EIS-Webinar-Silabs-KM-Content-Program-2023-06-07.pdf

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WEBINAR
WEBINAR
How Silicon Labs Developed and Deployed an
Effective Content Program in 3 Months
ALLISON BROWN
CLIENT PARTNER
EARLEY INFORMATION SCIENCE
THANK YOU
BOB POWER
VP ENGINEERING
SILICON LABS
PRAKASH GOVIND
SOLUTION ARCHITECT
SETH EARLEY
CEO AND FOUNDER
SPEAKERS
MODERATOR

Moderator
2
Seth Earley
Founder & CEO
Earley Information Science
Seth@earley.com
https://www.linkedin.com/in/sethearley/

Today’s Speakers
Allison Brown
Client Partner
Allison.Brown@earley.com
3
Prakash Govind
Solution Architect
Prakash.Govind@earley.com
Bob Power
VP of Engineering
Silicon Labs
rapower@silabs.com
• Leads Silabs University
• Managed Silabs embedded software (MCU and
wireless) and grew team from 20 to over 300
• Avid cyclist (team captain that raised over $1.5mm
for Dana Farber Institute)
• 23+Years of Experience Leading IA Centered
Programs
• Industries Served: Healthcare, Consumer Packaged
Goods, Industrial Supplies, Manufacturing, and
Distribution
• Technical architect for EIS
• Develops and implements knowledge
management solutions
• Currently researching use of LLMs in
knowledge programs

BeforeWe Get Started
WE ARE RECORDING SESSION WILL 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
4

Agenda
• About EIS
• Generative AI Depends on Content
• Training Content Chaos at Silicon Labs
• Story Telling: BusinessValue of Content IA
• Project Mechanics (content structure, taxonomy, schema, metadata, tagging, etc.)
• Making Abstract Concepts Concrete (Implementing iterative MVPs)
• Demonstrating value quickly (3 months)
5

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1994
YEAR FOUNDED.
Boston
HEADQUARTERED.
50+
SPECIALISTS & GROWING.
Earley Information Science is a professional services firm focusing on architecting and organizing
data – making it more findable, usable, and valuable.
Our proven methodologies are designed to address product data, content assets, customer data,
and corporate knowledge bases. We deliver scalable solutions to the world’s leading brands,
driving measurable business results.
We make information more useable, findable, and valuable

Copyright © 2023 Earley Information Science, Inc. All Rights Reserved.
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Generative AI
Without the right data, Generative AI “hallucinates”.
7

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GenerativeAI
8
Creates new, original content
Trained to learn the patterns and relationships
Learned knowledge generates new content
(Not a copy of the training data).
This means that the knowledge of the organization
needs to be referenced by the technology
*Source: ChatGPT

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LLMs are Trained on Publicly Available Content
After the model is trained, it can be fine-tuned on specific conversational
domains or topics to improve its performance on specific types of
conversations.
Fine-tuning involves training the model on a smaller, more focused dataset of
conversational data that is specific to the desired domain or topic.
Source: ChatGPT
This allows the model to learn more specialized
knowledge and terminology that is relevant to
the domain, and to generate more accurate and
contextually appropriate responses.

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What does this mean for Content Management and Chatbots?
10
Content (and Knowledge) Management is needed to:
• Ensure that the chatbot has access to a wide range of relevant
information and can provide accurate and useful responses to user
queries
• Identify knowledge and expertise needed for the chatbot to function
effectively
• Design the chatbot's knowledge base and information architecture to
support user needs.

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Generation vs Retrieval
11
User query
Process query
using LLM to
understand user
intent
Generate response based
on LLMs understanding of
language patterns and
concept relationships
Retrieve response based
on querying organizational
knowledge and content
Process response
using LLM to provide
conversational
format
Uses publicly
available information
Uses proprietary
information and
nonpublic IP
Does not compromise
or expose IP
LLMs used to
process query and
present results

“THERE’S NO AI WITHOUT IA”
KnowledgeArchitecture is Needed to Support
GenerativeAI
12

Every experience is a knowledge and content experience
14
I RENEW
I INSTALL & I USE
I SHOP & I BUY
I’M AWARE
What
products do
you offer?
Which products
will meet my
needs?
How do I install, use
and troubleshoot
your product?
How do I get the
most value from
your product?
EVERY QUESTION AND EVERY ANSWER NEEDS TO BE BROKEN
DOWN INTO FUNDAMENTAL COMPONENTS.

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Product Manual – HM 2900 Series Modem
GenerativeAI Reference Content Requires Context
15
Overview
Set up options
Settings Model 2960
Error code 50
Settings Model 2970
Error code 56
Installation
Troubleshooting
Hardware setup
VPN requirements
Factory settings
Technical Specifications
I need to install a modem.
Which modem model do you have?
Content type = Product Manual
Content type = Troubleshooting
Content type = Installation
Product name = HM 2900 Series Modem
Model = 2960
Error code = 50
Model 2960
I am receiving an error code of 50
OK, here are the installation settings…
That error requires the following
troubleshooting steps:
What context is required?
Type of information, installation, product model, error code, etc.
Metadata provides context

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Generalized vs Specialized Language Models
16
Organization
Industry
Generalized LLMs: GPT 3, GPT 4, LaMDA, BERT
Life Sciences: Gatortron, SciBERT, BioNeMo, MED PALM-2,
BioMegatron, Med-BERT, BioGPT, MedGPT, ChemGPT
Fintech: Bloomberg LLM, FIBO LLM
Legal: LexisNexis LLM
And more.
Enterprise Ontology with
company specific metadata

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Getting your content organized
• Generalized language models are about understanding the world but will not
use your IP for competitive differentiation
• Content can be ingested into a database that Generative AI can use as a
reference source
• If the content is not there, the system cannot provide an accurate answer.
• This means that content databases are critical to using tools like ChatGPT
• Metadata (tags) applied to content become additional signals to the LLM
• Getting an answer to a support question requires context provided by
metadata:
• Product name
• Model
• Version
• Etc. …
This information (content model) needs to be
ingested into a Generative AI application as
part of the “embeddings”

Poll
18
1. Not at all
2. There is limited understanding
3. This is well understood by leadership
Does your leadership understand the role of organization specific
content and knowledge in ChatGPT types of applications?

Poll
19
1. Little structure, no formal processes
2. Department level standards but little to no
compliance enforcement
3. Mature processes and standardized structures
for high value content (support, ecommerce)
4. Enterprise-wide standards with compliance
metrics
5. None of the above
What is the state of content management in your
organization?

About Silicon Labs
20
Silicon Labs is a $1billion global leader in secure, intelligent
wireless technology.The organization faced significant
challenges in content usability, traceability, and findability,
hindering their internal training workflows and access to
critical knowledge assets.

Training Storage Was in Chaos
21
Training Type
Technical
Presentations
Field Training
R&D Training
(Internal or external)
Site Based Training
3rd Party Training
Software Conferences
Professional
Development
Customer
Conferences
Lunch-n-Learn
Compliance Training
Webinars
Storage (back end)
No consistent storage location
Ad-hoc access control scheme
No schema
No taxonomy
No metadata
No naming scheme
No defined workflow
No publishing process
Lots of tribal knowledge
Chaos!
Nearly impossible to find or
reuse any of our training
content

Content Management Systems
A content management system (CMS) is a tool that helps
companies organize digital content. It is the single source
of truth for all content.
§ Organize – Create schema, categories, and taxonomies
§ Classification – Tag content
§ Storage – Cloud-based, access controlled
§ Workflow – Support governance or workflow
§ Versioning – Ability to track different versions
§ Publication – Deliver content to desired channels
But… Prior to employing a content management system,
you need to design how you are going to organize and
classify your content.
SILICON LABS CONFIDENTIAL. DO NOT DISCLOSE IN ANY FORM OUTSIDE OF SILICON LABS.
22
Organize Classification
Storage Workflow
Versioning Publication
1. Smartsheet.com

Metadata and Taxonomy
23 SILICON LABS CONFIDENTIAL. DO NOT DISCLOSE IN ANY FORM OUTSIDE OF SILICON LABS.
IC Design
Radio Frequency
(RF)
Receiver
Transmitter
Synthesizer
Analog
Data
Converters
Filters
1. Inspired by Earley Information Sciences
Taxonomy
• A logical structure used to organize content
into categories, where the groups are first
defined broadly and then narrowly
• Uses a single, organizing principle
• SilabsU uses Training Topic
• Provides a controlled vocabulary
Metadata
• Data about data
• Provides context for the data
• Helps organize, find, and understand the data
Course ID R&D-HW-491
Title Fundamentals of Thermal Noise
Abstract Among all the things...
Author Harry Nyquist
Topics Hardware; IC Design
Created 28-Feb-23
Classification Internal Use Only
Skill Level Beginner
Duration 1.5h

Learning Management Systems
A learning management system (LMS) is a software application
for the administration, tracking, reporting, and delivery of
educational courses, training programs, or learning and
development programs. It is where learners engage with the
content.
§ Administer – LMS administers can create and assign training
§ Delivery – The LMS delivers content to learners
§ Engagement – Learners engage with the content
§ Tracking – The LMS tracks registration and completion
§ Reporting – Management can receive reports on their
team’s training status
§ Integration – The LMS can integrate with other systems such
as the CMS or the HR systems where goals can be tracked
24
Administer Delivery
Engagement Tracking
Reporting Integration
1. Smartsheet.com

Workflow / Processes
25
§ Open – Creates a request for new training
§ Selection – Course is selected for development
§ Design – Course is designed
§ Developed – Course is developed
§ Production – Includes video shooting, editing,
etc.
§ Approval – Approved by the instructional
designer and pillar owner
§ Published – Published to 1 or more publishing
targets

Tying it All Together – Learning Platform
26
Content Management System (CMS)
(Content, Metadata, Taxonomy)
New Content Creation Import / Edit Existing Content
CMS Requirements
• Near-infinite storage
• Integration with Microsoft 365
• Structured tagging, taxonomy
• No license issues
• Versioning
• IT’s security requirements
• Fast access across the globe
Transformation
• Workflow-based integrations
• Transform into frontend formats
• Apply tagging from master taxonomy
Workday Learning
(LMS)
Salesforce
(Customer)
Silabs.com
(Public)
YouTube
(public access)
Intranet Confluence
• Publishing targets
• Learners discover
and interact
• Source artifacts
• Content developers

Top Down versus Bottom Up
27
• Initial goal was to deploy Silicon Labs University
• Building training content – but had no place to put it
• SharePoint was not well received in the org (initial intranet
deployment was disappointing)
• Pulled together quickly
• Gained visibility across the organization
• Other teams recognized their content chaos
• Reached out to address their challenges

Success Metrics
28
“Great Place to Work” Survey* questions on training:
• Asks employees if they are satisfied with the amount of training opportunities they have
• Each year typical impact has been +/- 1% year after year
• This score as an increase of 5% in the first 6 months of SilabsU being launched.
This is a massive shift!
Number of courses in our Learning Management System
• Q1 of 2022: 149 courses
• Q1 of 2023: 583 (a nearly 400% increase in training being offered)
(Due to finding and tagging existing content and making it widely available through our CMS and LMS)
Number of training hours consumed in Learning Management System
• 35% increase in training hours consumed
(Due to finding and sharing of content that already existed)
*https://www.greatplacetowork.com/solutions/recognition/lp

Summary
29
§ Training source content is now stored in one place
• Tagged with appropriate metadata
• Classified according to a controlled taxonomy
§ There is a workflow and governance process in place
§ Content is published and findable for learners and
content creators to promote use and reuse
§ There are many other opportunities to apply lessons
learned to other types of content.
• Technical documentation
• Policies and procedures
Chaos Tamed!

Storytelling: Communicating Value
30
“We are finding that as departments see our success, they want to emulate it. With the governance
process we have in place as well as the carefully designed information models, we can extend to new
applications more quickly and cost effectively. The assets from this project become increasingly valuable
as they are extended and applied.” - Bob Power

Poll
31
1. Not being considered
2. Some awareness but not a priority
3. Departmental level efforts with budget
4. Content/knowledge enterprise priority with
budget and executive attention.
5. None of the above (let us know in QA tab)
How important is improving content for your
organization?

Demonstrating value quickly (3 months)
32
• Content Analysis
• Conduct Stakeholder
Interviews
• Identify core content
and process issues
• Gap Analysis
• Validate scope &
impact of Course
Content to the
organization
• Quick Wins -
Leveraging existing
platform (SharePoint)
• Balance display
taxonomies for
navigation and
findability
• Leverage SharePoint
improved content
accessibility and
syndication
• Implement schema
• Implement key
facets
• Normalize metadata
• Leverage inheritance
• Implement naming
conventions and
tagging guidelines
• Metadata Fill
• Clarify roles, policies,
structure
• Integrate with key
workflows
• Metrics-driven
governance
• Prevent drift – ongoing
review and refinement
• Inform, educate, and
scale to broader
audience
Assess &
Diagnose
Quick Wins
Design &
Implement
Design
& Implement
Attributes
Governance
Deployment
Discovery & Strategy
Design & Implementation the CMS Foundation

Delivery Plan
33

Stakeholder Interviews - What We Heard
34

Stakeholder Interview Findings
35

Content Audit & Analysis
36

Content Audit & Analysis Findings
37
Inventory
• Legacy content and growth areas
from R&D, Pers Dev, FAE, Legal,
International tech groups
• Includes content stores on LMS,
Brainshark, and DropBox
• Includes LifeSize and other
trainings
Taxonomy Resources
• 2 Taxonomies
• WorkDay Tagging (Topics)
• Subject Matter and Structural Tags
• Learning Terms Taxonomy
• Learning Index Specification
• Community
Support docs and search logs
• Using Brainshark: A step-by-step guide
• Top Bloc WorkDay Learning Questions
• Graphics-Criteria for Creating a Learning
• Search logs from Docs.silabs.com,
Coveo-silabs.com, SiLabs.com, and
Community Search
Key Observations:
• Legacy content buckets well-organized
• Some content lost in past issues
• Workflow is ad hoc and inconsistent
• Lots of treasures lost at sea
Recommendations:
• Move towards master content schema
• Develop a common-ground workflow
• Automate workflows where possible
• Develop and normalize taxonomies

Workflow Working Sessions
38

Schema Design
39
Notice key discrepancies:
• Date treatments
• "Expiry Period" vs "Expiration Date"
• Curator / Creator / Author fields
• How are these different in practice:
• Category (L1-L5)
• Topic (R&D, LMS)
• Presentation Filters/Tags
Problems to solve…
• Extract facets and vocabularies from
topics list
• Identify topics that overlap
• Comparing Topics with .com navigation
• Map existing fields
• Disambiguate forms attribution

Taxonomy & Metadata
40
Topic Salad

Solution Implementation
41
Training Type
Technical
Presentations
Field Training
R&D Training
(Internal or
external)
Site Based
Training
3rd Party
Training
Software
Conferences
Professional
Development
Customer
Conferences
Lunch-n-Learn
Compliance
Training
Webinars
Stakeholder Interviews
Content Audit & Analysis
Content Workflows Taxonomy Design
Schema Design
SilabsU IA and UX implemented
in SharePoint
From Chaos to Order and Findability

Getting Started
42
Schedule a consultation with EIS
What is it?
An interactive and structured 30-minute Q&A discussion led by an experienced EIS product data
professional.
What do you get?
A discovery document that:
• Offers clarity on your organization’s content strengths and weaknesses.
• Provides insights on industry-leading content management practices.
• Identifies and prioritizes activities to bring into your own processes.
Schedule here: https://www.earley.com/meetings/allison-brown1/content-ia-readiness or send a note to
Allison.Brown@earley.com

Q&A
43

Additional Reading
44
Conversation with ChatGPT on Enterprise Knowledge Management
https://www.earley.com/insights/conversation-with-chatgpt-on-enterprise-knowledge-management
The ComingTsunami of Need Knowledge Management for Artificial Intelligence
https://www.earley.com/insights/the-coming-tsunami-of-need-knowledge-management-for-artificial-intelligence
Profiting Next Level Knowledge Management – KM World
https://www.earley.com/insights/knowledge/articles/profiting-next-level-knowledge-management-kmworld
Conquering Chaos Smart Content Management Interview
https://www.earley.com/insights/knowledge/articles/conquering-chaos-smart-content-management-interview-seth-
earley
State IndustryTransactional Content Management
https://www.earley.com/insights/state-industry-transactional-content-management
5 Misconceptions About Data and AI Projects
https://www.earley.com/insights/5-misconceptions-about-data-and-ai-projects
Podcast:
[Earley AI Podcast] Episode 31: Kirk Marple
https://www.earley.com/insights/earley-ai-podcast-episode-31-kirk-marple

EarleyAI Podcast
45
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
46
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
Allison Brown
Client Partner - Earley Information Science
______________________________________________
Cell: 470-385-1773
Email: Allison.Brown@earley.com
Web: www.earley.com

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