Radically New Approach to Strategic Consulting
Institute for Statistical Studies and Economics of Knowledge HSE has developed iFORA (Intelligent Foresight Analytics) – a unique big data mining system. It is based on the cutting-edge technologies in machine learning and semantic analysis. iFORA analyses big data to provide decision-makers with objective and valuable insights.
The main products of the iFORA system are visual and analytical data showcases and statistical tables.
The system analyses documents in various languages, including English, Russian, Chinese and other Cyrillic & Roman languages.
The iFORA system is noted:
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In 2020 iFORA was featured in Nature as an effective decision support tool
(Nature, 2020, Vol. 583)
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In 2018 the OECD noted iFORA as a successful initiative in the field of digitalization of science(OECD STI Outlook 2018)
iFORA in numbers
4 steps towards high-level analytics
The information base of the iFORA system contains more than 800 million documents
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> 390 million
Scientific publications
> 4 million
Research projects / grants international and national programmes / foundations
> 300 thousand
R&D reports
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> 150 million
Patents
> 100 thousand
Scientific conferences
> 5 thousand
Educational programmes
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> 2 million
Vacancies
> 3.5 million
Public procurement data
> 3.5 million
Social networks
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> 28 million
Market analytics and professional media
> 55 million
Popular science media
iFORA presentation
Functionality
iFORA uses advanced semantic analysis, machine learning and AI algorithms and solutions to provide recommendations for government agencies, companies, universities and research centres in various areas.
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Trends
- Trend spotting and trend response
- Mapping strategic structural shifts in S&T, innovation, education, markets, industries
- Lifecycle and latent change analysis
- Identification of S&T contribution to tackling the Grand Challenges
- Measurement of technology impact on the economy sectors and organisations
Markets
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Quantitative market estimates and forecasts
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Technology maturity measurement
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Technology and product portfolios
- Discovery of new segments in emerging markets
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Digital tracing of regional and local markets
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Start-ups discovery and mapping
Project management
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Operative competitive intelligence for project decision-making
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Evaluation of projects and bids
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Identification of individuals and teams for project implementation
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Technology and management roadmaps
Policy evaluation
- Recommendations on government programmes
- Ontologies of national strategic planning
- Legal AI, gap analysis
- Comparative agenda analysis
- Procurement mapping
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Benchmarking and risk assessment
- Assessment of competitiveness and performance across various types of entities
- Fact-based reputation analysis
- Sentiment and content analysis
- Media impact performance indicators
- Risk management
- Independent rankings
Forecasts
- Future events and emerging alternatives
- Critical timelines to 2100
- Textual extraction and summarization of expert opinions
- Quantitative consensus forecasts
- Tracing and projecting properties of products and technologies
Networks
- Network analysis
- Identification of competence carriers
- Cross-sectoral interlinking
- Cooperation and affiliation analysis
iFORA has been successfully employed
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Long-term forecasts of scientific, technological, innovative, social and economic development
Research fronts, patent landscapes
Science, technology and innovation policies
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Strategies, programmes and roadmaps of industrial and regional development
Strategic foresight and corporate planning
Performance and efficiency of countries, markets and companies
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Personnel training and skill adaptation programmes
Marketing strategies
Reputation assessment for partnership and contractual decisions
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Companies and brands valuation
Investment analysis and portfolio management
Validation of research findings, forecasts and projects
Publications & Conferences
List of publications
Conferences
Foreign partners
Contacts
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E-mail: issek@hse.ru, ifora@hse.ru Tel.: +7 (495) 621-28-73 |
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The website is organized in the framework of a research grant funded by the Ministry of Science and Higher Education of the Russian Federation (grant ID: 075-15-2022-325)