The Simplicity Shift – Scott Jenson

Date : October 28, 2009

“Feature Blindness”

Scott Jenson defines ‘feature blindness’ as users being blinded by a feature list. He identifies the bottom-up approach of creating a user persona and a task scenario as more efficient than the top-down approach, in terms of organizing a commonsensical hierarchy of a product’s features.  The ideal visualization would be to ‘tame’ the feature list and prioritize features in accordance to a set of usage requirements and subsequent usage frequency.

UnFeatures: There’s More Than Meets The Eye
In considering the context of use, designing a product requires considerable knowledge of the end-user’s lifestyle. In this sense, the product will have to fit a type of skilled-user. UnFeatures, as Jenson explains, “is a broad set of problems that needs to grow over time.” (p.75) For instance, what would be the setup requirements of a product? Does it allow for direct usage, or are there instructions to follow prior to a full-use interface? With broad markets there is a need for easy and simple step-instructions. A product strategy is planned to account for errors and facilitate error-correction. Jenson suggests an example of a ‘search button’ that does the work for users to avoid rejection and enhance user forgiveness and product acceptance. UnFeatures should be included in the product strategy plan at the very beginning of the design process. For instance laying down the products features (functions offered by the product) and the unfeatures (concerns such as recovering from errors and user feedback) can help create a hierarchy of relevance.

The Priority Trick
Having features and unfeatures is great, but when it becomes very confusing for users when too many options are available. Jenson notes that creating a persona/scenario helps shape the hierarchy of your features. Quantitative data is usually handy here: frequency of use, and a preference ranking are some of Jenson’s examples of how to decide on what’s important to users.

Make the Easy, Easy and the Hard, Hard
If we carefully follow Jenson’s method, with a persona/scenario determined and a un/features list prioritized, at this point the skeleton of the product can be build and secondary features can be worked out through sub-menus for example and elaborate layering. Keeping it simple, as the title of his book suggests, seems to be Jenson’s motto. One trick here would be to calibrate the flexibility of a product while considering the complexity and simplicity factor. If more flexibility creates ambiguity, then limited depth insures a higher usability level.

Another trick is to consider both expert and emerging users, and in doing so, expert features will be hidden in the depth of the interface. This way, emerging users will not be overwhelmed by a multitude of features that may not have compatible relevance.  So features addressing specific sub-category users (expert users for instance) demand more timely investment in digging out the features, which allows the broader audience to have access to minimal and immediate core functions.

If we look at a “feature list as a source of design confusion” (p.89), we can begin to break them down into categories and organizing their hierarchical importance in terms of the end-user/persona characteristics.

“Innovation Blindness“

Scott Jenson defines ‘innovative blindness’ as members in a team that hold “attitudes that discourage innovative thinking.” (p.113) He critiques corporations that follow previously established guidelines for designing rather than thinking outside the box and looking for new ways of designing meaningful products for meaningful experiences. In his opinion, it is important to question the design in context and its underlying rules.

Where innovation occurs is when you get design dreamers and pragmatic programmers working together to get brilliant design implemented with as little work as possible.” (p.114)

See the Water
The water becomes hard to see when designing new products is made in terms of how previous products work. Here the author gives two of the more commonly employed: the digital watch and critical elements of desktop appliances such as the scroll-bar. He emphasizes conventions and template designs a great hindrance to innovative breakthroughs. Scroll-bar do not need to be included everywhere for the sake that they are useful on the web. New products need new conceptions to create new meaning and new ways of navigating space.

Embrace the impossible
One way to go about encouraging change and innovation in a team is to create a synergistic environment for both pragmatic programmers and dreamer designers to talk, negotiate, collaborate and agree on a compromise that meets both of their concerns and goals. Jenson suggests trying new ideas and failing in the process. This type of approach however is only made possible in a team that promotes a positive attitude to iterative design. Design that does not fail is mostly a design that is done safely and that more often than not will be quite similar its predecessors. Questioning design templates helps designers engage in innovative attempts and “See the Water.” One factor to consider here would be user expectations and how deviating too far from them may obstruct a product’s usability. Zeroing in again on scenarios and personas helps achieve a balance between pragmatism and innovation and hence provides a meaningful ground for designers and programmers to articulate their respective needs and generate collective design solutions. Jenson calls this a “win/win situation.” (p.122)

Fail fast
By Fail fast, the author suggests starting with an idea or concept that may be frail, putting in on paper, working out the ‘works’ and ‘don’t works’ of it and proceeding in a frequent iterative fashion. He offers three typical methods used for innovating and evaluating concepts: Sketching (web sketch, increasing fidelity); Interactive Demos; User Testing.

Innovation doesn’t flow fully formed through your fingers on to the paper. It only comes in fits and starts, and, sometimes, you have to work your way through dozens of designs to find your way to the one that pulls it all together.” (p.132)

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Frog’s Research Learning Spiral (How the Why of data wins over the What)

Category : Designing for Usability, Interface Research, Research in Design Practice

As clients are becoming increasingly aware of ethnographic-ish research being an essential and integral part of the design process for a high quality user experience offer, it is important for us (designers) to learn an appropriate format for approaching and customizing research on a project basis.

Frog’s Research Learning Spiral, as David Sherwin names it in A Five-Step Process for Conducting User Research, allows us to think of research as not only a set of insight-focused methodologies and collaborative practices, but also very much so as a process of articulating and defining the focus area and scope of the research itself through its 5 learning stages: Objectives, Hypotheses, Methods, Conduct, and Synthesis.

I would like to focus on the the 3 early stages of the spiral. These are fundamental in situating the research area and addressing design questions with methodologies geared towards feeding our knowledge lexicon of people and things in their habitual contexts according to pre-defined objectives and hypotheses.

1 • Objectives focus on the framing of questions following the 3 Ws and an H structure: Who What When Where Why and How. These together help us define who the demographic user base is, what activities they might be involved in while using our service or product, when they would be engaged in such activities, where these activities would take place, under which emotional or rational states (why), and using which processes. These questions are in turn reformulated into simple statements of research objectives, which outline the scope of the research effort.

2 • Hypotheses are assumption packed opinions or suppositions we have about a product or service, its users, and the contextual settings in which the product acts, which are meant to be tested and challenged. Sherwin lists 3 types of hypotheses: attitude (what users would like to get out of a service), behavior (what users would like to do with the service), and feature (which feature users would most enjoy using).

3 • Methods — such as contextual enquiry, surveys, interviews, and benchmarking — can help prove or disprove these hypotheses by revealing key data about a demographic user, their contextual environments, and identify leverage points wherein design can affect their everyday and provide positive change or support. Other more participatory activities which involve probing users — such as diary studies, card sorting, and paper prototyping — can serve as experiential idea generating methods with a capacity for drawing design solutions and concepts that meet the user’s needs and mental models. Finally, evaluative methods — such as usability testing, heuristic evaluations, and cognitive walkthroughs — will demonstrate whether these ideas are effective, useful, and desirable.

[F]inding meaning in your data […] can mean reading between the lines and not taking a quote or something observed at face value. The why behind a piece of data is always more important than the what.” — Lauren Serota, Associate Creative Director at Frog Design

According to Sherwin, data tells us what and when users do things, but not why. Context is in fact king. Integrating such framework for user research helps provide us with the contextual understanding — the understanding of given demographics’ everydayness — for making more informed design decisions.

I am particularly interested in the name “learning spiral”: a looping process that doesn’t need to be lengthy, costly, and a unique event. It is spiral and has the potential of being a cyclical and iterative process, which can be applied as needed at different stages of a design process and with a different scope. That spiral from which I can learn allows me to investigate more specific areas of my users’ everyday by defining learning objectives.

While every research endeavour has a plan and objectives, i particularly found this interpretation because it gives importance to the planning and framing of research and integrates objectives definition as part of the research itself. Typically research seems to begin with contextual inquiry and interviewing right away as a recourse to inform the design approach and concept which does not necessarily end up being a solution that is desirable. Involving research participants in the framing of the research seems to be a more inclusive and humane approach that is bound to have a worthwhile and desirable quality.

— source: http://bit.ly/1eCoUyH

“A Human-Centered Technology.” – Don Norman

Category : Designing for Usability

When we speak of technology what we are often referring to are communication platforms, such as computers and cellular phones. For Don Norman (1993), anything that is created by human beings is a technological artifact, whether physical (tangible objects, i.e. paper) or mental (information structures, i.e. language). Norman divides technological innovations into good and bad, those that “make us smart” by extending human capabilities and those that “make us stupid” by frustrating, captivating, enslaving. His focus involves the understanding of both technologies that extend cognition and those that manipulate cognition.

While innovation has a historical contribution in terms of providing appropriate and efficient tools for society that help enrich human knowledge and enhance mental capabilities (memory, thought, reflection), it has however created modes of entertainment that increasingly promote a consuming rather than a productive people and is accountable for the apparent social divider between the haves and havenots of technology.

Moreover, new technologies create new social practices, specializations and knowledge requirements. This in turn adds a new set of social dividers between those who possess the required skills and those who don’t. The societal implications of new technologies have become more and more complex with the advent of computational systems. Norman’s aim is as follows: “If we learn the reasons for and the properties of these various technologies, then maybe we can control the impact.” His aim is to influence a human-centered approach to technology; a quest for technologies that demonstrate humane properties.

“Toward a Human-Centered View of Technology and People”

As technology becomes increasingly immaterial, its function concerns more and more the fulfillment of abstract needs, such as time-management, decision-making, methodologies, etc. Norman’s critique is that rather than aiding human cognition, technologies today add confusion. He notes that “distractibility”, “illogical” behaviour, and other concurrent characterizations are responses to the rigidness of the way in which computers operate. Those characterizations are related to the constant need for technology to impose a certain type of behavioral and mental effort (i.e. to pay attention, to speak grammatically, etc.). In addition, the information overload creates a feeling of inadequacy in society; such as, the inadequacy of the mind the memorize a certain amount of information, the incapacity to cope with innovation, etc. His premise is that “people err,” and computers don’t, instead they are faulty and the blame is made on the human.

‘To say that people often act illogically is to say that their behavior doesn’t fit the artificial mathematics called “logic” and “decision theory.” ‘

Hence, this adds yet another social divide: machine-centered behavior and human-centered behavior. For Norman, the problem lies in the way technology inadequacy to adapt to the human. Technology would benefit society if it understood more the way in which human cognition works. Norman also criticizes technology for creating an information saturation wherein humans are exposed to too much information that results in a so-called mental “burnout”. To illustrate this side-effect, Norman distinguishes between what technology is able to measure (i.e. hours worked per week) and what it can’t (i.e. satisfaction, rupture, experience), which he respectively refers to as “hard sciences” and ‘ “soft” sciences.’ The “hard sciences” make simple and objective measurements, leaving out what it cannot measure; whereas the ‘ “soft” sciences’ concerns itself with the complexity of subjective measurements, thereby making a link between technology and the living system.

“Two Kinds of Cognition”

The author identifies two kinds of cognition: experiential and reflective. Experiential cognition refers to the way we acquire expert skills through the use of our bodily skills; reflective cognition refers to mental thought processes that inform the development of new concepts and appropriate structures that serve as guides for action. Those are complementary and essential in the sense that they include both inner (mental) and outward (physical) practices.

The point here is that technologies that excite the mind (such as, TV) cannot be a substitute for experience, and hence cannot provide adequate modes of cognitive enhancement. “Experiential thought” is only possible through an active participation and engagement of the world.

Source:
Norman, Don. “A Human-Centered Technology.” Things That Make Us Smart: Defending Human Attributes in the Age of the Machine. Perseus Books, U.S.A., 1993.

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