How We Incrementally Improved Search
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This document outlines the incremental steps taken to improve search at an organization. It began with an out-of-the-box search tool that indexed only a fraction of content and provided many dead ends. The vision was to create a single search box that could search all content in a Google-like interface. A roadmap was created to prioritize integrating the most used systems starting with low hanging fruit. Implementation involved customizing the search interface, adding APIs to other systems, and various approaches like blended, brokered and query resubmit searches. Analytics tracked usage and best search terms. Benefits included a single access point and overcoming limitations of individual tools. Challenges remain around complex permissions, design convergence and integrating
How We Incrementally Improved Search
- 1. How We Incrementally Improved Search Ravi Mynampaty @ravimynampaty
- 2. Agenda Background • Roadmap • Implementation • Analytics • Benefits • Challenges • Next Steps
- 3. Background: A few years ago… • Out-of-the-box Ultraseek • No optimization, no customization • Fraction of HBS content indexed / searchable • Many dead ends • Proliferation of different search tools • User sentiment • “search sucks” • “why can’t it be more like Google”
- 4. Background: Our Vision • One Search Box to Rule Them All • The long term goal: enterprise search • One-stop searching • Google-like simplicity • Handle refinement / navigation on results page
- 5. Agenda • Background Roadmap • Implementation • Analytics • Benefits • Challenges • Next Steps
- 6. Roadmap: Preliminary Steps • Inventory document collections • Inventory search-type tools • Of the above, identify – most heavily used – strategically significant – high impact – Low Hanging Fruit (LHF)
- 7. Roadmap: Implementation Plan • Prioritize tasks by ease of content access and implementation (LHF) • Develop timeline • Build prototypes and iterate the design
- 8. Agenda • Background • Roadmap Implementation • Analytics • Benefits • Challenges • Next Steps
- 9. Implementation: How we built it • Customized Ultraseek’s results display code • Worked with owners of software apps –Provided JSON APIs –Allowed us to spider their app/repository • HTML is the API !! • In other words: No rocket science involved
- 10. Implementation: Three Integration Approaches • Blended Search (e.g., Faculty/Staff Directory) • Brokered Query (e.g., Video Catalog) • Query Resubmit (e.g., Alumni Directory)
- 11. Implementation: Blended Search Spider HBS web content outside of HBS.EDU • Harbus.org (student newspaper) • Club and affiliated sites Spider HBS content located in other applications • Faculty and staff phone book • Alumni Class Notes application
- 12. Implementation : Optimize and clean up search indexes Work with content owners to create good HTML page titles • Faculty Publications pages • 20th Century Leadership database • Address MS-Office / PDF files too Eliminating duplicate search results / use filters Adjusting Relevance per collection / source / file path
- 13. Implementation : Create Best Bets Top 10 Queries Oct – Dec
- 14. Implementation: Unify Blended Search + Query Resubmit
- 15. Query refinement options (Blended Search) Query resubmit options “Integration-lite”
- 16. Implementation: Expanding the Net w/ Brokered Search • When direct indexing isn’t practical Harvard.edu search HBS VideoTools (intranet only) MBA Event Calendar (intranet only) • A query is handed off to another search engine • Results are returned “behind the scenes” as JavaScript Object Notation (JSON) / Python • Ajax-like support of asynchronous search processes
- 17. Implementation: Brokered Query in Action
- 18. Implementation: Brokered Query in Action
- 19. Implementation: Brokered Query in Action
- 20. Implementation: One-offs • Software Dev Docs (cmd line) $ find ./software/docs –name ‘*html’ | xargs grep -i oracle | less (returns 100s of docs) • Built web-based search UI
- 21. Agenda • Background • Roadmap • Implementation Analytics • Benefits • Challenges • Next Steps
- 22. Analytics: Tracking Usage of Features
- 23. Analytics: Tracking Best Bets
- 24. Analytics: Tracking Best Bets
- 25. Agenda • Background • Roadmap • Implementation • Analytics Benefits • Challenges • Next Steps
- 26. Benefits • Single point of access for various repositories • Shortcomings of underlying tools overcome • Better access to content from rest of Harvard • Traffic boost to e-commerce site
- 27. Agenda • Background • Roadmap • Implementation • Analytics • Benefits Challenges • Next Steps
- 28. Challenges • Search is never done • Complex permissions issues • SERP design convergence • SharePoint
- 29. Agenda • Background • Roadmap • Implementation • Analytics • Benefits • Challenges Next Steps
- 30. Next Steps • Tackling the mixed-mode situation • Integration with taxonomies • Search experience within HBS applications • Faceted search where rich metadata available • Analytics feeding website design and vocabulary development
- 31. Conclusion • Tactical, iterative approach enabled significant progress • Implementing simpler features/tweaks may have higher impact • Your existing search engine may have more gas in it than you realize