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Data Quality 101 Liz Crawford, GS1 GO Director Data Quality & GDSN
© 2012 GS1 Agenda 2 Defining DQ Information Supply Chain Business Case: B2B / B2C DQ Recommendations DQ Tools
“Be a yardstick of quality. Some people aren't used to an environment where excellence is expected.” Steve Jobs “I know it, when I see it” Potter Stewart, Associate Justice US Supreme Court
© 2012 GS1 Define Data Quality What DQ is not 4 DQ is not a one-time solution DQ is not linear DQ is not technology specific DQ is not a “thing” – like a program or application - it’s a perception
© 2012 GS1 What is Data Quality? Definition 5 Data Quality is a perception or an assessment of data's reliability and fitness to serve its purpose in a given context. Data are of high quality "if they are fit for their intended uses in operations, decision making, and planning" (J. M. Juran). Quality Control Handbook, New York, NY: McGraw-Hill, 1951
Information Supply Chain "In God we trust, all others bring data.“ Unknown “Quality is everyone's responsibility” W. Edwards Deming 6
© 2012 GS1 Challenge: It’s The Data… Ventana Research conducted a multiple industry survey of large corporations and found the top five concerns regarding data to be: 1. We spend more time reconciling data than analyzing it (33%). 2. No one is accountable for the quality of information (17%). 3. We cannot determine which spreadsheet has correct data (12%). 4. It takes weeks to close our books (11%). 5. We duplicate R&D efforts (6%). 7
© 2012 GS1 Information Supply Chain – B2B 2C 8 Data Providers Data Recipients Demand Supply AggregatorsEnd UsersBusiness UsersSynch
DQ Business Case It is not necessary to change. Survival is not mandatory.” ~W. Edwards Deming
© 2012 GS1 How did we get here? Lack of data governance – an absence of ownership and accountability for key data assets Lack of identified internal / external “authoritative” data sources - leading to poor data accuracy within and across business areas Complex IT infrastructure (multiple systems, many LOBs) Silo-driven, application-centric solutions (TMS, SUS, HXA1) Multiple disconnected processes at local, regional, and corporate levels which may be in conflict Tactical initiatives to “re-solve” data accuracy rather than understanding and addressing root causes 10
© 2012 GS1 Data Quality Benefits – B2B Enterprise Intangibles –Ease of doing business with Users –Decision making - inaccurate information cannot support well informed decisions –Organizational trust –Confidence in enterprise Risk –Regulatory –System investment & development (cannot be fully utilized) –Integration (new systems, acquisitions) –Fraud – exploitation of failures or loopholes within the system Costs –Error prevention – (proactive) –Error detection and correction – (reactive) –Overpayments (claims/settlement costs) –Rework /Increased workload/Increased process times –Increase cost per volume (throughput, avg cost transaction, volume pricing) Revenues –Impaired forecasting –Erroneous bill-backs/Invoicing –Delayed or lost collections 11 Shared handling of data between entities with different business rules and data definitions creates inconsistency and leads to poor data quality across the supply chain. Poor data quality negatively impacts the following key management areas: Improving data quality reduces costs, increases operational efficiency, increases profitability and yields better business information for decision making.
© 2012 GS1 12 “Quality in a product or service is not what the supplier puts in. It is what the customer gets out and is willing to pay for. Customers pay only for what is of use to them and gives them value. Nothing else constitutes quality.” ~Peter Drucker “Retailers need to think of their business as a multi-channel environment that can potentially include mobile, online, and bricks and mortar stores. Winning with shoppers requires a consistent experience across channels …whether it be price, service, reviews, selection, style or other key attributes." John Burbank, President of Strategic Initiatives, Nielsen (PC World, 3/12/12)
© 2012 GS1 Data Quality Drivers – “C2B” 13 Big data – 2012 year of “Big Data” Technology is rapidly changing One provider - Apple (Q1 FY 2012) 37.04M iPhones (up 128%) 15.4M tablets (up 111%) Test – (5 min) search for “shopping” apps 22 general shopping aps 30 brand specific
© 2012 GS1 Are you standing on a burning platform? 14
DQ Recommendations Do or do not… there is no try. ~Yoda Quality is never an accident. It is always the result of intelligent effort. John Ruskin
© 2012 GS1 Chrysler BuildingTaipei 101 DQ is Foundational But, How Big a Foundation is Needed? Not a formula: there is no E=mc 2 Best practices are the foundation not the ceiling Your foundation is unique: Decisions regarding the breadth, depth, and timing of DQ will determine the scope and resource requirements for DQ 16
© 2012 GS1 DQ Strategy Essential Elements Data managed at enterprise level Data ownership & accountability, clearly defined roles & responsibilities Development efforts that affect critical business data championed from the top down and supported with change management processes An enterprise forum to ensure end-to-end impact assessment of all data management efforts Adoption and enforcement of best practices including standardization, definitions, rules and business processes. 17 A strategic approach to DQ generates accurate and reliable business information which becomes an enterprise asset.
© 2012 GS1 OK, so what should we do ? 18 Get Executive Buy-in DQ Assessment Look at Critical Business Processes –Internal Lens – run the business –External Lens – supporting customer POV Identify Key Attributes when missing or incorrect will cause those critical business processes to fail. Based on standards Fix the critical stuff Quick wins - Low hanging fruit /biggest bang for your buck, 80/20 In house or external Third party Synchronize the data Information Governance Program (Long Term) Policies, procedures, information lifecycle, organization (roles responsibilities)
DQ Tools Help and Guidance Quality is not an act, it is a habit. Aristotle Quality means doing it right when no one is looking. Henry Ford
© 2012 GS1 How can GS1 Help? 20 GS1 Program/ Projects OverviewOwnerStatusUser Base Data Quality Framework (DQF) Comprehensive platform of Industry DQ Guidelines GS1 GOAvailable / Implemented Local MO’s Data Sources Data Recipients 3 rd Parties GS1 MO Programs Various local support programs with many different capabilities and features Local Country MO Available / In Progress Local MO’s Data Sources Data Recipients Data Quality 2020Expansion of DQFGS1 GOIn ProgressLocal MO’s Data Sources Data Recipients 3 rd Parties
© 2012 GS1 GS1 Data Quality Framework A checklist of current best practices and desirable requirements for an optimal management of data quality. The Framework contains three main sections: Requirements for a good Data Quality Management System A product inspection procedure A self-assessment procedure for companies Developed and endorsed by the Industry
© 2012 GS1 Who can help me? Users - Your local GS1 MO Local DQ Programmes serving local needs. There is no interoperability between MO programmes but a common thread is the Data Quality Framework (DQF) From the GS1 MO Survey existing or planned DQ Programmes: 86% have Awareness & Communication activities 74% perform Community Management activities 70% offer Training & Education activities 52% with Consulting activities 56% perform Validations & Integrity Checks 43% have Product Inspections. 30% have Accreditation & Authentication activities 39% provide On-boarding tools Full survey detail is on the GS1 DQ website
© 2012 GS1 GS1 MO DQ Programmes Inventory Summary results reflecting current and future activities Training & Education Data Quality Awareness & Communication Community Management Product Inspection s Validations & Integrity Checks Accreditation & Authentication On- boarding ToolsConsulting GS1 MO Australia Brazil Canada China Colombia Croatia France Germany Hong Kong Hungary India Italy Japan Mexico Netherlands New Zealand Poland Russia South Africa Spain Sweden UK US Table key Active; actions or services of this sort have been rolled-out and/or implemented. Under development; activities of this type are not yet fully operational but they are being developed and are expected to be released in the short term. Tentative; actions may tentatively occur in the short- term future but no work has been started. Plans may be not fully approved or may be still dependant on other factors. None; No plans or deployed activities exist in this area. Data return by Category and MO Who can help me? Your local GS1 MO
© 2012 GS1 Who can help me? GS1 GO 24 Training & Modules on DQ & DQF Online Self-Paced (updated) Hands on sessions – as requested by MOs, As there demand dictates we may schedule sessions in conjunction with I&S or regional forum events, or special sessions when required.
© 2012 GS1 The Data Quality Framework PACKAGE is publically available All you need to use the Framework in one package Includes: The Data Quality Framework v3.0 Implementation Guides (user’s manual!) Automated scorecard for self-assessment Automated scorecard for KPIs Data Quality Introductory Presentation Read me
© 2012 GS1 Data Quality Website and Library Website Library Data Quality Framework and support documentation Case studies, white papers Data Quality Program Internal Implementation Example Data Quality Videos Links to Related Technical Documents on standards
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Liz Crawford Director, Data Quality & GDSN Princeton Pike Corporate Center 1009 Lenox Drive, Suite 202 Lawrenceville, NJ T W Contact Details
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