Cross discipline collaboration benefits from group think, a consolidation of soft system methodology and user focused design that all starts with design thinking that sees clients, designers, developers and information architects working together to address user problems and needs. As with any great adventure, design thinking starts with exploration and discovery.This presentation examines the high level tenants of system thinking, expands the scope of user thinking to include tools and devices that users employ to find out designs and delve into the specifics of design thinking, its methods and outcomes.

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4. Hilltop Algorithm
Quality of links more important than quantity of links
Segmentation of corpus into broad topics
Selection of authority sources within these topic areas
Hilltop was one of the first to introduce the concept of machine-mediated “authority” to combat the human
manipulation of results for commercial gain (using link blast services, viral distribution of misleading links. It is used
by all of the search engines in some way, shape or form.
Hilltop is:
Performed on a small subset of the corpus that best represents nature of the whole
Authorities: have lots of unaffiliated expert document on the same subject pointing to them
Pages are ranked according to the number of non-affiliated “experts” point to it – i.e. not in the same site or
directory
Affiliation is transitive [if A=B and B=C then A=C]
The beauty of Hilltop is that unlike PageRank, it is query-specific and reinforces the relationship between the
authority and the user’s query. You don’t have to be big or have a thousand links from auto parts sites to be an
“authority.” Google’s 2003 Florida update, rumored to contain Hilltop reasoning, resulted in a lot of sites with
extraneous links fall from their previously lofty placements as a result.
Photo: Hilltop Hohenzollern Castle in Stuttgart

5. Topic Sensitive Ranking (2004)
Consolidation of Hypertext Induced Topic Selection [HITS] and PageRank
Pre-query calculation of factors based on subset of corpus
Context of term use in document
Context of term use in history of queries
Context of term use by user submitting query
Computes PR based on a set of representational topics [augments PR with content analysis]
Topic derived from the Open Source directory
Uses a set of ranking vectors: Pre-query selection of topics + at-query comparison of the similarity of query to topics
Creator now a Senior Engineer at Google
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6. Caffeine (2010)
Indexing infrastructure
Made it easier for engineers to “add signals” that impact ranking
Pre announced and open to public testing
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7. Panda (2012)
Originally thought to be about content quality. Then expanded to machine mediated judgement of user experience.
UX and IA are now the odd man out in designing experience for users. Google now determines quality
of user experience and content with UX metrics? Clickthrough, Engagement, Satisfaction
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8. Hummingbird (2014)
Comparison of search query to general population search behavior around query terms
Revises query and submits both to search index
Confidence score
Relationship threshold
Adjacent context
Floating context
Results a consolidation of both queries
Entity=anything that can be tagged as being associated with certain documents, e.g. Store, news source, product
models, authors, artists, people, places thing.
Query logs (this is why they took away KW data – do not want us to reverse engineer as we have in past)
User Behavior information: user profile, access to documents seen as related to original document, amount of time
on domain associated with one or more entities, whole or partial conversions that took place
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9. Alternative to figuring out something that really works, to working with UX, to find an alternative to the SEO guns
and religion of keywords and links
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10. UX, Content Strategy, IA
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Users strive to make sense of reality as they move through situations, time, and space
Users encounter gaps in their knowledge and see these as barriers
Users seek to “bridge” the gap and reach their goal (a reality that again makes sense)
Users don’t know what they don’t know. So, they start looking from a point of ignorance. IR systems have a hard
time grasping this.

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Searchers use a variety of techniques and resources
Safe to say, not many of our users “surf over” to the product sites to “check out the action”. Our users
are more about “directed browsing” an unstructured and opportunistic search, stopping along the way
to get berries, with a specific objective in mind.
When our users come to our sites each click is a question regarding what they think they need to
know. Either we answer the question, provide them with enough information to refine their search
and continue on, or fail them completely where, in frustration at being confronted with yet another
information problem, they resort to the search box in the upper right corner.

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(What is Browsing Really: Bates 2007) 4 elements to browsing behavior: glimpsing, selecting/sampling, examining,
acquiring/discarding. Browsing does not equal scanning (differentiated behavior)
“We define browsing as movement in a connected space. In order to achieve this movement, people undertake
certain actions: they shift their gaze, they alter their position, they skip over things, they glance at things briefly, from
afar, or close up, they back up, they pause or stop and they respond to interesting phenomena (Kwasnik 1992)”
Web search engines have not been good for browsing, recent attempts of support with facets, filters (spatial,
temporal)
Whenever possible, we should empower our users with fine-tuned navigational aids so that they can find what they
are looking for themselves.

16. Dynamic Semantic Clusters: Where User Experience Begins
Mark Baker: Content Strategy Forums: July 25, 2014
http://csforum.eu/articles/dynamic-semantic-clusters-where-user-experience-begins
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17. Resource: Pew Internet Trust Study of Search engine behavior
http://www.pewinternet.org/Reports/2012/Search-Engine-Use-2012/Summary-of-findings.aspx
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18. How to search:
56% constructed poor queries
55% selected irrelevant results 1 or more times
Get Lost in data:
33% had difficulty navigating/orienting search results
28% had difficulty maintaining orientation on a website
Discernment
36% did not go beyond the first 3 search results
91% did not go beyond the first page of search results
Resource: Using the Internet: Skill Related Problems in User Online Behavior; van Deursen & van Dijk; 2009
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20. VISUAL COMPLEXITY & PROTOTYPICALITY
The results show that both visual complexity and proto-typicality play crucial roles in the process of forming an
aesthetic judgment. It happens within incredibly short timeframes between 17 and 50 milliseconds. By Comparison,
the average blink of an eye takes 100 to 400 milliseconds.
In other words, users strongly prefer website designs that look both simple (low complexity)
and familiar (high prototypicality). That means if you’re designing a website, you’ll want to consider both factors.
Designs that contradict what users typically expect of a website may hurt users’ first impression and damage
their expectations.
August 2012
Resource: http://googleresearch.blogspot.com/2012/08/users-love-simple-and-familiar-designs.html
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22. Put the sidewalks where the footprints are
Resource: Stuart Brand: How Buildings Learn
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23. Fell into designing for the technology
I made it look pretty
Wordpress sites: Spot them from afar…HUGE spanning hero and infinite scroll neither of which users like much
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26. IR systems have built in functional complexity to accommodate multiple aggregators and actors that are opaque to
users (black box)
The information system has the role of supplying knowledge and is not always the sole supplier of output
Role of knowledge support of specific action
Information System/Soft System: information as socially constructed
Information Engineering: information as a concrete phenomena
Problem solving encompasses system, cultural and strategic concerns
SSM incorporates system learning and experiential learning and applies to problem-solving
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29. IR systems have built in functional complexity to accommodate multiple aggregators and actors that are opaque to
users (black box)
The information system has the role of supplying knowledge and is not always the sole supplier of output
Role of knowledge support of specific action
Information System/Soft System: information as socially constructed
Information Engineering: information as a concrete phenomena
Problem solving encompasses system, cultural and strategic concerns
SSM incorporates system learning and experiential learning and applies to problem-solving
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30. Soft system Methodology (SSM)
SSM grew out of the failure of systems engineering-excellent in technically defined problem situations-to cope with
the complexities of human affairs, including management situations. As system engineering failed we were naturally
interesting in discovering what kind of approach could with problems of managing.
SSM is the logic-based stream (engineering) incorporating cultural and political streams to make judgments
between conflicting interests by setting up criteria on what is significant and how to judge?
Model the purposeful activity of the users to define the transformations to take place
SSM: A Thirty Year Perspective: Peter Checkland (2000)
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31. We already do this in a manner of speaking except without the underlying systems part, hence the current unicorn
hunt for UX professionals
Assumptions:
• The adaptive whole includes contradictory systems
• The system will go through one or more transformation
Intersection and Transformation of SSM: Mathiassen, Lars, Neilsen, Asid (2000)
Idea is that there would be better systems of IT professionals had better understanding of the scope, nature, impact
of the system
User experience will be better if UX professionals have a better understanding of scope, nature and impact of
designs on IT (there are always trade offs)
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32. SSM is about cycles of discussion, learning and debate – unlike IT system thinking that looks for absolute complete
solution every time
SSM uses system and psychological foundations to develop models of human behavior – measurement of these
models is problematic because not always quantitative
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33. Answered for us and the client
Would this become the first deliverable after signing?
Precipitate the client questionnaire?
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34. Stage 1: Express the current situation and surface the plurality of project team world views
Examine the background of the problem to expose issues, problems in structure, develop representation of relevant
domains. Used for identification of knowledge gaps, elicit discussion with all of the stakeholder groups
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35. Stage 2: Uncover the Root definition = relevant systems that will provide insight into the problem. Used to ensure all
points of view and intersect considered
Select issues that warrant a closer look by project team
Develop alternative views
Explore creative scenarios
Drill down into specific organization processes
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36. Stage 3: Conceptual Model: expressed by verbs, usually focused on 3 sub systems (knowledge, criteria,
application. Verbs are the first level of resolution, e.g. monitor and control.
Develop agreement and action plans through process of accommodation
Output = human activity system to create evaluation of the real world
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37. Stage 4: Execute on action plans for purposeful activity
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38. This is what I see when I read/hear about Agile UX
It is a virtuous cycle
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39. The new-new thing
Multipolar experience where everyone has an opportunity to participate in delivering a satisfying experience
Serious play >> magical thinking
There’s lot of interest in design thinking these days – don’t care about that, care about what it means to us as
information professionals
Most of the work here has been done by agencies that are VERY successful (IDEO, frog). Mention that next time
your boss says they: can’t afford it, can’t sell it, can’t use it.
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40. … A methodology that imbues the full spectrum of innovation activities with human center design ethos.
We thought that we were doing this…Wrong
It should be doable without custom dev
It has to make money or facilitate making money (that’s how they pay us)
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41. Top down logic
Logically certain and verifiable conclusion
What does the data say
Validate thinking with metrics
Google would not be around today if it practiced deductive reasoning
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42. Possibility over validity
I wonder over I know
1st step of reasoning is wonder, not observation
Logical leaps of the mind from data that does not fit the models
Actively look for new data points
Challenge accepted explanation
Infer possible new worlds
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43. Experimental: nothing wrong with failure as long as it comes early
Flexible: accept that milestones cannot be predicted with certainty and projects take on a life of their own
Collaborative : silver buckshot instead of silver bullets
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44. Inspiration: insights gathered from across resources
Ideation: ideas that become insights
Implementation: base ideas developed into concrete plan of action
prototypes
good not working model that inspires conversations
shared vision realized
nothing codified – spring board to meaningful changes (this avoids codified mistakes)
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45. Design thinking does not occur in linear steps
Spaces demark different and related activities
Can loop back and forth between spaces
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46. (NOT THE RFP)
(may have to think outside the problem space, e.g. Bill Gates wanting to bring internet access to sub-Saharan
Africa only to find out that they need clean water and electricity first)
Well constructed brief provides for serendipity, unpredictability and the capricious whims of fate
Prepares the soil for ideation
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47. Need T-shaped folks here
Tools: brainstorming with cross discipline, diversity of participants, visualization and conceptualization
Group sort ideas
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54. Discover client world views, environment, cultural and political influences
Surface interacting systems within organizations (client and ours)
Define user purposeful activities (what problems are we trying to solve)
Iterate the engagement
Shift thinking from optimizing for the technology to optimizing for the users
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60. Navigation dominance
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61. A true “home” page, start here and navigate to where you want to be
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62. Broad band comes into its own and BIG pictures make a splash
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63. Why Google started moving away from link-based relevance
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70. Remember the unstructured problems? Intangibles?
They will attempt to derail the best laid plans.
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73. Deliverables/Outcomes: Persona, Competitive Landscape review, Lifetime Customer Value, SWOT Analysis, KPIs
and Goals
Branding & messaging framework review
Client customer data
Demographic and Psychographic data (FB, Google affinities, social media channels (twitter, LI, etc)
Analytics: To see what we’ll be able to track.
Competitive Landscape Review
Client competitive data
Existing and emerging competitors
Industry news/trends
SWOT Review
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74. Answered for us and the client
Would this become the first deliverable after signing?
Precipitate the client questionnaire?
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75. Deliverables:
Content audit for gaps (keep, kill, demote)
Content strategy for opportunities
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80. http://www.wsj.com/articles/SB106134872871015900
Because great architecture is not enough. This is Falling Water by Frank Lloyd Wright. The joke is that it lives up to
its name with
Cantilevered terraces (projections that extend well beyond their vertical support)
Structural engineer found that "after more than 60 years, Fallingwater was still moving." One side of the living room
terrace, he reported, had sagged almost seven inches.
Sounds like it worked for Wright more than for the client.
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81. While we discuss whether there can be such a thing as a UX designer,
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