The role of BI in content strategies
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This document summarizes a presentation on the role of business intelligence in content strategies. It discusses how organizations are increasingly dealing with large amounts of unstructured data and how semantic technologies, enterprise search, and text analytics can help analyze this content. The presentation outlines these topics and trends in content analysis like using non-relational databases and analyzing social media data. It aims to show how these tools can expand an organization's analytical capabilities.
The role of BI in content strategies
- 1. Technology Evaluation Centers The Role of Business Intelligence in Content Strategies Jorge García, Research Analyst Info360 Conference, 2011. Washington, D.C. www.technologyevaluation.com
- 2. Technology Evaluation Centers Expert Panel Fernando Mesa Principal Technologist MarkLogic Matt Kodama VP of Product Management Global Public Sector Endeca Ian Hersey Chief Technology Officer Attensity
- 3. Technology Evaluation Centers Outline 1. Introduction 2. Semantic technologies (Fernando Mesa, MarkLogic) 3. Enterprise search (Matt Kodama, Endeca) 4. Text analytics (Ian Hersey, Attensity) 5. Q&A session
- 4. Technology Evaluation Centers Content Is King? “Content is where I expect much of the real money will be made on the Internet, just as it was in broadcasting.” — Bill Gates, 1996 Much of this content is unstructured data. Shoutmeloud.com
- 5. Technology Evaluation Centers Unstructured Data, an Extreme Case http://www.popularwealth.com
- 6. Technology Evaluation Centers Unstructured Data “unstructured data, such as the natural-language text of documents or pictorial images” — Encyclopedia Britannica ” Source: Rational Retention (http://www.rreurope.com)
- 7. Technology Evaluation Centers Unstructured Data Explosion Many organizations have come to realize that valuable information is contained in “unstructured documents” (PDFs, plain text, …)
- 8. Technology Evaluation Centers Common Unstructured Data Types “The challenge of modeling and making sense of information content falls in the analytic rather than data management domain.” — Seth Grimes, InformationWeek, 2005 The problem is finding a way to analyze the data
- 9. Technology Evaluation Centers CMS and BI Content Management Business Intelligence (BI) Systems (CMSs) New types of analysis tools to analyze large volumes of unstructured data
- 10. Technology Evaluation Centers Content and BI: Closing the Gap • Adoption of semantic publishing frameworks e.g., Resource Description Framework (RDF) and extensible markup language (XML) • Evolution of text analysis techniques e.g., sentiment analysis and text-mining techniques • Evolution of software development frameworks e.g., service-oriented architecture (SOA)
- 11. Technology Evaluation Centers BI for Content: Applications • Marketing Analyzing unstructured data from a competitive environment. • Fraud Detection Analyzing suspicious behaviors within financial documents and other unstructured data. • Legal Industry Locating and analyzing relevant data within legal documents. • Homeland Defense Looking for suspicious patterns within thousands of immigration sources and documents.
- 12. Technology Evaluation Centers BI for Content Analysis: Advantages • Expand analysis potential • Enable easier content search within corporate information • Expand analysis capabilities to a broader number of users
- 13. Technology Evaluation Centers Main Technologies Semantic Technologies Standards, frameworks, and software deal with unstructured information — from storage and categorization to unstructured data analysis. http://cscie153.dce.harvard.edu/lecture_notes /2008/20080916/handout.html
- 14. Technology Evaluation Centers Some Basic Functionalities Enterprise Search Comparable to Web search processes: information is collected and integrated, processed, and indexed. Devoted to easing the search for particular content within an organization.
- 15. Technology Evaluation Centers Some Basic Functionalities Text Analytics Models the structure of plain text for analysis purposes. •Data and text mining •Lexical analysis •Predictive analytics
- 16. Technology Evaluation Centers Trends to Watch For • Data Storage and Exploitation Non-relational databases for managing content-based information. • Social Media Data and Analysis Organizations are incorporating social media into their analysis efforts. • Integrated Collaboration More user-centric systems, the ability to work in groups (share and communicate). • Geo-BI Geolocalization capabilities.
- 17. Technology Evaluation Centers Thank You Jorge García, Research Analyst jgarcia@technologyevaluation.com www.technologyevaluation.com