Accelerating Product Data Programs with Pre-PIM Software

www.earley.com © 2023 Earley Information Science, Inc. All Rights Reserved.
WEBINAR
WEBINAR
SPEAKERS
Accelerating Product Data Programs with Pre-PIM
Software
SETH EARLEY
CEO & FOUNDER
EARLEY INFORMATION SCIENCE
THANK YOU
ELI COOLEY
SENIOR CONSULTANT
•
JOE WALSH
CLIENT PARTNER
•
MODERATOR

Today’s Speakers
Seth Earley
Founder & CEO
Earley Information Science
Seth@earley.com
https://www.linkedin.com/
in/sethearley/
2
Eli Cooley
Senior Consultant
Eli.Cooley@earley.com
JoeWalsh
Client Partner
Joe.Walsh@earley.com
https://www.linkedin.com/in/joewalsh361/

BeforeWe Get Started
WE ARE RECORDING SESSIONWILL BE
50 MINUTES PLUS
10 MINUTES FOR
Q&A
YOUR INPUT IS
VALUED
Link to recording & slides
will be sent by email after
the webinar
Use the Q&A box to
submit questions
Participate in the polls
during the webinar
Feedback survey afterward
(~1.5 minutes)
Thank you to our media partners : CMSWire & Marketing AI Institute
3

Agenda
• Typical challenges of Product Information programs
• Limitations of PIM software
• Spreadsheets and more spreadsheets
• What is Pre-PIM software?
• Use Cases
• Getting Started
4

About Earley Information Science
5
Proven methodologies to organize information and data.
SELL MORE
PRODUCT
SERVICE
CUSTOMERS
EFFICIENTLY
INNOVATE
FASTER
1994
YEAR FOUNDED.
Boston
HEADQUARTERED.
50+
SPECIALISTS & GROWING.

Poll
6
1. We are in the planning stages for PIM
2. We are in the PIM implementation
stage
3. We have implemented PIM but have
challenges
4. We have a PIM fully deployed with no
major issues
5. None of the above
Where are you on your product information
management journey?

Typical Pre-PIM Challenges
7
• Data modeling
• Pilot data design
• Taxonomy design
• Stakeholder review and approval
• Data quality and remediation
• Measuring impact
• Governance and change management

8
Understanding PIM Data
PIM
Technical
Support
Pricing
CPQ
Product Data Enrichment
Taxonomy & Metadata Mgmt
Enforce Rules & Standards
eCommerce
Inbound
Quality
&
Governance
Reporting
Centralized Product Data
Product ID Marketing Copy
Taxonomies Tech. Attributes
Descriptions Sellable UoM
Related Products Digital Asset
PLM
ERP
MDM
Supply Chain /
Data Pools
Marketing
Content
Knowledge
Bases Partner Feeds
CMS / DAM
Content Templates and Assets
Components Content Metadata
Libraries Content Fragments
Project Name Digital Assets
Websites /
Mobile
Outbound
Syndication
ETL
&
API
Services
“UPSTREAM”
“DOWNSTREAM”
Supplier Portal
8

PIM Data Ingestion (with spreadsheets)
PIM
PLM
ERP
MDM
Supply Chain /
Data Pools
Suppliers
A Bunch of Spreadsheets
convert to
load files
9

PIM Data Ingestion with Pre-PIM
Environment
PIM
• Product Data Enrichment
• Taxonomy & Metadata
Mgmt
• Enforce Rules &
Standards
• Stakeholder Review
PLM
ERP
MDM
Supply Chain /
Data Pools
Suppliers
Pre-PIM
export load
files
10

What a Pre-PIM is and isn’t good for:
Use Case Pre-PIM PIM
Data Model Design x
Taxonomy Design x
Business Stakeholder Engagement x
Data Remediation x
Design Metrics x
ControlledVocabulary x
Style Guideline Build & Alignment x
Master Data Management x
Syndication of Product Data x
Day-to-Day Onboarding / Supplier Portal x
ERP Integration x
DAM Integration x
Workflow & Permissions x
11

Data Onboarding: Isn’t that what a PIM does?
12
• An environment needs to be set up
• PIM design decisions needed before deployment
• Data model design not easily changed
• Hierarchies, categorization and attributes may need to be updated
• Governance challenging to maintain and apply
• Help text, definitions, style guides, controlled vocabulary
• Performance issues when making large scale changes
• Moving hierarchies, large scale exports
• Minimal Data Quality Reporting & Analytics Out-of-the-Box

Can’t I use spreadsheets?
13
• Complex and difficult to reconcile
• Issue tracking and versioning
• Inability to collaborate effectively
• Difficulty synching separate
workstreams
• Difficult to manage taxonomy in
spreadsheets
• More time managing spreadsheets,
less time designing

Poll
14
1. Using too many spreadsheets
2. Unable to track issues effectively
3. Lack of audit trails
4. Difficulty enforcing style guides and standards
5. Data quality metrics
6. Something else
What are your challenges around onboarding data to PIM?
(check all that apply)

Why use a Pre-PIMAccelerator?
15
• Reduces complexity of design
• Enables experimentation and iteration
• Integrated governance & data quality rules
• Integrated issue tracking and documentation
• Enables monitoring of design and structural metrics

www.earley.com © 2023 Earley Information Science, Inc. All Rights Reserved. 16
INTRODUCING
commerce

Use Cases
17
• PIM onboarding
• Data quality & remediation
• Autoclassification
• Data quality benchmarking
• Building / applying data quality rules & controlled vocabulary
• Preview mode
• Taxonomy design
• Base-variant design
• Data sourcing review
• Description optimization

Design Iteration before PIM Implementation
18
• Optimize hierarchy design
prior to PIM implementation
• Align regional and channel
hierarchies with master
hierarchy
• Review and approve with
stakeholders before loading to
PIM

Data Quality & Remediation
19
• Normalize attributes and attribute
values prior to PIM load
• Avoid lifting and shifting
unnormalized content
• Normalize content across multiple
sources
• Build data quality rules inline with
normalization efforts

Collaborative Design
20
• Data optimization activities require centralized issue tracking across multiple stakeholders
• JIRA boards and shared spreadsheets are not integrated with design environment and can
be inefficient
• Design software allows for access to issue tracking at the point of design

Autoclassification
21
• Autoclassification functionality classifies items to existing hierarchy design with ranked
guesses and confidence scoring
• Autoclassifiers decrease time spent classifying items so analysts can focus on design work
• Autoclassifiers also help identify misclassified items

Structural Metrics
22
• Easy access to real-time reporting and analytics allows Analysts to assess
design and identify outliers and opportunities
• All data is exportable to facilitate analytics in external reporting platforms

Integrated Governance
23
• Integrated vocabulary management and style guidelines allow for semi-
automated content normalization
• Data quality rules can be loaded to PIM (where applicable)

Preview Mode
24
• Preview mode allows for
hierarchy, filters, typeahead
and table views to be
viewed in real time prior
to PIM load
• Preview mode is the
preferred review context
for some stakeholders

Getting Started
25
Schedule a consultation with EIS
• Product data readiness assessment
• Wordmap Commerce demo (with your data)
To schedule a conversation, send a note to:
Carolyn.Southwick@earley.com

Q&A
26

Additional Reading
27
From Earley Information Science
How Product Attribute Schema Design Provides a Competitive Advantage
https://www.earley.com/insights/how-product-attribute-schema-design-provides-competitive-
advantage
3 Product DataTaxonomyTips to ImproveYour eCommerce
https://www.earley.com/insights/3-product-data-taxonomy-tips-to-improve-your-ecommerce
6 Steps to SuccessWithYour PIM Implementation Project
https://www.earley.com/insights/6-steps-success-your-pim-implementation-project

EarleyAI Podcast
28
Listen to the Earley AI Podcast to explore what's
emerging in technology, data science, and
enterprise applications for artificial intelligence and
machine learning and how to get from early-stage
AI projects to fully mature applications.
Found wherever you listen to podcasts, including…
Henrik Hahn,
Chief Digital Officer,
Evonik
Dr. Mark Maybury, former
CTO at Stanley, Black &
Decker
RECENT EPISODES

Contact
Seth Earley
CEO – Earley Information Science
________________________________________________
Cell: 781-820-8080
Email: seth@earley.com
Web: www.earley.com
LinkedIn:
https://www.linkedin.com/in/sethearley
29
Joe Walsh
Client Partner
Joe.Walsh@earley.com
https://www.linkedin.com/in/joewalsh3

Thanks!
30

Accelerating Product Data Programs with Pre-PIM Software

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