Get the inside scoop on how industry leaders in Europe are developing and executing their digital transformation strategies. 451 Research VP and expert analyst, Matt Aslett and Lucidworks VP Channel, Simon Taylor shares and discusses key insights on: The top challenges and aspirations European business and technology leaders are solving using AI and search technology Which search and AI use cases are making the biggest impact in industries such as finance, healthcare, retail and energy in Europe What characteristics technology buyers should use when evaluating AI and search solutions

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Applying AI and Search
In Europe - Now
Matt Aslett, 451 Research
Simon Taylor, Lucidworks
October 23, 2019

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Today’s Speakers
Matt Aslett
Research Vice President
Simon Taylor
VP, Worldwide Channels &
Alliances

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451 Research is a leading IT
research & advisory company
Founded in 2000
250+ employees, including over 120 analysts
1,000+ clients: Technology & service providers, corporate advisory, finance,
professional services and IT decision-makers
2,000+ technology & service providers under coverage
451 Research and its sister company, Uptime Institute, are the two divisions of The
451 Group
Headquartered in New York City, with offices in London, Boston, San Francisco,
Washington DC, Austin, Mexico, Costa Rica, Brazil, Spain, UAE, Russia, Taiwan,
Singapore and Malaysia

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Data and analytics is the fuel for
accelerated digital transformation
Data and analytics is the fuel for
accelerated digital transformation

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43% 27% 22% 8%
Execution Evaluation Consideration
No
Strategy
92% of enterprises are currently executing or
developing digital transformation strategies.
Q. Which of the following best describes the status of your organization's digital transformation strategy? (n=548)
Source: 451 Research's Voice of the Enterprise: Data Platforms and Analytics, Q1 2019

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More enterprises are using data to drive
strategic decision-making
“To what extent would you
say strategic decisions are
currently ‘data-driven’ at
your organization?”
Q. To what extent would you say strategic decisions are currently 'data-driven' at your organization? (n=548)
Source: 451 Research's Voice of the Enterprise: Data Platforms and Analytics, Q1 2019

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0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Execution. We have a
formal strategy and are
actively digitizing our
business processes
and/or assets
Evaluation. We are
planning and researching
to develop a digital
transformation strategy
Consideration. We are
considering it, but have no
formal plans
No Strategy. We currently
have no digital
transformation strategy
Few strategic decisions are data-driven Nearly all strategic decisions are data-driven
Data-driven companies are leading digital
transformation
Q. Which of the following best describes the status of your organization's digital transformation strategy?
Q. To what extent would you say strategic decisions are currently 'data-driven' at your organization? (n=548)
Source: 451 Research's Voice of the Enterprise: Data Platforms and Analytics, Q1 2019

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Automating
the mundane
Automating
the
complex
Augmenting humans
to do everything else

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©2019 451 Research. All Rights Reserved. Q. Which of the following best describes your organization's current machine learning adoption status? (n=1,540)
Source: 451 Research’s Voice of the Enterprise: Artificial Intelligence & Machine Learning, 2H 2018 Advisory Report
Machine
learning is
becoming a
more common
component
of enterprise
analytics
strategies …

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Q22. And what are your organization’s most significant barriers to using machine learning?
Source: 451 Research, Voice of the Enterprise: Artificial Intelligence/Machine Learning 2018 H2
Finding and
accessing
data is a
significant
barrier to
machine
learning,
ranking only
behind the
lack of skilled
resources
40%
33%
32%
28%
22%
18%
18%
7%
2%
Not enough skilled resources
Accessing and preparing data
Limited budget
Deploying the results in operational
systems
Hard to build and maintain
Algorithms inappropriate for our uses
Lack of support/involvement from senior
leadership
None - we have no barriers to using
machine learning
Other
% of respondents (n=745)

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“What scares me so much now isn’t the nature of the data
that we’re… storing with partners. What worries me is how
fragmented it is… we’re dealing with 40, 50, 60, 100 SaaS
providers, all which have little pieces of our customer data.
If we had to go and find all of that data, I’m not sure we’d
know where it was.”
I T E N G I N E E R I N G
M A N A G E R

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Regional differences
Q22. And what are your organization’s most significant barriers to using machine learning?
Source: 451 Research, Voice of the Enterprise: Artificial Intelligence/Machine Learning 2018 H2
32%
47%
Finding and
accessing
data is a
greater
challenge in
EMEA than in
North America

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The
importance
of AI/ML to
analytics
projects is
more widely
recognized in
North America
than EMEA
Regional differences
26%
18%
Q. To what extent do you agree or disagree with each of the following statements? - ‘AI and machine learning are an
important component of my organization's data platform/analytics initiative(s)’(n=548)
Source: 451 Research's Voice of the Enterprise: Data Platforms and Analytics, Q1 2019

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A higher
proportion of
enterprises in
North America
are currently
executing or
developing
digital
transformation
strategies
Regional differences
95%
88%
Q. Which of the following best describes the status of your organization's digital transformation strategy? (n=548)
Source: 451 Research's Voice of the Enterprise: Data Platforms and Analytics, Q1 2019

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“[Digital transformation] is going to give
[the organization] the ability to focus more and
more on being proactive. Right now, they're very
reactive, and they need to get out of that mode
and be proactive.”
I T E N G I N E E R I N G M A N A G E R
M A N U FA C T U R I N G

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Improving operational efficiency
Improving customer experience
Gaining competitive advantage
Responding faster to opportunities
and threats
Increased sales
Reducing errors and mistakes in
business
Reducing risk exposure
Lowering costs
Regional differences
Q. What are the most significant benefits your organization is realizing from its
use of data platforms and analytics? (n=548)
Source: 451 Research's Voice of the Enterprise: Data Platforms and Analytics, Q1 2019
20%
22%
26%
26%
31%
34%
50%
59%
33%
23%
26%
27%
26%
28%
42%
51%

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Regional differences
19%
10%
Q. To what extent would you say strategic decisions are currently 'data-driven' at your organization? (n=548)
Source: 451 Research's Voice of the Enterprise: Data Platforms and Analytics, Q1 2019
A significantly
higher
proportion of
enterprises in
North America
consider
themselves
amongst
the most
‘data-driven’

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“Everybody is able to get to data now; it’s really
about how fast and how efficiently you can get to it
and use it to get the answers that you need.
So, that's really what's driving us.”
M I D - L E V E L M A N A G E R

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Benefits of natural language search
for finding and accessing data
Support for natural-language queries lowers the
barriers to analytic insight
Enables C-level executives or other less technical
personnel to intuitively query data
Search is particularly important in surfacing
insight from unstructured data

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The most
data-driven
companies
stand to
gain the
most from
adoption
of natural
language query
interfaces 32%
13%
14%
16%
16%
26%
27%
33%
9%
24%
31%
31%
34%
45%
39%
43%
Not interested in any
AR/VR tools
Speech interfaces
Natural language generation
Natural language text interfaces
Automated visualization
Automated recommendations
Real-time alerts
0% 10% 20% 30% 40% 50%
Data Drivers All respondents % of respondents (n=713)
Q. Which of the following newer technologies - if any - is your organization most interested in using to interface with data?
Q. To what extent would you say strategic decisions are currently 'data-driven' at your organization? (n=780)
Source: 451 Research's Voice of the Enterprise: Data Platforms and Analytics, Q1 2019

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Q13. Do the following types of data feed your AI/ML workloads? :- Unstructured textual data (e.g., office files)
Source: 451 Research's Voice of the Enterprise: AI and Machine Learning, Infrastructure 2019
Unstructured and
semi-structured
data is set to
play a greater
role in machine
learning projects
42% 32% 14% 6%
Use today With 12 months
12-24
months
2-3
years
88% of enterprises are expect to be using
unstructured textual data for AI/ML with
two years

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Summary/recommendations
Digital transformation is real and is happening now – 92% of enterprises are currently executing
or developing digital transformation strategies. Those with no strategy, or even in the
consideration stage, risk getting left behind.
The most data-driven companies are also taking a lead when it comes to digital transformation.
Enterprises that are behind with regards to making data-driven decisions (especially in EMEA)
are at a distinct disadvantage.
Finding and accessing data is a significant barrier to analytics and machine learning, and is a
greater barrier to companies in EMEA than in North America.
Natural language search lowers the barriers to analytic insight, and is particularly important in
surfacing insight from unstructured data, which is set to play a greater role in machine learning
projects.
The most data-driven companies, the same ones taking a lead when it comes to digital
transformation, also stand to gain the most from adoption of natural language query interfaces.

24. TRANSITIONING TO AGILE DIGITAL
IS CHALLENGING

25. Data Scientist Driven Activity Business Alignment
Meet the “Last Mile Problem”
RawData
Velocity
Variety
Veracity
Volume
Value
DataCollection
ETL, Capture,
Archive, Move
& Manage
DataLake
Store to HDFS,
Azure or AWS,
Break down
for processing
LakesideUnderstanding
Select,
Describe,
Explore, Verify
Quality, Parse,
Clean, Join &
Structure
DataRefinery
Model,
Aggregate &
Transform
Multi-
Structured
Data (JSON)
Transactions&Interactions
Retain runtime
& historical
models for
ongoing
refinement
ApplicationConstruction
Contextual
Business Use
Cases
Cubes for
Navigation &
Mining
Intelligence&Analysis
User Specific
Visualization,
Presentation,
Dashboards &
Reporting
BigDataManagementBigDataManagementBigDataManagementBigDataManagement
&Analytics&Analytics&Analytics&Analytics
❶ ❷ ❸ ❹ ❺ ❻ ❼ ❽
Problems with Activating Data for Digital Transformation

26. Data Scientist Driven The Last Mile Problem
Reduced Time to Value
Quantifiable & Faster
ROI
ManagedData
Human
Process
Application
IndexPipelines
Ingest, Parse,
Index, Entity
Map, Cluster
ML Rules &
Algorithms
Ontology
Enrichment
BehavioralEnrichment
Insight,
Relevancy
Ranking,
Signals
Processing
Real-time
Learning
ApplicationVisualization
Dynamic
Prototype,
Build &
Visualization
Interactive
Dashboarding
Search,Discovery&Search,Discovery&Search,Discovery&Search,Discovery&
OperationalAIOperationalAIOperationalAIOperationalAI
RawData
Velocity
Variety
Veracity
Volume
Value
DataCollection
ETL, Capture,
Archive, Move
& Manage
DataLake
Store to HDFS,
Azure or AWS,
Break down
for processing
LakesideUnderstanding
Select,
Describe,
Explore, Verify
Quality, Parse,
Clean, Join &
Structure
DataRefinery
Model,
Aggregate &
Transform
Multi-
Structured
Data (JSON)
Transactions&Interactions
Retain runtime
& historical
models for
ongoing
refinement
ApplicationConstruction
Contextual
Business Use
Cases
Cubes for
Navigation &
Mining
Intelligence&Analysis
User Specific
Visualization,
Presentation,
Dashboards &
Reporting
BigDataManagementBigDataManagementBigDataManagementBigDataManagement
&Analytics&Analytics&Analytics&Analytics
❶ ❷ ❸ ❹ ❺ ❻ ❼ ❽
❶ ❷ ❸ ❹
Direct Alignment with Business Needs
Problems with Activating Data for Digital Transformation

27. The Digital Transformation
Question?
To AI or not to AI?
The Digital Transformation
Question?
To AI or not to AI?

28. What goes wrong with AI in Digital
Transformation Projects?
 BAD DATA IN = BAD RECOMMENDATION OUT. FALSE
POSITIVES REDUCING CONFIDENCE IN MACHINE
LEARNING
 LONGER TERM COMPLACENT DEPENDENCIES ON
“INFALLIBLE” MACHINE LEARNING
 IN ABILITY FOR AI TO TAKE ACCOUNT OF THE WIDER
CONTEXT OF BUSINESS NEEDS / OUTCOMES

29. Challenges Adopting AI & ML
IT Teams
May misunderstand both the business
objectives and the machine learning model.
Business Leaders
Define the business goals that want to pursue
with AI, but they don’t usually understand the
challenges and limitations of building ML models
Data Scientists
Understand machine learning, but they might
not truly understand the business objectives
and they may build “hungry” models that
consume too many IT resources
1 2
3

30. Artificial Narrow Intelligence (ANI)
is the AI that exists in our world today,
programmed to perform a single
task — whether it’s checking the
weather, being able to play chess, or
analyzing raw data to write journalistic
reports.
Artificial General intelligence (AGI) or
refers to machines that exhibit human
intelligence. In other words, AGI can
successfully perform any intellectual task
that a human being can being conscious,
sentient, and driven by emotion and self-
awareness.
ANI systems can process data and
complete tasks at a significantly
quicker pace than any human being
can, which has enabled us to
improve our overall productivity,
efficiency, and quality of life e.g.
assist doctors to make data-driven
decisions, making healthcare better
AGI is expected to be able to reason,
solve problems, make judgements
under uncertainty, plan, learn,
integrate prior knowledge in decision-
making, and be innovative,
imaginative and creative.
It’s all about Operationalizing AI

31. Machine learning is a method of data analysis
that automates analytical model building. It is
a branch of artificial intelligence based on the
idea that systems can learn from data, identify
patterns and make decisions with minimal
human intervention.
SAS INSTITUTE INC. 2019

32. How to Implement Machine Learning
A FRAMEWORK FOR APPLYING AI IN THE ENTERPRISE
GARTNER INC. 2017

33. ML-Powered Search Maturity Curve
Self-Learning
Taxonomies/Entity
Extraction
Automated
Relevancy Tuning
Query Intent
Prediction
Keyword Search

34. Many AI tools’ designs
start with just data, not the
human question. Thus, a
great AI platform focuses
on an answer to the
question by using search,
rather than just being tool
based.
AI AUTHORITY
CHAO HAN, HEAD OF DATA SCIENCE, LUCIDWORKS
SEPT 2018

35. FILTER
VISUALIZATION
ACTIVITY
CONTENT
INDEX NATURAL
LANGUAGE
BOOSTED
RESULTS
MACHINE
LEARNING
QUERY RULE
MATCHING
USER
SIGNALS
FACET, TOPIC
& CLUSTER
D ATA
Human
Generated
System
Generated
Application
Generated
S O L U T I O N
Digital
Workplace
NATURAL
LANGUAGE
MACHINE
LEARNING
QUERY RULE
MATCHING
USER
SIGNALS
FACET, TOPIC
& CLUSTER

36. MACHINE LEARNING
HYPER PERSONALIZATION
Go beyond using static rules and profiles to dynamically
customize the experience based on data signals.
Use AI and machine learning to predict user intent and give
employees the insights they need, when they need them.
Maximize the opportunities to tailor content that fits
each and every employee’s wants and needs.
Explore
Curate
Integrate

37. Use Case Examples
Digital Commerce
Predictive
Merchandising
Catalog Search
Sentiment Analysis
Personal
Assistant/Chatbots
Digital Workplace
Scientific Research
Call Center
Prioritization
Support Deflection
Natural Language
Search
Fraud Detection

38. Advanced connectors and AI enrichment,
delivered by intuitive applications created with App
Studio
D ATA
Any format,
any platform
S O L U T I O N
Personalized
to meet needs of
each unique
employee
FUSION
Server
FUSION
AI
FUSION
App Studio
FUSION
AI
F U S I O N P L AT F O R M
Human
Generated
System
Generated
Application
Generated
Digital
Workplace

39. FUSION
Server
FUSION
AI
FUSION
App Studio
FUSION
AI
NLP: NER, phrases, POS
Document classification
Anomaly detection
Clustering
Topic detection
Search engine &
data processing
Connectors
ETL pipelines
Scheduling & alerting
SQL engine
Rules engine
Query pipelines
Query intent detector
Automatic relevancy
Signals & query analytics
Recommenders
A/B testing
Modular components
Stateless architecture
User-focused experience
Geospatial mapping
Results preview
Rapid prototyping
S C A L A B L E O P E R A T I O N S
SECURITYCDCRCLOUDSCALABLEEXTENSIBLE

40. ML for Anomaly Detection

41. Signals ML for Rule Rewrite

42. D ATA
Any format,
any platform
Human
Generated
System
Generated
Application
Generated S O L U T I O N
A S S E M B LY
Digital
Workplace
FUSION
INDEX
FUSION
INDEX
FUSION
QUERY
FUSION
QUERY
FUSION
AI
RULE
ENTITY
ML
NLP
BOOST
SIGNAL
R A P I D A S S E M B LY P L AT F O R M
FUSION
Q&A System
FUSION
Risk Analysis
FUSION
Data Discovery
A P P L I C AT I O N T E M P L AT E S

43. R E A L - L I F E E X A M P L E S
Lucidworks Fusion
powers connected
experiences
Customer
Care
Investigatio
n
Employee
Q&A
Supply
Efficiency
Compliance
& Audit

44. CASE STUDY
Single, global source of truth
in their knowledge management application
An accurate picture
of client interactions and expertise
Content disambiguation
within PwC’s app – from sources like
Wolphram Alpha and Wikipedia
10M
DOCUMENTS
MANAGED
40K
EMPLOYEES

45. CASE STUDY
Better customer support
Putting the right information in front of customers in fewer clicks
Improved support calls
Shorter wait times, and a more engaged support
Reinvestment of savings
200%
INCREASE IN CTR
50K
FEWER SUPPORT
TICKETS
91%
REDUCTION IN TCO

46. CASE STUDY
More searches converted
into sold tickets
Full-site search on iOS and
Android mobile apps
33%
INCREASE IN
CONVERSION
63%
IMPROVEMENT IN
VISITORS
15%
REVENUE ATTRIBUTED
TO SEARCH

47. THANK YOU

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Q & A

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