why your business needs accurate product data

1

Why Your Business Needs Accurate Product Data

2

Our Presenters

Kerry YoungVP & General Manager

Jeff CowanDirector, Community Engagement

3

Introducing EnterWorks

Empowering Business & IT Users for over 21 years

All-in-one platform for MDM, PIM, and DAM

Only Solution that is a Leader on both the Forrester PIM and MDM Waves

Highest customer satisfaction marks on industry analyst reports

Industry expertise: distributors, manufacturers, retail, hospitality, service companies, and member / buying groups

Strong Global SI & Technology partnerships

Business MissionEnable Our Customer’s Growth,

Efficiency and Differentiation through Exceptional Competency in Data

as Enabled by our MDM Technologies

Complexity MasteredMaster Shared and

Application Data for Business Model Agility

Discrete Viewsof Everything

Provide a Central View of Data Across Enterprise

Networks

Differentiated Experiences

Leverage Multiple Domains for Combinatorial Precision

4

A Division of A Division of

51%LICENSES

OUTSIDE U.S.

2,400+GLOBAL

CUSTOMERS

75+COUNTRIES

200+NEW ACCOUNTS

ANNUALLY

22OF SAP’S TOP 25

CUSTOMERS

48NET PROMOTER

SCORE

Auto Apparel Oil & GasFood &

BeverageConsumerProducts Retail Pharma Chemical Entertainment

EnterWorks Acquisition, Inc. Proprietary and Confidential4

© 2020 GS1 US All Rights Reserved

Why Your Business Needs Accurate Product Data

Jeff Cowan - GS1 US

June 18, 2020


Antitrust Caution

GS1 US is committed to complying fully with antitrust laws.

We ask and expect everyone to refrain from discussing prices, margins, discounts, suppliers, the timing of price changes, marketing or product plans, or other competitively sensitive topics.

If anyone has concerns about the propriety of a discussion, please inform a GS1 US representative as soon as possible.

Please remember to make your own business decisions and that all GS1 Standards are voluntary and not mandatory.

Please review the complete GS1 US antitrust policy at: http://www.gs1us.org/gs1-us-antitrust-compliance-policy

6

http://www.gs1us.org/gs1-us-antitrust-compliance-policy


Agenda

• GS1 US Overview• Data Quality Background• GS1 US National Data Quality Program• Data Governance Guidance• Questions

7


GS1 US Overview

8


• We create a common foundation for business by uniquely identifying, accurately capturing, and automatically sharing vital information about products, locations, and assets.

• We enable visibility through the exchange of authentic data.

• We empower business to grow and to improve efficiency, safety, security, and sustainability.

At GS1, we believe in the power of standards to transform the way we work and live.

Our Purpose

9


Our Unique Role

10

• Neutral and not-for-profit

• User-driven and governed

• Global and local

• Inclusive and collaborative

GS1 is…We bring communities together.


The Global Language of Business

114+ Member Organizations Serving Business Around the World

11


GS1 by the Numbers

12

1.5 million companies around the world use GS1 Standards.

More than 6 billion GS1 barcodes are scanned every day.

25 million products are assigned U.P.C.s in the GS1 US Data Hub® | Product tool.

More than 30 million products are registered by brand owners in the GS1 Global Data Synchronization Network™ (GDSN®).


GS1 Standards

13


A Case for Change

Data Quality Background

14


What is Data Quality?

Data Quality is having:• Consistent• Complete• Accurate• Standards-based• Time-stamped data

And, most importantly, data quality is a shared responsibility of trading partners.

15

Measurement of Data Quality

Most recently shared datamatches the physical data

Electronic Data

Physical Data


Information Accuracy & Data Quality

16


Benefits of Item & Case Data Accuracy

Shopper Benefits:1. Comprehensive and seamless shopping experience2. Accurate price/product compare to shelf tag3. Improved on-shelf availability4. Improved consumer brand confidence5. Improved consumer retailer loyalty

Cost Savings:1. Improved trailer optimization2. Warehouse storage efficiencies3. Elimination of inaccurate weight and

measure (penalties and fines)4. Proper product identification

Enhanced Collaboration:1. Improved trading partner relationships2. Expedited new item setup3. Greater plan-o-gram accuracy4. Improved U.P.C. transition

Improved Retail Execution:1. Accurate order delivery2. Reduction of out-of-stocks3. Improved speed to shelf execution4. More effective promotion execution5. Integrated digital experience

Risk Mitigation/Cost Avoidance:1.Digital order matches physical products2.Consumer is able to see accurate product

nutrition, ingredients and allergen information at time of order

3.Product transparency

17


The Rising Risks of Poor Data Quality…

...and Its Impact on Your Entire Business18



19


The Effect of a ¼ Inch on Transportation Costs

Scenario 1 (Existing data)

Scenario 2 (Measured data)

11.60 X 7.68 X 5.25 11.60 X 7.68 X 5.0

20 X 9 20 X 10

180 cases 200 cases

9,000 cases 10,000 cases

Case Dimensions

Cases per full truck (50 pallets / 25 double stacked)

Cases per pallet

Pallet Pattern

Case height is actually ¼ inch

shorter

Extra layer able to be added to

the pallet

20 extra cases per pallet

XYZ Widget Company

20


The Effect of a ¼ Inch on Transportation Costs

Now, let’s say that XYZ sold 500,000 cases of Widgets last year…

Scenario 1 (Existing data)

Scenario 2 (Measured data)

9,000 cases 10,000 cases

56 50

$3,000 $3,000

$168,000 $150,000

Cases per Truck

Total Cost

Cost per Truck

# Trucks

1,000 more cases per truck

6 less trucks

$18,000 annual savings

That would be $108,000 in potential savings!

Let’s say that XYZ Company has 200 SKUs, and just 3% of them have the case height overstated in a similar fashion:

21



22


GS1 US National Data Quality Program

23


Data Quality Structure

24


GS1 US National Data Quality Program

Data Governance Process: • A strong data governance process is essential to strong master data management -

leading to good quality data.

• Within the GS1 US National Data Quality Program, an organizations master data management and data governance process is assessed to determine the degree which people processes and procedures are in place to ensure quality data is maintained.

• The Data Governance Assessment asks probing questions regarding:

25

• Data Governance• Data Quality• GTIN Management• Product Management

• Attribute Accuracy• Data Synchronization• Training


Education & Training Protocol

Education & Training• Within the GS1 US National Data Quality Program, an organization’s Education and

Training Protocol is examined to:- Determine if those responsible for data quality have been trained- Assess the method(s) by which they are kept current- Assess the knowledge transfer process as people transition roles

• The assessment is comprised of three quizzes- GTIN Management Standard- GS1 Package Measurement Rules- GDSN – if applicable

26


Attribute Audit

Attribute Audit

• The ultimate proof of an organization’s capacity to produce and maintain good data lies within the product information itself.

• Within the GS1 US National Data Quality Program, the Attribute Audit assesses certain key product attributes to verify that the attribute information being shared matches the physical product.

27


Data Quality Attributes

Foundational

• Linear Dimensions (height, width, depth) *• Gross Weight /UoM*• Country of Origin• Ti-Hi

A change to any of these attributes in the product pre-production stage does not require adherence to the GS1 GTIN Management Standard. Once in production the rules will need to be adhered to.

*Overall Accuracy: if any of the linear dimensions or gross weight is out of tolerance – the item/case is considered inaccurate.

Fundamental

• Brand Name• Declared Net Content/UoM• Pack Quantity• GTIN

Once the GTIN is shared with a trading partner – a change to any of these attributes, independent of which stage in the product development cycle (pre-production or production) will need to adhere to the GS1 GTIN Management Standard.

28


Data Governance Guidance

29


Based Upon 5-point best practices

1. Adhere to GS1 Standards and Rules for foundational attributes in internal setup

2. Assign data owners throughout the organization

3. Appoint one entity/department/individual as the sole owner of product data

4. Audit items produced in a sustainable production environment ready for shipment (finished goods)

5. Execute communication on attributes, both internally and externally

30


Five Point Best Practice - #1

31

• Brand Name• Declared Net Content/Unit of Measure• Pack Quantity• GTINA new GTIN must be assigned at all applicable levels of change, following the GTIN Management Standard.

1. Adhere to GS1 Standards and Rules for Foundational Attributes in internal setup:During innovation of a new

item, key foundational item attributes should be tagged as “foundational”.

These attributes should not be considered preliminary once shared and need to adhere to the GTIN Management Standard during the innovation process.


GTIN Management Best Practices

• Develop a strong foundational knowledge of GS1 Standards for product identification- Identifiers & Data Carriers- Product Hierarchy- GTIN Allocation/GLN Allocation- Barcode Print Quality

• Develop institutional knowledge of the GS1 GTIN Management Standard throughout the organization.- Develop and document your organizations GTIN assignment and maintenance strategy.

• Understand your organization’s prefixes.- Will different lines of business share the same prefix or utilize different prefixes?

• Understand the prefix capacity required.- Determine the levels of the hierarchy that need to be identified.- Recommend assigning a GTIN to all levels even if not placed on the logistic unit.

• Greatly reduces the need to re-package at a later date.

32


Package Measurement Best Practices

• Determine the means to capture and share weight and dimensional data pre-production.

• Measure every level of the hierarchy and every GTIN separately.

- Do not aggregate weights and measures.• Develop an audit process to ensure measurements stay

accurate over time.• For packaging types with variability (flexible packaging,

liquids, very small items) measure and weigh several samples and communicate the average.

• Develop institutional knowledge of the GS1 Package Measurement Rules.

33



As the products data elements are added by the various roles within your organization, each of the data stewards is accountable for the accuracy of those attributes throughout the lifecycle of the product.

2. Assign data owners throughout the organization.

• Data Stewards are identified, along with their individual processes for accountability, and practices toward accurate data.

• Data Stewards are connected in a way that provides continuity in item data (one source of the truth).

34



Ensure specific controls & measures are in place for gathering data, and are overseen by a master data governance entity – this can be a team of people or one person depending upon the size of the organization.

3. Appoint one entity / department / individual as the sole owner of product data.

• Master Data Governance exists (team or individual) to ensure continued controls and measures in place for gathering data.

• Provides support and oversight to the Data Stewards.

35



Verification must occur for trusted quality data to be used and exchanged within the trading community.

Audit of all attributes must be done on finished goods, following the GS1 Package Measurement Guidelines.

4. Audit all new items produced in a sustainable production environment i.e. finished goods.

• Verification of identified attributes are a part of the process on all finished goods.

• Trained personnel are identified, and accurately applying Package Measurement Guidelines.

• The verification process includes a feedback loop to data stewards, and Master Data Governance.

36



Accurate data at all levels of the hierarchy is leveraged throughout the supply chain.

Alignment to one source of the truth within your Master Data Governance Department is crucial to sharing the same accurate data across the supply chain.

5. Execute communication of initial attributes and package measurements, both internally and externally.

• The physical data should align with the information shared with trading partners.

• The process for correction and change should align with industry best practices.

37


Data Sharing

There are many ways data can be shared:- EDI- GS1 Global Data Synchronization Network (GDSN)- Product Catalog

• Incorporate data sharing as an ongoing business process within the organization.• Data sharing is not a one-time initiative and should not be viewed as a project to

be completed.• Ensure the output from the MDMS/PIM complies with trading partner requirements.• Establish ownership for each attribute that is exchanged.• Periodically audit information shared with trading partners.

38

- Sales Sheets- Web Portals


Data Governance Recap - Execution

• Executive buy-in:- Provides the necessary resources to establish a cross functional data governance

team.- Defines and communicates the vision for data governance.

• Education:- There needs to be a documented education and training policy to ensure resources

remain current as internal Data Governance processes change, personnel change, and GS1 Standards evolve.

• Metrics:- Data quality should be measured its completeness, timeliness and accuracy.

39


Data Governance Recap - Execution

• Single Source of the Truth- When possible there should be a central repository of data- In situations where this is not achievable – there needs to be clearly documented

hand-offs and audits to ensure the data is consistent across multiple repositories.

• Verification:- Regularly scheduled audits of the master data file. - Regularly scheduled audits of all data governance processes, procedures, and training. - Regularly scheduled audits of the product.

40


Data Governance Recap - Guiding Principles

• Information is an asset and should be viewed and managed as such.- Businesses need to own the data, manage the process by which it is sourced, and

institute an organization-wide vision.

• Quality data is achieved by applying management processes, methods, tools and best practice.- Data governance requires organizational change, which is something organizations

can resist.

• Data Governance is a business process, not a project. - Success is achieved when the people, processes, and technology are flexible. - Business requirements change over time, meaning that the use of the data also

changes for purposes beyond the scope of existing expectations.

41


GS1 US Data Quality Resources

GS1 US National Data Quality Program1. Data Quality Framework2. Self – Assessment Guide3. Data Governance Best Practice Guidance4. ROI Calculators5. Data Quality Playbook6. Case Studies:

A. Driving Product Data Quality by the Numbers B. Ecolab Takes a Clean Approach to Spotless Product DataC. Beaver Street Fisheries Outswims the Big FishD. Bean Counting is Easier with Quality Data

7. Data Quality Online Course

42

www.gs1us.org/dataquality

http://www.gs1us.org/dataquality


Contact Information

43

Jeff CowanDirector Community EngagementGS1 US

T 609.620.8043

E [email protected]

www.gs1us.org

mailto:[email protected]


Legal Disclosure

GS1 US, Inc. is providing this presentation, as is, as a service to interested parties. GS1 US MAKES NO REPRESENTATIONS IN THIS REGARD AND DISCLAIMS ALL WARRANTIES, EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO, ANY WARRANTY OF ACCURACY OR RELIABILITY OF ANY CONTENT, NONINFRINGEMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE.

GS1 US shall not be liable for any consequential, special, indirect, incidental, liquidated, exemplary or punitive damages of any kind or nature whatsoever, or any lost income or profits, under any theory of liability, arising out of the use of this presentation or any content herein, even if advised of the possibility of such loss or damage or if such loss or damage could have been reasonably foreseen.

44


Questions?

EnterWorks MDM & PIM Virtual ConferenceJoin us for Upcoming Webinars in the Everything MDM & PIM Virtual Conference Series

www.winshuttle.com/event/everything-mdm-pim-virtual-conference/

Rich Products – Overcoming the Challenges of Multi-Channel Syndication Using Automation in PIM

PIM and Fender – Perfect Together!

June

23June

25

Week 4: Voice of the Customer – Rich Products & Fender

Presented by Nandor Forgach of Rich Products and Ramesh Vadassery of IntelliTide

HDA Truck Pride Customer Case Study

Replay: Forrester – The Next Generation of MDM & PIM: AI & Machine Learning

June

30July

2

Week 5: Customer & Expert Insights – HDA Truck Pride & Forrester

Presented by John Lurz of HAD Truck Pride and Rusty DiNicola of Pivotree

Presented by Michele Goetz of Forrester

Presented by Jon Varo of Fender

https://www.winshuttle.com/event/everything-mdm-pim-virtual-conference/

why your business needs accurate product data

Documents