Artificial intelligence has rapidly captured the collective corporate imagination, leveraging advanced algorithmic techniques, natural language processing, and human-computer interaction to ignite transformative change in advanced problem-solving. Base models for Generative AI systems are trained to support various applications such as market forecasting, customer behavior predictions, and risk assessments. However, the quality of these models is inextricably linked to the quality and diversity of the data they're trained on. In this webinar, we explore the pivotal role of data enrichment in molding AI models with a specific focus on the intricacies of how data enrichment contributes to the training processes. Data enrichment, in which existing datasets are infused with additional context and relevant information from trusted third-party sources, helps AI models surpass their current capabilities, enabling them to understand complex nuances and generate contextually attuned responses. A core component of our discussion will revolve around leveraging trusted third-party datasets. We will discuss how integrating these datasets with AI models can lead to a more comprehensive understanding of intricate patterns and a heightened ability to generate insightful outputs. Attendees will learn about: ·        Establishing levels of trust in third-party datasets ·        How data curation by domain experts improves data enrichment ·        Methods of data enrichment ·        The dependence on trusted third-party data for enhancing the quality and relevance of AI model outputs

1. A Fireside Chat moderated by
David Loshin
Affiliate Research Director, TDWI
President, Knowledge Integrity, Inc.
Senior Lecturer, University of Maryland
November 2, 2023
Supercharging AI with Data Enrichment

2. SPONSOR
2

3. President, Knowledge Integrity, Inc.
Senior Lecturer and Lead for External Relations,
University of Maryland

4. What we will talk
about today
• Setting the stage:
• Generative AI as a business imperative
• Data imperatives for AI model quality
• Discussion: The pivotal role of data
enrichment in training and fine-tuning
Generative AI models

5. Emergence of Generative AI

6. What is Generative AI?
• A subset of artificial intelligence that includes systems designed
to generate outputs such as images, music, text, or other forms of
media, based on its training data
• Learns from existing and generate new data that is consistent
with the original data set
• Generative AI systems that have been trained on billions of
parameters use prediction to create new instances of data in
response to provided prompts
• Large Language Models (LLMs) are a type of Generative AI that
have been trained on massive amounts of content

7. Ensuring Trustworthy & Appropriate Results
Data
volumes
Data
quality
Data
access
• Issues include:
– Bias
– Privacy
– Ethical concerns
– Legal concerns
– Hallucinations

8. Data Enrichment & LLM Training
• Improving the utility of data through appending and integration of
relevant content from additional sources
• Enrichment is used for
– Refining contextual nuances
– Improving fidelity of prompt responses
– Improve pattern recognition to reduce probability of hallucinations
– Improve interpretability of results

10. The leader in data integrity
Our software, data enrichment products and
strategic services deliver accuracy, consistency, and
context in your data, powering confident decisions.
of the Fortune 100
99
countries
100 2,500
employees
customers
12,000
Brands you trust, trust us
Data leaders partner with us
10

11. AI initiatives succeed with trusted data
of leading
businesses have
ongoing investments
in artificial
intelligence
91%
From Noise to Brilliance: Supercharge AI with Data Enrichment
Algorithms
Data
Modeling
Large
Language
Models
Deep
Learning
Hyperparameter
Tuning
Training
Data
Retrieval
Augmented
Generation
Supervised
Learning
Natural
Language
Processing
Bias
and
Fairness
Artificial
Intelligence
Feature
Engineering
Neural
Networks
Chatbots
Machine
Learning
Data
Mining
11
Source: NewVantage

12. For trusted data,
you need data integrity
Data integrity is data with maximum
accuracy, consistency, and context for
confident business decision-making
Data
Integrity
From Noise to Brilliance: Supercharge AI with Data Enrichment
12

13. What is data
enrichment, exactly?
13
It’s the process of enhancing your data by
appending relevant context from additional
sources – improving its overall value,
accuracy, and usability.
From Noise to Brilliance: Supercharge AI with Data Enrichment

14. Trusted third-party data at a global scale
Addresses &
Property
Verified and validated address and
property data for map display and
analytics
Boundaries
Administrative, community, and
industry-specific boundaries for data
enrichment and territory analysis
Demographics
Demographic and consumer context
data for better understanding people
and behavior
Points of
Interest
Detailed business, leisure, and
geographic features for location
and competitive intelligence
Streets
Robust street-level data for mapping,
analysis, routing, and geocoding
Risk
Natural hazard boundaries related to
flood, fire, earthquakes, and weather
14
Expertly curated datasets containing thousands of attributes for faster, confident decisions
From Noise to Brilliance: Supercharge AI with Data Enrichment

15. 15 From Noise to Brilliance: Supercharge AI with Data Enrichment
Purchases &
Shopping
Building & Parcel
Boundaries
Lifestyles
PreciselyID
School Rankings
Points of Interest
Addresses Population
Property Attributes
Weather
Natural & Manmade
Hazards
Travel Time
Administrative
Boundaries
Land & Property Consumer Environment
Data enrichment can be easy with the right tools
A unique identifier for every address that doesn’t change, and other methods for appending data

16. Addressing AI limitations with enrichment
Inaccurate training data
leads to poor model
accuracy and
performance, yielding
low-quality results
Clean data reduces the
need for extensive data
prep, simplifying the
overall AI pipeline and
improving efficiency
High-integrity data
reduces the time and
computational resources
required for model
development
Practitioners can rely on
consistent data to
extract meaningful
features that contribute
to model performance
Transparent, accurate
data aids in the
understanding of model
decisions, builds trust,
and identifies biases
Data with integrity
avoids introducing noise
that contributes to
overfitting, resulting in
more robust models
Models trained on high-
integrity data are easier
to maintain, as changes
are less likely to cause
unexpected issues
Easier model
maintenance
Reduced
Preprocessin
g Overhead
Effective
Feature
Engineering
Enhanced
Model
Interpretability
Reduced
Overfitting
Faster model
training
Model
Accuracy and
Performance
When AI models are built
on reliable data, they are
more likely to perform
consistently and
dependably
Reliable
Model
Deployment

17. 17 From Noise to Brilliance: Supercharge AI with Data Enrichment
• Financial crimes
and compliance
• Customer insight
• Branch location analytics
• Fraud analytics
• Risk analysis
• Customer insight
• Fraud analytics
• Pricing
• Network and coverage
planning
• Customer insight
• Location-based
marketing & advertising
• Asset management
FINANCIAL SERVICES INSURANCE TELECOMMUNICATIONS
• Customer insight
• Retail location analysis
• Location-based
marketing & advertising
• Home search
• Appraisal analysis
• Valuation modeling
RETAIL
• Service optimization
and delivery
• Planning
• Compliance and safety
• Emergency response
and management
• Economic development
• Site selection
• Market analysis
• Lifestyle modeling
GOVERNMENT REAL ESTATE
• Customer insight
• Checkout analytics
• Logistics and delivery
• Location-based
marketing & advertising
eCOMMERCE
Solve complex, real-world challenges

18. Key takeaways
Appending relevant context from
additional sources
What is data enrichment?
Accuracy, performance, and utility
across various applications
How does it improve your AI?
Improves business outcomes, saves
money, and user trust
How does it benefit you?

19. Fireside chat
19
Copyright © 2023 TDWI

20. QUESTIONS?

21. CONTACT INFORMATION
If you have further questions or comments:
David Loshin, Knowledge Integrity, Inc.
loshin@knowledge-integrity.com
Antonio Cotroneo, Precisely
antonio.cotroneo@precisely.com

22. Thanks to Our Sponsor
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23. THANK YOU!
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