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 ï Background âą Roadmap âą Implementation âą Analytics âą Benefits âą Challenges âą Next Steps
- 3. Background: A few years ago⊠⹠Out-of-the-box Ultraseek âą No optimization, no customization âą Fraction of HBS content indexed / searchable âą Many dead ends âą Proliferation of different search tools âą User sentiment âą âsearch sucksâ âą âwhy canât it be more like Googleâ
- 4. Background: Our Vision âą One Search Box to Rule Them All âą The long term goal: enterprise search âą One-stop searching âą Google-like simplicity âą Handle refinement / navigation on results page
- 5. Agenda âą Background ï Roadmap âą Implementation âą Analytics âą Benefits âą Challenges âą Next Steps
- 6. Roadmap: Preliminary Steps âą Inventory document collections âą Inventory search-type tools âą Of the above, identify â most heavily used â strategically significant â high impact â Low Hanging Fruit (LHF)
- 7. Roadmap: Implementation Plan âą Prioritize tasks by ease of content access and implementation (LHF) âą Develop timeline âą Build prototypes and iterate the design
- 8. Agenda âą Background âą Roadmap ï Implementation âą Analytics âą Benefits âą Challenges âą Next Steps
- 9. Implementation: How we built it âą Customized Ultraseekâs results display code âą Worked with owners of software apps âProvided JSON APIs âAllowed us to spider their app/repository âą HTML is the API !! âą In other words: No rocket science involved
- 10. Implementation: Three Integration Approaches âą Blended Search (e.g., Faculty/Staff Directory) âą Brokered Query (e.g., Video Catalog) âą Query Resubmit (e.g., Alumni Directory)
- 11. Implementation: Blended Search Spider HBS web content outside of HBS.EDU âą Harbus.org (student newspaper) âą Club and affiliated sites Spider HBS content located in other applications âą Faculty and staff phone book âą Alumni Class Notes application
- 12. Implementation : Optimize and clean up search indexes Work with content owners to create good HTML page titles âą Faculty Publications pages âą 20th Century Leadership database âą 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 â Dec
- 14. Implementation: Unify Blended Search + Query Resubmit
- 15. Query refinement options (Blended Search) Query resubmit options âIntegration-liteâ
- 16. Implementation: Expanding the Net w/ Brokered Search âą When direct indexing isnât practical ï Harvard.edu search ï HBS VideoTools (intranet only) ï MBA Event Calendar (intranet only) âą A query is handed off to another search engine âą Results are returned âbehind the scenesâ as JavaScript Object Notation (JSON) / Python âą 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 âą Software Dev Docs (cmd line) $ find ./software/docs âname â*htmlâ | xargs grep -i oracle | less (returns 100s of docs) âą Built web-based search UI
- 21. Agenda âą Background âą Roadmap âą Implementation ï Analytics âą Benefits âą Challenges âą Next Steps
- 22. Analytics: Tracking Usage of Features
- 23. Analytics: Tracking Best Bets
- 24. Analytics: Tracking Best Bets
- 25. Agenda âą Background âą Roadmap âą Implementation âą Analytics ï Benefits âą Challenges âą Next Steps
- 26. Benefits âą Single point of access for various repositories âą Shortcomings of underlying tools overcome âą Better access to content from rest of Harvard âą Traffic boost to e-commerce site
- 27. Agenda âą Background âą Roadmap âą Implementation âą Analytics âą Benefits ï Challenges âą Next Steps
- 28. Challenges âą Search is never done âą Complex permissions issues âą SERP design convergence âą SharePoint
- 29. Agenda âą Background âą Roadmap âą Implementation âą Analytics âą Benefits âą Challenges ï Next Steps
- 30. Next Steps âą Tackling the mixed-mode situation âą Integration with taxonomies âą Search experience within HBS applications âą Faceted search where rich metadata available âą Analytics feeding website design and vocabulary development
- 31. Conclusion âą Tactical, iterative approach enabled significant progress âą Implementing simpler features/tweaks may have higher impact âą Your existing search engine may have more gas in it than you realize