How can you improve Data Quality Best Practices

How can you improve Data Quality? Best Practices GS 1 Data Quality Framework (DQF) Introduction "Believe one who knows by experience" Unknown “Simplicity is the ultimate sophistication” Leonardo da Vinci

How can you improve Data Quality? 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%). 2 © 2012 GS 1

How can you improve Data Quality? It’s more than the Data… Capscan Ltd. commissioned a survey in 2012 to document organizations’ progress in improving Data Quality since their last survey in 2010. They concluded: • Financial stresses and weak trading conditions are forcing companies to look inward and not provide the means, processes or systems required to allow data quality initiatives to be successful. • Data is not measured or financially valued enough at a strategic level. • Organisations do not truly understand the benefits of data quality management • For most organisations DQM remains an issue of operational improvement and not something of true strategic relevance. © 2012 GS 1 3

How can you improve Data Quality? Obstacles • 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 • 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 4 © 2012 GS 1

How can you improve Data Quality? The Opportunity • Organisations need reliable and usable business information based on high quality data. • Data management processes and systems are inefficient and distributed throughout the enterprise – producing data of poor quality which generates imprecise, vague and inaccurate information. • Without data of high quality, organizations lack the ability to generate and leverage reliable business information across the enterprise to make informed decisions. Data Quality © 2012 GS 1 Information Quality Business Intelligence 5

“Methods or techniques that have consistently shown results superior to those achieved with other means, and that are used as a benchmark. (Best in class) Best Practices © 2012 GS 1 6

How can you improve Data Quality? Best Practices - Foundation • Best Practices are the floor not the ceiling • Best Practices by their definition are already proven and should be your foundation. • You must have the proper foundation from which to innovate and it is through innovation that you achieve a competitive advantage. © 2012 GS 1 7

How can you improve Data Quality? Best Practices – DQMS Key Components With Best Practices, you don’t have to reinvent the wheel • Data Quality Management System (DQMS) 1. 2. 3. 4. 5. 6. © 2012 GS 1 DQ Strategy DQ Policies DQ Procedures DQ Standards DQ Metrics DQ Organization 8

How can you improve Data Quality? DQ Strategy • Where is your business now? • How does it operate internally • What drives its profitability • How it compares with competitors • Where does it need to be? (top-level objectives) • What will be the focus of the business and its source of competitive advantage? • Define vision, mission, objectives, values, techniques and goals • Where will the business be in five or ten years? • What is needed to do to get there? • • What changes are needed to deliver on the strategic objectives? What is the best way to implement those changes? What changes are required to the structure are required of the business What are the goals and deadlines? • What are your guiding principles? © 2012 GS 1 9

How can you improve Data Quality? Policy - examples • Information Standards Policy – All enterprise information shall have defined standards. – Defined information standards will be published for all employees to easily review and reference. – Each information standard shall be unique in the way it is defined and consistently applied across the enterprise. The type of enterprise information requiring defined standards shall be: Master Data, Transactional Data, Analytical Data, or Web Content. • Information Ownership Policy – Each field of enterprise information will be assigned to an information owner from a business functional group. The information owner will be ultimately accountable for the accuracy, integrity, auditability, consistency, completeness and security of this business critical information as it relates to their business group or function. • Information Integrity Policy – The collection, import, modification, manipulation, extraction, retention, archiving, retirement, purge, and /or deletion of enterprise information shall be governed. • Information Security Policy – All enterprise information shall be protected from unauthorized access, review, publication, and modification. • Information Technology Policy – All requests for new enterprise information or data quality applications shall be evaluated, recommended, and approved prior to purchasing. © 2012 GS 1 10

How can you improve Data Quality? Procedures & SOPs • Information Ownership Policy • Define and Maintain Information Standards • Information Governance Conflict Resolution, Escalation and Non. Compliance • Information Governance Communication • Information Integrity Policy • • • © 2012 GS 1 Define and Maintain Information Standards Define and Maintain Information Quality Metrics Monitor Information Quality Metrics Information Governance Conflict Resolution, Escalation and Non. Compliance Request Add Edit Customer Info Mass Add Edit Activities Identify or Request Add Edit Review Approve Perform and Publish MDM Review Approve Perform and Publish 11

How can you improve Data Quality? Standards • Master Data • • • Product/Material Vendor Customer Employee Financial/Chart of Accounts (COA) • Transactional • Analytical/BI • Portal & Content © 2012 GS 1 12

How can you improve Data Quality? Metrics – Business Critical Attributes - Core • Based on Business Critical processes* O 2 C P 2 P POS • Critical Attributes - i. e. Inputs & Outputs Master Data • Master GTIN, Nutritional, Weights, Packaging (LWH) • Transactional Price, Promotion, PO, Invoice, etc. • Processes dependent on DQ (E 2 E business processes) • Processes which impact DQ (internal to data owner) – create, add, & maintenance • Information required downstream • Needed to make a purchasing decision - i. e. Nutrition • These will vacillate based on consumer demand interest * Attributes critical for the execution of that process become “core” © 2012 GS 1 13

How can you improve Data Quality? Metrics – Key Concepts • • • © 2012 GS 1 Based on Business Critical processes Standards, Dimensions, Conditions KPIs are defined from “core” attributes Reporting Auditing Lifecycle 14

How can you improve Data Quality? Organize for Data Quality • • © 2012 GS 1 Enterprise Structure DQ Operational Model DQ Roles Responsibilities/RACI 15

Data Quality Framework: An Introduction © 2012 GS 1 16

Data Quality Framework Background GS 1 created the Data Quality Steering Committee (DQSC) as the group responsible to manage and maintain the DQF. April 2005, the GCI Joint Business Planning Data Accuracy Task Force developed recommendations, the Data Quality Framework (DQF) 2006 - DQF governance transitioned to GS 1 © 2012 GS 1 2008 DQF updated 2007 – collaborated with AIM & Cap. Gemini to develop self assessment module and KPI Model to gauge compliance with DQF. 2008 DQ Challenge launched pilot program with 10 mfr & 6 retailers 17

Data Quality Framework Guiding Principles • Real World: • Created by industry for industry, based on user needs • Flexible: • Adaptable to the needs and requirements of specific trading partner relationships. • Customer requirements are fluid and changing, supports the need to be agile and responsive • Scalable: • May be included in part or whole in any kind of data quality management system. Not only for large organizations • Prudent: • Minimise implementation costs, enabling immediate benefits • Complementary: • to GS 1 System standards • Voluntary: • Strongly encouraged, yet not mandatory © 2012 GS 1

Data Quality Framework Benefits The Data Quality Framework is publicly available to industry and is offered by GS 1: • As a collection of best practices • As a “compass” that points organisations to their most significant internal opportunities • As a guide to help implement internal processes for data quality • A common set of principles from which trading partners can build, enhance, and expand their collaboration © 2012 GS 1

Data Quality Framework What is it? What does it do? ü Collection of desirable best practices ü Adapts to each organisation’s particular set of circumstances ü Helps establish metrics ü Supports the implementation of GS 1 Standards ü Aids in the organisation of action plans ü Provides tools for organisations to run their own self assessments ü Offers an objective reference © 2012 GS 1 ü

Data Quality Framework It is not… û An ultimate cure for all data quality needs û A step-by-step manual to û define processes, or û prescribe a detailed implementation plan û û û © 2012 GS 1 A document specific to one industry A certification or external audit A guarantee for trading partners An obligation A one-time solution A short-term project û

Data Quality Framework In a Nutshell • Developed and endorsed by the Industry • A broad guideline of best practices and desirable capabilities to address the improvement of data quality. • The DQF includes: • • © 2012 GS 1 Key elements of Data Quality Management System Model for a self-assessment Model for a product inspection & KPIs Recommendations for Monitoring & Continuous Improvement

Data Quality Framework Key Components • Data Quality Management System (DQMS): • A list of recommended best practices to ensure internal data management and data management systems produce a high data quality output • Recommendations expressed as desirable capabilities that add value to organisations • Self-assessment • Best practices to conduct a self-administered assessment that compares the organisation to the recommended best practices of the DQMS • Questionnaire to assess conformity to DQMS requirements • Product inspection procedure • A procedure for the physical inspection of products and data which may be used on its own or as part of data quality audits • KPI Model to validate actual accuracy of the data • Recommendations for monitoring & continuous improvement • Model for Implementation (picture and Link) © 2012 GS 1

Data Quality Framework How it Applies • The DQF is not meant to be used in its full entirety • It’s greatest strength is that specific parts can be used independently • Each part of the Framework may be implemented separately, if and when required by organisational conditions. • To maximize results, it needs to be used within a clearly defined scope and objectives. • The DQF is part of an ongoing process and each organisation must adapt it to their unique situation for the best application of the Framework. © 2012 GS 1

Data Quality Framework An approach - the Basic Cycle • Assessment of capabilities and priorities • Data Quality strategy • Start a cycle of continuous improvement © 2012 GS 1 Define goals and objectives Identify areas of opportunity Monitor changes and plan further improvements Implement improvements • Execute, document and take action

How do you Improve Data Quality? Summary - Elevator Pitch • Incorporate the key elements of DQ • Because inaccurate, unreliable data is costing you and your trading partners money • Without good, accurate data, Global Data Synchronisation enable the rapid, seamless transfer of bad data • Apply the Data Quality Framework: • Managed by GS 1 • An industry-developed collection of best practices to establish sustainable data quality. • It includes: – Recommendations to establish management processes to improve the quality of the data, – Self-assessment tools to find improvement opportunities. – Model inspection procedures and KPIs for product audits – Recommendations for monitoring & continuous improvement • The DQF is flexible and scalable, it: • Is not just for big organizations, and • Must be adapted to the unique requirements of each organisation © 2012 GS 1