Using Scenario Analysis to Predict the Future of the Semantic Web

Using Scenario Analysis toPredict the Future of the Semantic Web August Jackson Ernst & Young

I work for Ernst & YoungOne of the largest audit, tax and advisory firms inthe worldThis material does not constitute advice related toany of the services the firm providesMentions of firm clients do not constitute anendorsement or recommendation to investAll opinions expressed here are solely my own@8of12 #slacid

“Its tough to make predictions, especially about the future.”@8of12 #slacid

Semantic Web: Threat or Opportunity?@8of12 #slacid

Disruptive technologies follow non- linear improvement curves@8of12 #slacid

Questions?

Scenario analysis enabled us consider possible futures and create meaningful early warning systems High Significant Disagreement Uncertainty Events@8of12 #slacid

A clear problem statement is critical to a quality scenario analysis Subject Domain Customer Segment Timeframe Geography@8of12 #slacid

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@8of12 #slacid

Seek a diversity of expertise to identify the critical trends and uncertainties Internal Sources Sales and Marketing Management R&D External Sources@8of12 Customers Academics #slacid

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@8of12 #slacid

Questions?

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@8of12 #slacid

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@8of12 of data #slacid

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@8of12 of data #slacid

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@8of12 #slacid

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

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

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

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

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

Implication wheels can be used to flesh out the story of each scenario Hypothesi s@8of12 #slacid

Implication wheels can be used to flesh out the story of each scenario Implicatio n Hypothesi s Implicatio n@8of12 #slacid

Implication wheels can be used to flesh out the story of each scenario Consequen ce Implication Consequen Consequen ce ce Hypothesis Implication Consequen ce@8of12 #slacid

What could the “Smart Content” mean for news media? Nearly all published sources are semanticall y modeled@8of12 #slacid

“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@8of12 #slacid

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@8of12 #slacid

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@8of12 #slacid

Questions?

Thank You!August Jacksonaugust (at) augustjackson (dot) nethttp://augustjackson.net@8of12

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Using Scenario Analysis to Predict the Future of the Semantic Web

by August Jackson on Jul 13, 2012

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