within specific domains, and resolve data quality conflicts and exceptions resolve data quality conflicts and exceptions CDH Owner Responsible for representing the CDH Responsible for representing the CDH for issue resolution at the CDH working for issue resolution at the CDH working group level while incorporating best group level while incorporating best practices practices CDH Support Team CDH resources who support the working group through technically related activities Centralized Governance Federated Stewardship Enterprise Data Consumers Represent organizations or applications that depend on clean, accurate data to perform their primary functions Business Business Unit Unit Stewards Stewards Accountable to fix, mitigate and maintain the quality of Accountable to fix, mitigate and maintain the quality of data within their business units data within their business units Technical Stewards Business Stewards Provide knowledge of Provide knowledge of how systems organize business data and and process Enterprise processes for capturing Data within a b.u. and storing Enterprise Data

11 Data Governance Highlights Understanding of problem evolved From dirty data to definitional & relationship issue Data modeling as path to shared understanding Systematic data profiling & analysis Efficiently handling a large of volume of detailed issues Writing the business rule logic Cross-functional and iterative approach Representation from key business units Data knowledge Involved in the process of how data from the hub will get consumed

12 How Data Gets Dirty Intra-System Issues Typos and errors Application not aligned to business process Duplicate values Difficulty in retiring data Inter-System Issues Business processes Completeness data only captured when relevant for immediate operational needs! Data Applications Data! Different standards across systems Data accuracy degrades over time

13 Building Relationships (aka Data Shredding ) Example UT Southwestern Medical Center ERP System University of Texas University of Texas Austin University of North Texas Southwestern Medical Center University of Texas MD Anderson University of Texas Medical Branch University of Texas Permian Basin CRM System University of Texas Southwestern Medical Center Account 1 Address 1 Address 1 Contact 1 Account 2 Address 2 Address 2 Contact 2 Account 3 Address 3 Address 3 Contact 3 Account 4 Address 4 Address 4 Contact 4 Account 5 Address 5 Address 5 Contact 5 Account 6 Address 6 Address 6 Contact 6 Account 7 Address 7 Address 7 Contact 7 Account 56 Address 133 Address 209 Contact 1397

14 Linking Customers & Accounts Customers Selling Relationships ABC US Ops ABC Inc XYZ Subsidiary ABC EU HQ ABC Global Account Net 45 days 10% discount Max credit $5M R&D Process Development Manufacturing 15% Charge Freight Net 30 Net 30 days 15% Discount Charge freight GSA Cust. Freight Net 60 Net 60 days GSA Pricing Customer pays freight Parties Accounts

15 Vendor Selection Process Vendor Shortlist Primary Criteria Deciding Factors Data Quality Trillium Software (Quality & Discovery) SAP Business Objects (First Logic) Platon Rich profiling capability for data governance team Sophisticated parsing Global coverage Integration with MDM platform All major MDM vendors recommended Trillium Clearly the market leader and deepest functionality MDM Platform Siperian Oracle (CDH & UCM) Flexible data model Ability to integrate across diverse applications Ability to handle complex data inter-relationships Professional services & support Siperian approach and framework very flexible Siperian vision more coherent (single platform for customer & product MDM)

16 CDH at Millipore Pilot Phase Scope Includes US & Canada Data from Seven Internal & One External Sources ERP Accounts Data Stewards ERP Contacts Distributor Tx s Siperian Hub B2B Tx s Web Account Registration ETL Match & Merge Millipore Customer Hub Reporting CRM System 1 Trillium Software Standardize & Validate CRM System 2 D&B Hierarchy

17 Packed Fields Names have been altered ADDRESS_LINE_1 14 Clyde St 225 Randall Laboratories Division Of Physical & Biological Sciences Nat Sci II Annex, 2250 Alcazar St MC 9317 GNH 6602 Stork School of Med--Diabetes Research 229 Stanley Bldg No 3206 Molec & Cell Biology Dept 2425 Stockton Blvd. 650 Welcome Hospital Cell Lbiology and Human Anatomy Lab Technician 2800 Plymouth Road Bldg. 25/257A Ann Arbor Mi University Blvd Johnson center for cancer Biology & MRB Bldg Rm Dept of Pathology C/O Nocunex GMP Warehouse nd Ave Research Bldg III Dermatology Rm 3100D 695 Charles Young Dr. 700 Chesterfield Parkway West BB5M / Biologics Dept 700 Pilottown Rd Contacts B 34 Hildebrand # 1460 P. Frank Group Chemistry Dept Bikini Rd Dept. of Pathology and Laborat W. Carson St. BOX 12 Eastern Point Rd Exploratory Biolog CB5/ Science Center Dr Parties Location Info Institute for Bioinformatics 236 Info & Computer Science Bldg. 2 University of State, Irvine Sampson Gene Therapy Ctr 1600 SW Archer Rd, Pediatrics Bldg ARB, Rm RG-179, Box 1234 The University Of City Hsc Pediatric Infectious Diseases 6431 Fannin, MSB Smith School Of Medicine, MIMG Dept ADDRESS_LINE_2 C/O Williams Contractors**Inv Do Not Use** Special Codes ^DIAG^!!DUP 2005 NW Sammamish Rd Suite 107 I-90 Lake Pl Brough Genetics Research Center, Rm 2309 C/O Univ College of Marine Studies PO 13ZLV84591 **Invalid Use G1623-S57398** PO RR1213SD0 **Conversion See ** 650 Charles E Young Dr South (Rm CHS)

18 Some Statistics Stage Degree of Consolidation Degree of Linkage Orgs Contacts Locations Org-Contact Account D&B Baseline (no Trillium) N/A N/A N/A Vanilla Trillium Quality ~60% ~85% ~70% Tuned Trillium Quality ~95% ~95% ~85% Effective triage of base data quality and structure was the most significant contributor to overall results

19 Projected Business Benefits Improved data is not the end it is the means to the end Improved customer analytics Improve the user experience & efficiency of e-commerce Improve customer and account identification within order management Improve customer and account identification within field service Improve the quote-to-order process Better manage global credit risks Avoid business dealings with fraudulent or risky customers Improved data can now be leveraged within numerous systems business processes

20 Success Factors & Lessons Learned Focus on business problems/ opportunities Clean data in itself is useless Understand root causes of dirty data Data governance must lead the way Subject matter experts need full visibility into the data The devil is in the details Shredding data Easier to de-duplicate and create linkage from shredded data More granular concepts are easier to define (e.g. site versus customer ) Data, systems & processes are bound together Hard to make progress without addressing all three Siperian & Trillium complement each other very well Flexible tools, allow for high degree of customization Great support and cooperation among the firms

21 Agenda Millipore Customer MDM Project Trillium Software Overview Trillium Software MDM Solutions Q&A Harte-Hanks Trillium Software 25 Linnell Circle Billerica, MA

22 About Trillium Software Trillium Software A business unit of Harte-Hanks NYSE listed (HHS) $1+ B Revenue, 6,000 employees worldwide Worldwide offices in Europe Asia-Pacific Americas Double digit profitable growth for 13 years Trillium Software Customers Forbes Global 10 Fortune 100 Sampling Recent Ratings Forrester Wave Leader Gartner 2008 Magic Quadrant: Leader Current Analysis: Very threatening to competition Bloor Research (UK): Best in Class World Class Award Portfolio Integrates seamlessly in all environments Harte-Hanks Trillium Software 25 Linnell Circle Billerica, MA

23 Getting By or At Risk? Harte-Hanks Trillium Software 25 Linnell Circle Billerica, MA

24 MDM Solution Architecture Harte-Hanks Trillium Software 25 Linnell Circle Billerica, MA

25 Data Quality Services Is Data Quality Included in MDM? Functionality Profiling Automated Identification & Routing Context-Sensitive Data Cleansing Trillium Software Highly interactive data analysis workbench Rich views of patterns, relationships, rules and anomalies in the data Deductive approach based on all available data Out-of-box rules Context-sensitive Out-of-box rules Typical MDM Platform Not included Deterministic logic based on a limited set of fields Rules must be created One-to-one substitution based on literal values Rules must be created Verification & Enrichment Matching Merging & Survivorship Address verification & geo-coding out-ofbox Easy to append other enrichment data Robust matching based on tunable & auditable rules Highly granular, multi-matching options Robust selection of surviving values on field-by-field basis Not included Capabilities vary from very basic (exact) matching to fairly robust fuzzy logic Capabilities vary from very basic logic to select surviving record to fairly robust fuzzy logic Harte-Hanks Trillium Software 25 Linnell Circle Billerica, MA

26 Trillium Software System Discover anomalies, rules, relationships and meanings in existing datasets Define data metrics and targets Relate metrics to business impact Trending and Scorecard reporting Red, Amber, Green conditions Apply robust out-of-the-box rules to standardize data from around the globe Enhancement with additional attributes such as geocodes, product classifications, etc. including using external Automatic de-duplication, sources relationship linking and merging based on transparent, tunable rules Harte-Hanks Trillium Software 25 Linnell Circle Billerica, MA

27 Global Processing TSS Process data from around the world, according to local standards Robust Country-specific standardization and matching rules, Street-level address correction, validation, and formatting Standard Basic Country-specific standardization and matching rules, Street-level address correction, validation, and formatting City-level address correction, formatting, and validation Harte-Hanks Trillium Software 25 Linnell Circle Billerica, MA

28 Enterprise Data Quality Services Harte-Hanks Trillium Software 25 Linnell Circle Billerica, MA

29 Pain Points Addressed The challenges of MDM without robust data quality functionality Limited insight into data issues/ scope of data quality problems Inability to quickly cleanse large volumes of dirty legacy data Separate point DQ solutions for each country, system or business process Need to define & maintain thousands of data transformation rules Reduced repeatability & auditability for data transformation processes Low match accuracy Opaque rationale for match results Unacceptable delays between data capture and data validation High cost for manual data scrubbing/ reconciliation efforts Without solid data quality, MDM projects are at risk of failure Harte-Hanks Trillium Software 25 Linnell Circle Billerica, MA

30 Thank You Questions?? US Office: UK/ EMEA Office: +44 (0) Central European Office: Harte-Hanks Trillium Software 25 Linnell Circle Billerica, MA