Will the semantic web be a threat or opportunity for information professionals? How do we plan for the future in the face of disruptive change and high uncertainty? This presentation explains how scenario analysis can bring some clarity to the future. We apply these methods to the question of the semantic web to understand where the threats and opportunities exist for information professionals.

1. Using Scenario
Analysis to
Predict the Future
of the
Semantic Web
August Jackson
Ernst & Young

2. I work for Ernst & Young
One of the largest audit, tax and advisory firms in
the world
This material does not constitute advice related to
any of the services the firm provides
Mentions of firm clients do not constitute an
endorsement or recommendation to invest
All opinions expressed here are solely my own
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3. “It's tough to make
predictions, especially about the
future.”
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4. Semantic Web: Threat or
Opportunity?
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5. Disruptive technologies follow non-
linear improvement curves
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6. Questions?

7. Scenario analysis enabled us
consider possible futures and create
meaningful early warning systems
High Significant
Disagreement
Uncertainty Events
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8. A clear problem statement is critical to
a quality scenario analysis
Subject Domain Customer Segment
Timeframe Geography
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9. STEEP framework aligns trends and
uncertainties
Environment
Social Technological Economic Political
al
• Demography • IT • Drivers of • Air quality • Laws
• Family life • Biotechnology growth • Water quality • Regulation
• Public health • Materials • Inhibitors of • Arable land • Elections
• Religion science growth • Climate trends • Political power
• Culture • Manufacturing • Monetary • Resource distribution
• Beliefs environment availability • Conflict
• Business • Litigation
cycles • International
• Wealth Relations
distribution
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10. Seek a diversity of expertise to identify
the critical trends and uncertainties
Internal Sources
Sales and Marketing
Management R&D
External Sources
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11. Isolate the two or three critical
uncertainties to create three to seven
scenarios Extreme State Z Extreme State A
Critical Uncertainty 1
Extreme State A Extreme State Z
Critical Uncertainty 2
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12. Questions?

13. Here’s the problem scope we’ll
address in a very high-level analysis
Subject Domain:
Application of Customer Segment:
semantic Knowledge
technologies to Professionals and
models for research their Stakeholders
and analysis
Timeframe: Geography:
3-5 years Global
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14. STEEP framework aligns trends and
uncertainties
Environment
Social Technological Economic Political
al
• Increased use of • Inclusion of • High valuation of Drive to conserve • Public budget
social media semantic “big data” energy with smart constraints drive
• Corporate standards in companies grids and smart push for cost-
adoption of applications • Cloud-based appliances savings
industry • Adoption of non- platforms move IT • Regulations
standards and relational spend from capex related to
general databases to opex personal data
ontologies • Adoption of • Availability of • Desire to increase
• Growing natural language semantic transparency of
frustration of time processing expertise some government
spent seeking • Availability of • New revenue operations
information easy-to-use tools models and
• Consumerization to create services based on
of IT ontologies data
• Ubiquitous high-
speed network
connectivity
• Growing volumes
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15. STEEP framework aligns trends and
uncertainties
Environment
Social Technological Economic Political
al
• Increased use of • Inclusion of • Valuation of “big Drive to conserve • Public budget
social media semantic data” companies energy with smart constraints drive
• Corporate standards in • Cloud-based grids and smart push for cost-
adoption of applications platforms move IT appliances savings
industry standard • Adoption of non- spend from capex • Regulations
ontologies relational to opex related to data
• Growing databases • Availability of privacy and
frustration of time • Adoption of semantic protection
spent seeking natural language expertise • Desire to increase
information processing • New revenue transparency of
• Consumerization • Availability of models and some government
of IT easy-to-use services based on operations
ontology editing data
software
• Ubiquitous high-
speed network
connectivity
• Growing volumes
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16. 1. Availability of semantic expertise
2. Adoption of natural language processing
3. Corporate adoption of industry standard ontologies
4. Inclusion of semantic standards in applications
5. Adoption of non-relational databases
6. Availability of easy-to-use ontology editing software
7. Valuation of “big data” companies
8. New revenue models and services based on data
9. Regulations related to data privacy and protection
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17. We can describe distinct futures
based on our chosen uncertainties
Availability of semantic expertise
Expertise is Rare Expertise is Common
NLP Limited Adoption NLP is Ubiquitous
@8of12 Adoption of natural language processing #slacid

18. We can describe distinct futures
based on our chosen uncertainties
Availability of semantic expertise
Expertise is Rare Expertise is Common
Smart Content
• Nearly all published sources
are semantically modeled
• Complex semantic models with
deep nuance and meaning
• New models for advanced
search, research and analysis
NLP Limited Adoption NLP is Ubiquitous
@8of12 Adoption of natural language processing #slacid

19. We can describe distinct futures
based on our chosen uncertainties
Availability of semantic expertise
Expertise is Rare Expertise is Common
Smart Content
• Nearly all published sources are
semantically modeled
• Complex semantic models with
deep nuance and meaning
• New models for advanced
search, research and analysis
Big Data and Little Else
• Semantic models applied
almost exclusively to structured
data
• Limited application of semantic-
based tools for written text
• Existing search and analysis
models prevail
NLP Limited Adoption NLP is Ubiquitous
@8of12 Adoption of natural language processing #slacid

20. We can describe distinct futures
based on our chosen uncertainties
Availability of semantic expertise
Expertise is Rare Expertise is Common
Big Data Everywhere Smart Content
• Semantic expertise applied • Nearly all published sources are
primarily to structured data semantically modeled
• Meaningful ontological models • Complex semantic models with
for structured data become the deep nuance and meaning
norm • New models for advanced
• New search and analysis search, research and analysis
models for data, less so for text
Big Data and Little Else
• Semantic models applied
almost exclusively to structured
data
• Limited application of semantic-
based tools for written text
• Existing search and analysis
models prevail
NLP Limited Adoption NLP is Ubiquitous
@8of12 Adoption of natural language processing #slacid

21. We can describe distinct futures
based on our chosen uncertainties
Availability of semantic expertise
Expertise is Rare Expertise is Common
Big Data Everywhere Smart Content
• Semantic expertise applied • Nearly all published sources are
primarily to structured data semantically modeled
• Meaningful ontological models • Complex semantic models with
for structured data become the deep nuance and meaning
norm • New models for advanced
• New search and analysis search, research and analysis
models for data, less so for text
Big Data and Little Else Statistics = Meaning
• Semantic models applied • Building ontologies is expensive
almost exclusively to structured and requires clear ROI
data • Reliance on statistical methods
• Limited application of semantic- for concept mapping
based tools for written text • New search and analysis
• Existing search and analysis methods based on word co-
models prevail occurrence
NLP Limited Adoption NLP is Ubiquitous
@8of12 Adoption of natural language processing #slacid

22. Implication wheels can be used to
flesh out the story of each scenario
Hypothesi
s
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23. Implication wheels can be used to
flesh out the story of each scenario
Implicatio
n
Hypothesi
s
Implicatio
n
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24. Implication wheels can be used to
flesh out the story of each scenario
Consequen
ce
Implication
Consequen
Consequen ce
ce
Hypothesis
Implication
Consequen
ce
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25. What could the “Smart Content” mean
for news media?
Nearly all
published
sources are
semanticall
y modeled
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26. “Smart Content” would see changes
the substance and medium of news
Publication
s offer APIs
Nearly all
published
Hypothes
sources are
is
semanticall
y modeled
“Data
Journalism”
becomes
standard
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27. The changes wrought by Smart
Content will have impact far and wide
Copyright
Challenge
s
Publication
s offer APIs
Nearly all New
Expectatio
published Monetizatio
ns of Hypothes n Models
Empirical sources are
Evidence is
semanticall
y modeled
“Data
Implicatio
Journalism”
n
becomes
Interactive
standard Infographi
cs
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28. Write the future history of each
scenario to drive your early warning
tracking effort
Universities will People will develop
educate people rich semantic Smart
about semantics models and tools Content
Today Software developers 3 -5
will release years
applications with hence
NLP capabilities
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29. Questions?

30. Thank You!
August Jackson
august (at) augustjackson (dot) net
http://augustjackson.net
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