1. Transforming Data Management
for the Cloud

2. Square Pegs In Round Holes: Rethinking
Data Availability in the Age of Automation &
Artificial Intelligence
Eric Little, Ph.D.
Industry Innovation Principal Director
Accenture

3. Eric Little, PhD
Industry Innovation Principal Director
Head of Strategy & Analytics – INTIENT
Accenture Life Sciences
Square Pegs In Round Holes:
Rethinking Data Availability
in the Age of Automation &
Artificial Intelligence
Copyright © 2023 Accenture. All rights reserved.

4. • Since the 70’s much effort has been spent on
databasing information
• The basic concept involves physical “lift &
shift” into ever larger buckets of information
• Data Warehouses 🡪 Data Lakes 🡪 Lake Houses
🡪 etc…
• The time to value realization is slow
• Effort and costs are very high over time (this
process never completes)
DATA INTEGRATION IN
THE “DB AGE”
Copyright © 2023 Accenture. All rights reserved. 4

5. 5
Copyright © 2023 Accenture. All rights reserved.
• Large organizations contain a wide
variety of data
• Moving and migrating all sources into a
single location is extremely
time-consuming
• People need “lenses” on their data that
is hard to provide in a single index
• We have become accustomed to
on-demand data, search results, and
analysis from our private lives
• Why are we still doing our day jobs with
older practices/tech? (iPhone effect)
Decentralized Data
Data
Analyze
Domain
Ontology
Process
Crawl
Catalog

6. Copyright © 2023 Accenture. All rights reserved. 6
APPLYING FAIR DATA PRINCIPLES
FAIR DATA – Findable, Accessible, Interoperable, Reusable
• Data needs to be consistently tagged and organized
• Standards exist to link data consistently across sources
• First key to success is effectively managing metadata
Semantics can help us here:
1. Controlled vocabularies (terms, synonyms, definitions)
2. Taxonomies (sub-class & super-class relationships)
3. Ontologies (RDF/OWL graphs to capture complex relationships)

7. 7
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Connecting Products & Services Across the Tech Landscape
• On-demand data allows for faster insights and exchanges of information within large networks
• Private data or open source can be connected
• Customization and delivery of specific client needs can be improved to provide tailored solutions faster and
cheaper over time
Crawler AI Engine Catalog Collection UI/UX
▪ Configuration of new data
source
▪ Optimization of New Data
Source
▪ Introduce new Models
▪ Integrations to existing
services
▪ New Index Creations
▪ Configuration of the
Metadata
▪ Data Interface
▪ Custom User
functionalities
▪ Additional UX
Modifications .

8. 8
Example: Finding Patients & Sites By Combining Data
Copyright © 2023 Accenture. All rights reserved.
Provides an innovative solution to select trial sites based on the available patient population. The technology connects different types of data together in a seamless &
associative way. For example, patients can be discovered using indirect reasoning across multiple data sets, e.g., trials, disease, treatment (drug), physician
(prescriber), claim, and geo-location.
Intelligence Everywhere helps clinical trial
designers make better decisions faster by
learning from similar trials.
Intelligence Everywhere connects and
understands these disparate data sources,
enabling the end-user to discover insights that
would have remained invisible using other
technologies.
Clinical trial leaders are directly empowered to
better prioritize sites to meet patient recruitment
goals, leveraging a data-driven approach spanning
patient claims, clinical operations metrics, and public
trial information.
Interaction Insights Impact

9. What About
Gen-AI?
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reserved.

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Making Gen-AI More Usable At Industry Scale
Security
Clients want assurances that their IP and/or data will
not be breached or exposed to open source. LLMs
need to be constrained to specific data
Explain-ability
LLMs often treat each interaction as a session and
therefore will perform tasks differently at different
times without the ability to explain how it generated
the answer it did
Daydreaming
LLMs will fill in gaps with new (and sometimes
fallacious) information
There are 4 major challenges to overcome with Gen-AI to reduce risk:
Ethics
LLMs need to be taught that certain data is
private/sensitive and certain objects (people) have
ethical import while other objects do not

11. Copyright © 2022 Accenture. All rights reserved. 11
Intelligence Everywhere with Gen-AI Becomes Codeless &
Lowers Barriers for Adoption by LS Clients
Solution Approach
Client Situation
Outcomes
• Clients are looking to tap into more and more of their data and be able to use that data to drive business decisions.
• Applies all the principles we’ve discussed: De-centralized data, metadata & semantic connectivity, on-demand pulls of information, advanced analytics & AI
• Tools such as this can accelerate clients’ capabilities dramatically
• We utilize Gen-AI technology to handle all internal coding so that
users only need to answer text prompts to find and analyze data
• All SPARQL graph queries are generated
• All Python coding, R coding, etc. for statistical analysis is generated
• Semantic data mapping from sources to the ontologies are
automated as much as possible (with human-in-the-loop to help
correct and teach the system)
• Information is driven automatically to BI Tools and UI/UX layers so
users can immediately see and interact with their data simply by
asking questions
• Capability to shift to voice automation (e.g., Alexa, Siri, etc.) is
straightforward
• Clients can do advanced data science analysis of data with almost zero added IT training
• Gen-AI systems will be “fenced in” inside of IE to be secure and not allow sensitive or proprietary data to be exposed
• All reasoning and decision-making by the system is limited by the semantics to specific domains; so, all steps are 100% explainable by the system
• Clients will be able to find highly complex patterns in their data & be able to automatically analyze those patterns using Gen-AI
• IE uses Gen-AI to increase data-driven decisions while addressing the risks of using Gen-AI technology
Technology Workflow Example

12. Square Pegs In Round Holes: Rethinking Data
Availability in the Age of Automation & Artificial
Intelligence
SVP Data Architecture and Chief
Evangelist, Denodo
Paul Moxon
Industry Innovation Principal
Director, Accenture
Eric Little, Ph.D.
FIRESIDE CHAT

13. Thank you!