Search Success in 2016 - Recap of ESE2015
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The document discusses themes and observations from the ESE 2015 conference. It summarizes some key themes discussed at the conference: - The promise of enterprise search to search across both structured and unstructured data sources within an organization. - A "search immaturity model" was presented which considers the many contextual factors that impact a user's search experience, such as their task, environment, abilities and more. - There was discussion of how end user expectations are changing due to consumer technologies, and users now expect intuitive, personalized search experiences similar to consumer search engines.
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Search Success in 2016 - Recap of ESE2015
- 1. ESE 2015 recap – Themes and Observations Search Success in 2016 Jeff Fried CTO, BA Insight
- 3. The Enterprise Search Promise RDBMS (JDBC, ODBC, SQLNet, DW, DM) Applications (e.g. ERM, CRM, Help Desk) LegacyData (e.g. ISAM, VSAM, IMS) MessageQueues (e.g. TIBCO, MQ-Series) DMS (e.g. M’Soft CMS, Documentum) eMail Systems (e.g. Notes, Exchange) Files (e.g. Word, Excel, pdf, images, mp3) Portals (e.g. WebSphere, WebLogic) WWW (HTML, XML, WML, JavaScript) PrivateWebs (e.g. news feeds, Intranets) DirectPush UNSTRUCTUREDSTRUCTURED REAL--TIME Enterprise Search Platform SITESEARCH MAILSEARCH BISEARCH DMSSEARCH CORPORATE SEARCH ECOMMERCE SEARCH …
- 4. 4 Themes over the last 2 days
- 5. 5
- 7. ? Environment Work TaskSituation User Repository Outcome Date Format Language Genre Authenticity Topic Domain Objective Type Solution Domain Workplace Culture Managerial factors Organizational factors Distraction Interruption Time Orientation Overview Evidence Explanation Instructions Definition Knowledge Experience Motivation Physical ability Cognitive ability Learning style Mental Model Interface Search Task Source: Adapted from Toms (2011). Task-based information searching and retrieval inRuthven & Kelly (eds.) Interactive Information Seeking, Behaviour and Retrieval, p. 49 Context for search is … complicated
- 8. Technology Expectations End-User Expectations Consumerization of IT & Intuitive User Experience Commoditization of Search Engines
- 10. The Concierge
- 11. The Butler
- 12. Soon?
- 14. What
Editor's Notes
- The search box and a button depends on people making a conscious decision to go get information. The idea is similar to that of a library, where a seeker must actively find a book or periodical. It was a natural outcome of computing architectures in which access time was extremely precious and computing locations were specific and crowded. Database queries are driven by user need, because hammering a RDBMS server damages its ability to respond, and even the Internet was initially accessible only from desktop machines. The model for information seeking, therefore, was that of the concierge. It was not called for until a necessity arose, and the concierge needed at least some a priori thought process and preparation. In short, concierges needed to be ready for many needs (but could not be ready for all of them) and the exchange would await the searcher’s finding his way to the “desk” – a Web page, typically.
- The next stage is what we live in right now – the transition is from a concierge model, where a user navigates his way to a place where the information source is available, to an always-on stage. Concierges stay at their desks and wait for people to come find them. Butlers are always available, ready to respond to any request for information. Thus do Siri, Google Now, Watson and a host of smaller, less well-known applications stand ready to help consumers discover whatever they need to learn more about. Such a butler model stands now almost entirely in the consumer-only space, although IT executives are beginning to explore the possibility that such a model could be available for business intelligence or content search.
- Look forward to a new era. Predictive modeling already allows some technologies to foresee what users need and provide it ahead of time, such as pre-caching browsers or CDNs that keep available copies of images, videos or pages that are frequently requested. For more than 10 years, start-ups have imagined the value of developing a sort of halo of information circling users’ explicit behavior in order to respond to their needs before they can even express them as needs. Early examples include Intellext Watson (no relation to IBM Watson), which watched what was being written and provided suggested and relevant content based on that activity. Other, larger vendors have pursued the same model, including HP Autonomy. However, the model has not heretofore worked, in part because users have not seen a need for it, in part because the results were not compelling, and in part because the input model of monitoring text as it was being input limited its appeal to people who typed in narrative text. Mighty changes have occurred. Gmail was released 10 years ago. Its model, in which ads are selected using the content of messages, was once an unimaginable departure from norms demanding that private content could not be used as a foundation for advertising. Mobile devices, especially smartphones (and in the future, wearable data-gathering devices), have increased the amount of available context that algorithms can detect about users by several dimensions. New media types have emerged, both in capture (think of Google Glass) and in produced media (podcasts begat YouTube channels). Today, interactions with mobile devices are increasingly driven not fully by input text but by interface and context. And while the butler model still expects a conscious input from the user, users are also enamored of the idea that an algorithm is dogging them to present them with opportunities before they even know to ask. Facebook experiments not just with filtering a user’s viewed feed to eliminate what it suspects he or she would find uninteresting; it also is elevating apparently unsought status posts as an opportunity for the user to indicate interest in them.