Name of Session: The Data Quality Journey

GS1 Standards Spring Event 18-22 March 2013 – Dallas Building Standards to Deliver Business Value
Name of Session: The Data Quality Journey MO DQ Services & Best Practices Time of Session: Monday 9:00 – 10:00 Who May Attend: Everyone Speaker names: Mark Widman, GS1 Global Office Sjoerd Schaper, GS1 Netherlands Carlos Ramos Aguilar, GS1 Mexico Robert Besford, GS1 UK

Introductions and the Data Quality Journey
Mark Widman, Director of Data Quality GS1 Global Office Sjoerd Schaper, Manager GS1 Data Pool GS1 Netherlands Carlos Ramos Aguilar, Innovation Manager GS1 Mexico 2020 Robert Besford, Solutions Development GS1 UK 2014 2013 2012 2010 Simplified DQF Light B2C Attributes New DQ KPI’s 2010 2011 2010 Inventory of MO DQ Programs 3rd Party DQ Solution Providers Data Crunch Reports MO DQ Tool Kit DQF 3.0 Implementation Guide DQF Started Inventory of DQ Programs DQF 1.0 DQF 2.0

GS1 Data Quality Foundation - Documentation and Tools
DQF 3.0 AAFES Bissell Homecare Campbell Soup ConAgra Foods Georgia Pacific Gladson Helen of Troy Kimberly Clark METRO Group Nestle PepsiCo Procter & Gamble Royal Ahold Sara Lee SCA Tesco The J.M. Smucker Unilever Wegmans Foods Capgemini GMA GS1 Australia GS1 Columbia GS1 Global Office GS1 India GS1 Italy GS1 Germany GS1 Mexico GS1 Netherlands GS1 South Africa GS1 Sweden GS1 UK Implementation Guide 3M AAFES ConAgra Foods Helen of Troy METRO Group Nestle PepsiCo Procter & Gamble Royal Ahold SCA The J.M. Smucker Unilever Wegmans Foods GS1 Australia GS1 Columbia GS1 Global Office GS1 India GS1 Italy GS1 Korea GS1 Germany GS1 Mexico GS1 Netherlands GS1 South Africa GS1 Sweden GS1 UK Inventory Report GS1 Australia GS1 Brazil GS1 Canada GS1 China GS1 Croatia GS1 Columbia GS1 France GS1 Germany GS1 Global Office GS1 Hong Kong GS1 Hungry GS1 India GS1 Italy GS1 Japan GS1 Korea GS1 Mexico GS1 Netherlands GS1 New Zealand GS1 Poland GS1 Russia GS1 South Africa GS1 Spain GS1 Sweden GS1 UK GS1 US GS1 Data Quality Framework 3.0 (DQMS, Self Assessment, KPIs, Product Inspection Procedures) Implementation Guide for Data Quality Framework (how-to) GDSN & DQ MO Tool Kit (MO best practices) GS1 Inventory of Data Quality Program Report (MO survey & Key Takeaways) GDSN GTIN Allocation and Packaging Rules 3rd Party DQ Solution Providers 2020 2014 2013 2012 2010 Simplified DQF Light B2C Attributes New DQ KPI’s 2010 2011 MO Tool Kit GS1 Australia GS1 Canada GS1 Columbia GS1 Global Office GS1 France GS1 India GS1 Sweden GS1 UK GS1 US 2010 Inventory of MO DQ Programs 3rd Party DQ Solution Providers Data Crunch Reports MO DQ Tool Kit DQF 3.0 Implementation Guide DQF Started Inventory of DQ Programs DQF 1.0 DQF 2.0

Data Quality Framework 3.0
Adaptable and Customizable Framework Data Quality Management System Organization Capabilities Standards and Policies Business Processes System Capabilities Self Assessment Questionnaire Scoring Model KPI’s and Product Inspection Procedures Information for Product Inspectors Sampling Pre-Inspection documentation requirements Inspection Report requirements Guidelines of KPIs targets for the industry Master Data Quality KPIs List of GSDN attributes for product inspections Plan Document Execute Monitor

Implementation Guide for the Data Quality Framework 3.0
Companion guide for the Data Quality Framework Essential Steps for the Implementation of a Data Quality Management System How to Conduct a Self-Assessment Product Inspections and Data Accuracy KPIs Ongoing Data Maintenance 3M AAFES ConAgra Foods Helen of Troy METRO Group Nestle PepsiCo Procter & Gamble Royal Ahold SCA The J.M. Smucker Unilever Wegmans Foods GS1 Australia GS1 Columbia GS1 Global Office GS1 India GS1 Italy GS1 Korea GS1 Germany GS1 Mexico GS1 Netherlands GS1 South Africa GS1 Sweden GS1 UK

Implementation Guide for the Data Quality Framework

GDSN & DQ MO Tool Kit Recommendation of Implementing Best Practices
Step 1: Engage the Community Step 2: Develop the Business Case Step 3: Ensure MDM program is in Place Step 4: Show how to solve the Business Problem Step 5: Enable Customers internal Systems for Compliance Step 6: Measure Market Participation and Develop Extended Strategy

GS1 MO Inventory of Data Quality Programs (Survey)
Category 1: Training & Education Executive Overview Grounding for Data Quality GS1 MO Inventory of DQ Programmes – Structure GS1 MO Inventory of DQ Programmes – Results Data Quality Framework GS1 MO Inventory of DQ Programmes – Key Takeaways Survey Recommendations GS1 MO Inventory Category 2: Data Quality Awareness & Communication Category 3: Community Management Category 4: Product Inspections Category 5: Validations & Integrity Checks Category 6: Accreditation & Authentication Category 7: On-Boarding Tools Started 2010 Published 2012 Category 8: Consulting

Inventory of DQF Key-Takeaways (as of Feb 2012) There are a substantial number of Data Quality tools & services in use today by MOs with an overall utilization rate of 59.8% Data quality is expected by almost all participating MOs to grow in importance over the next three to five years. MO service offerings show a strong correlation to local country market readiness, priority given to Data Quality, Data Pool linkage and GDSN adoption levels. The greater the community engagement, the greater the programme offering. The categories with the highest level of programme offerings are Data Quality Awareness & Communication (87.0%), Training & Education (69.6%) and Community Management (73.9%). This reflects MO commitment to ensuring their communities are involved and informed. There is strong MO support of the Data Qualify Framework (DQF) concepts as shown by the high levels of service offerings for Data Quality Self-assessments, Physical Audits and Audits Scorecards. However, 18% of the surveyed MOs still recommended that GS1 GO increase communications and training tools for Data Quality. In general, MOs promote the Data Quality Management System (DQMS) component of DQF but leave the consulting/implementation of technical "back end system" infrastructures to the to the plethora of 3rd Party Solution Partners that offer Master Data Management (MDM) related solutions. The focus of Data Quality needs to shift from basic DQ elements ("Awareness, "Training & Education", "Community Management") to driving better Data Quality Management System (DQMS) processes by Data Owners to make true progress. This is the nucleus of DQ problems. Local Data Quality Programmes serve local needs with virtually no interoperability between MO programmes. 36% of the MOs expressed GS1 GO help to facilitate knowledge exchange between MOs.

Inventory of DQF Key-Takeaways (Continued) There is no standard way to analyse and compare Data Quality because it is measured differently across GS1 MOs as well as Member Companies. It was recommended by 14% of the MOs that GS1 GO help the community develop standardized KPI's for Data Quality so progress can be measured consistently. Growth of GDS in existing and/or to new sectors was stated as an objective by 56.5% of the surveyed MOs. Many MOs seeking to increase GDS adoption will continue this emphasis and will shift resources/focus to Data Quality only after GDS usage stabilizes. The community needs to understand the importance of implementing GDS and Data Quality services simultaneously 43% of the surveyed MOs reflect Data Validation checks for local country requirements. An exploratory of these localize validations may reveal synergies between the MOs, creating an opportunity to globalize the requirements though the GDSN. It is assumed that MOs not offering or reflecting Validation & Integrity Checks do not have local country validations and they rely on Data Pools to enforce the GDSN global validation requirements. B2C Interest and B2C Involvement are sighted by 39.1% of the surveyed MOs as priorities. MOs expressing only B2C interest recognized the importance of B2C but will remain neutral on programme or service offerings for the next few years. These MOs also say they will actively follow Industry developments. Many MOs state they will align themselves to be heavily involved in B2C developments in order to prepare for the future. A few GS1 MOs say programmes or services are already being established or planned to support B2C.

3Rd Party DQ Solution Providers

KPI Attributes based on Business Context
Purchasing New Product Introductions Store Operations Replenishment Transportation & Logistics Warehouse and DC management Inventory & Distribution Plan-o-graming POS Consumer Information (B2C) Product Recall Invoicing Advertising Trade Promotion Management Routing Returns & Redemption & Claims eCommerse (catalog) ASN Track and Trace Demand Forecasting Item Master Alignment Payment Price and promotion Sales tax handling and submission Plan-o-gram Product Recall POS WMS / Inventory Core KPI Attributes Weights Measures Merchandising Item Management Consumer Info B2C Planning & Optimization Completeness and Accuracy

GS1 Data Quality Foundation – 2013
Category 1: Training & Education Category 5: Validations & Integrity Checks Category 2: Data Quality Awareness & Communication Category 6: Accreditation & Authentication Category 3: Community Management Category 7: On-Boarding Tools Category 4: Product Inspections Category 8: Consulting 2020 2014 2013 2012 2010 Simplified DQF Light *B2C Attributes New DQ KPI’s 2010 2011 2010 Inventory of MO DQ Programs 3rd Party DQ Solution Providers Data Crunch Reports MO DQ Tool Kit DQF 3.0 Implementation Guide DQF Started Inventory of DQ Programs DQF 1.0 DQF 2.0

Anti-Trust Caution GS1 and the GSMP operate under the GS1 anti-trust caution. Strict compliance with anti-trust laws is and always has been the policy of GS1. The best way to avoid problems is to remember that the purpose of the committee is to enhance the ability of all industry members to compete more efficiently. This means: There shall be no discussion of prices, allocation of customers, or products, etc. If any participant believes the group is drifting towards an impermissible discussion, the topic shall be tabled until the opinion of counsel can be obtained. The full anti-trust caution is available in the Community Room if you would like to read it in its entirety.

GS1 Netherlands Sjoerd Schaper - Manager, GS1 DataPool

Status GS1 DAS data pool > 900 suppliers 14 retailers
GTIN’s registered for the Dutch market GS1 DAS is based on the former 1SYNC-datapool Current DQ program started in 2011 Previous programs didn’t work

What is a Deltaplan? 1 Feb 1953: huge flooding

Data Quality Data quality in GS1 DAS was too low
Retailers couldn’t rely on the data Usage was not on the expected level A Deltaplan Data Quality was needed!

Program Data Quality Strategy and role GS1
correct& secure services awareness communication insight monitor and report

GS1 DAS Data Quality Checker
The GS1 DAS Data Quality Checker: A tool for continuous monitoring of DQ (daily basis) Report of logical validations and physical measurements Supplier gets a login to see his KPI’s and the errors on record level Powered by Clavis Technology

Dashboard Logical scores

Dashboard Physical measurements

Certification of new suppliers
New companies directly start using the DQ checker Requirements before they go live: a 100% score on logical checks a sample of 5-20 items

Physical measurements
In 2012 more than measurements at Retailer Distribution Centers > 8000 consumer units > 4000 cases Outsourced to special company Results lower than logical checks (on average 75%) Switched dimensions Outside tolerances ......

Proces Physical measurements
Supplier A B C Retailer X Y Z Assortment GLN’s & GTIN’s Sample 1 10% OK satisfies requirement Sample 2 Mandatory measurement of all items NOK satisfies requirement

Retailer involvement is essential
Weekly KPI-sheet to retail contacts Retailer dashboard in DQ checker Retailers asked to take action for non-improving suppliers Some are sending letters Some are giving fines to the supplier DQ must be part of the commercial negotiation

The future Much improvement, but the goals for 2012 were not reached
To be continued in 2013 DQ is not a separate project or action but should be part of the daily business

GS1 UK Robert Besford

About us

Our members Sole traders, to SMEs to multi-nationals Over 27,000
We now have over 27,000 members who come from a range of industries including food service, retail, manufacturing and health. This number grows every year – a large proportion of our new members trade online through sites like Amazon, Play and iTunes. From retail, food service, healthcare and online industries

Membership breakdown by turnover
64% of membership has an annual turnover of less than £1m 22/08/2012 34

Our data quality services

PRODUCT FLOW INFORMATION FLOW GS1 in the Supply Chain Right First Time
SUPPLIER HEAD OFFICE RETAILER FACTORY WAREHOUSE CONSOLIDATION RETAILER RDC STORE Right First Time So why was the data match so poor…………………………….. The reasons for much of the inconsistency and inaccuracy of product data held by grocery retailers can be found within supply chain processes. Different functions have different information needs. In the absence of an accurate and standardised source of data, each department has created its own local repository of information. The following chart identifies key areas where these separate islands of information exist, and highlights the key effects they have on the operational efficiency of the business. Each function managing data issues : increased overheads + systems = increased costs This process works today but not sustainable for the future INFORMATION FLOW

Professional service support

Example: GS1 UK Support Overview
MASTER DATA MANAGEMENT STRATEGY STRATEGIC ALIGNMENT - BUSINESS VALUE ARTICULATION – ROI – BUSINESS CASE Discovery Implement Rollout Business Understanding Business process analysis (as-is) – issues, impacts and root causes IT review and impact on business Data model review Business case Data quality assessment MDM Roadmap Solution Options and Definition Business processes definition and agreement (to-be) To-be data model Data governance strategy Process re-engineering and optimisation Software selection process: GDS data pool; MDM solutions ;DQ solutions Implementation Support Set-up data pool Requirements documentation : supplier handbook Pilot management On-board suppliers Internal data management controls and measures (KPI’s) Internal education Support with other solution implementations : MDM and DQ

Discovery : Business Case For Change

Implement : Plan, Build and Adopt

Member training services
GS1 Identification Keys Identifying products, cases and pallets correctly The rules around when and why GTINs (bar code numbers) need to change Including extra information (dates, batch numbers, etc.) Coupons Variable weight items Bar Codes The different bar code symbols and their applications Bar code size, colour and placement best practice Bar code quality and verification Automated goods receipt Pallet labels SSCCs Basics of EDI Order to Cash process Data Quality The cost of bad data Global Data Synchronisation Package measurement rules – how to measure products and cases correctly Product description best practice

Data monitoring services

Data audits Understand the scale of the Data Quality issues and its potential impact on the business Provide insight so that the customer can begin to build a plan to mitigate the key issues identified We use a TrueSource (Data Pool) and third party data quality tools to conduct the analysis Typically brand owners data is compared to that held by the retailer Comparison of product data sets does not indicate which version is correct Product data checks undertaken simply identify where there are differences between the brand owner and retailer data sets Product data held in TrueSource has not been completed by the brand owners specifically for Waitrose requirements Physical checks are ultimately required to determine the accuracy of product data Data matching exercises typically start with matching all active Stock Keeping Units (SKUs) between the brand owner and retailer prior to analysis

Data monitoring capabilities
On-boarding projects Visibility of progress of each supplier in setup of back-catalogue Manage the timely loading of supplier back-catalogue Ensure data meets all retailer requirements Ensure high quality data in data pool Detection/prevention of low quality data in the data pool Reporting on suspect/low quality data Supplier scoreboards, rankings and trends Monitor issues caused by changes to retailer requirements or industry standards Apply data quality checks not possible in data pool / data capture interface

Monitor supplier progress in setup of back catalogue
Expected number of GTINs per supplier Number of GTINs started in TrueSource Number of GTINs meeting retailer requirements

Provide Detailed Reports For Each Supplier
List of GTINs expected but not started List of errors per GTIN Summary of errors per category

Food Service Validation Service
Example… 196 Data Values per gtin reviewed Core Item Food & Beverage Extension 83 Mandatory/Dependency rules 57 Core attributes 10 F&B attributes 14 allergens 7 Nutrients Standard GS1 Validity Tests for attributes 70+ Supplier Reports

Solutions for individual suppliers & Retailers
Monitor data against multiple retailer requirements Custom reports (by brand, GLN, product category) Check for “suspect data” Retailers Monitor back-catalogue status Monitor ongoing data quality across all suppliers

Onsite physical inspections – GS1 UK Product Inspector App
Product Inspector is a native, stand-alone application that operates in both a networked and offline environment. The App has an embedded file management and communications system that allows the user to import product inspection sets in a variety of formats, including Microsoft Excel, from either a server or as attachments. This data can then be consumed by a Product Information Management (PIM) system or Business Intelligence (BI) System to initiate workflow activities or generate GS1 Compliant Data Quality reports following the principals defined in the GS1 Data Quality Framework.

GS1 Mexico Carlos Ramos Aguilar

Background on DS 1999 - First Data Sinchronisation (DS) iniciative
data pool, owned and developed by GS1 Mexico 2002 – First massive adoption effort 2005 – Creation & adoption of the Measurement Service (inspection) in Mexico, fully integrated with the local data synchronization process a new strategy needed, in-depth study of DS activity and expectatives from the whole Industry, regarding the interest of adopting a global model of GDS 2008 – launch of GS1 Mexico DP (Syncfonia)

2008-2011, implementation strategy
8 major retailers on-board Comercial Mexicana, Chedraui, Corvi (Wholesaler), Diconsa, HEB, Soriana, Super Kompras, WalMart +75% of the retail market Initial categories Grocery (food & non-food) Health and beauty Beverages Initial focus New item process

Data Quality Framework
Our Services Membership Data Validation Data & Photo Capture Data Quality Framework Knowledge Center Training + Add value to the membership

The 60% of the products tested, do not pass at the first validation
Data VALIDATION The verification is done on 22 fields. The main rejections are in: The 60% of the products tested, do not pass at the first validation Variant 22% Gross Weight 17% Functional Name 12% Width 11% Depth 10% Height 7% Net Content Others 14% Involves taking a physical sample of the product that was previously loaded into SYNCFONIA, where our staff compares the data loaded against the product information. If the checking is approved, one data quality flag is enabled in the data pool, which lets the subscriber know that the information is reliable.

Data & PHOTO CAPTURE Over 1,285 companies have selected GS1 Mexico service, to capture more than 155,500 items Launched on 2008, companies are able to have all their items not just published at their Data Pool by GS1 Mexico but inspected as well. A significant element added recently within this process are the 9 pictures from every product, taking into account the planograms specifications and 3 shots for marketing purposes. No cost in addition to annual fees. 31, 355 gtins with photo at the end of 2012

training Data Quality Training Program: a 4 modules 4hrs training program where companies from all sizes are able to learn & experience about GS1 Data Quality Standards (GDSN, GS1 ID, GS1 Measurement Rules), getting ready for synchronizing master data through GDSN. Classroom: companies get the basics of GS1 System in order to understand the importance and relevance of the processes impacted by data quality; Measurement Service Workshop: a hands – on area where companies are provided with calibers and scales in order to measure their products according to GS1 guidelines. Some companies haven´t got the equipment to do this in their internal locations, so they can come to GS1 and make use of it (no extra fee). IT Room: the participants register & publish their trade items into their data pool. Data Quality Experience Room whereby simulating the processes involved within the supply chain, they realize of the impact that such good and bad data quality produce.

Data quality framework
Data Quality Framework (compliance): based on large companies needs, this service is composed by a whole training program and allows companies to show compliance against Data Quality Framework, by providing with a self assessment report (up to 80% of the score). GS1 Mexico, defines a sample (taking into account the whole amount of items from that company). If the whole inspection goes all right, there´s a report provided to the company that enables them to prove consistency within their processes, if not they are expected to go for Measurement Service Inspection item per item. Key figures: Working on the overall first global pilot with P&G Learning for Unilever

Services for Retailers
Professional Services Project Management Leader Internal development support for connectivity between catalogs Support in supplier integration Advice to businesses Data capture at Store and Distribution Center Measurement Service Data Capture Photo Capture Education and Training Training the team on concepts Bar Code and Rules of Measurement Training companies in the Data Quality Model Our strategy is based on developing new services that increase the value of membership and support future projects

Numbers 2,640 Suppliers 232,490 GTIN’s 53% Data Quality
4,361 products with photo 2011 (Groceries, Beverages, Health & Beauty) 3,245 Suppliers 317,468 GTIN’s 66% Data Quality 32,897 products with photo 2012 (Hardware, Toys, Apparel, Pharmacy, Electronics) In 2011 the major supermarket chains in México requested that all new product data; must be loaded in Syncfonía and published to them with data quality. In 2012 the scope was extended to new categories, and those categories that were integrated should keep updating the data. During 2012 Soriana (the second retailer in Mexico) requested support of GS1 to ensure the quality of data in their SAP migration project

Barriers and learnings
The companies have separated processes in generating product data. Data quality is affected by the way data is entered, stored and managed. Data quality is not one-time effort, it requires frequent maintenance. In the information input process should be a model that ensures data quality. The systems do not guarantee the accuracy of the data. The information is basic to improve in business, operational, logistical and financial processes. Data quality minimizes errors, reduces risk and add a value in time along the supply chain. Bad information is a “Domino Effect” If the original data are not accurate, there is a chain reaction where the information is carried along the supply chain, producing large negative impacts and costs. Bad data has severe cost impact on the industry, that is why data quality is crucial to operational and transactional processes. Items per package do not match with the information of the product catalog The invoice does not match with the information of the delivery order The case dimensions do not match The purchase order is wrong, this product is no longer in the inventory I can not find the product with this barcode in the warehouse With these descriptions I can not find what product is The product does not fit on the shelf

“Confidence and efficiency in the exchange of information with trading partners”
CERVECERIA CUAUTHEMOC MOCTEZUMA “Processes are simplified because the information is stored in a centralized repository, where data are taken for other processes or business documents” WAL-MART MEXICO Y CENTROAMERICA “The information is in the same language (standards) and allows easy distribution and understanding” KRAFT FOODS / MONDELEZ INTERNATIONAL “Quick cataloging process. Only with entering a barcode can be extracted product information at different levels” CHEDRAUI Not easy to work with the operation of stores and distribution centers, the retailers do not know how many products in total are physically on the shelf vs. logical stock, we had to be proactive to increase the number of companies and products with data quality hardly dedicate resources companies and corporations do not have a comprehensive process of data quality, but also the adoption of the validation service has been slow and very questionable because validate product by product and providers are not ready.

Next steps 2013-2015 1) New categories 2) New retailers & wholesalers
3) New attributtes (nutritional, additional file information, etc) 4) Image pool (under analysis) 5) New process Strengthen and give sustainability to continuous data quality Expand the use of Data Synchronization, with new fields & new users We are still in the process, some providers insist that they know how to measure their products and brands do not pay attention to all the details, but we are convinced that we will establish the platform for short term projects focused on the end consumer New categories 2) New retailers & wholesalers 3) New attributtes (nutritional, additional file information, etc) 4) Image pool (under analysis) 5) New process Mark for “delete” = no replenishment Change and update product data

Meeting Etiquette Meetings will begin promptly at designated start times Avoid distracting behavior: Place all mobile devices on silent mode Avoid cell phones Avoid sidebar conversations Speak in turn and be respectful of others Be collaborative in support of the meeting objectives

Name of Session: The Data Quality Journey

Similar presentations

Presentation on theme: "Name of Session: The Data Quality Journey"— Presentation transcript:

Similar presentations

About project

Feedback

Log in

Auth with social network:

Name of Session: The Data Quality Journey

Similar presentations

Presentation on theme: "Name of Session: The Data Quality Journey"— Presentation transcript:

Similar presentations

About project

Feedback