Macadamian Blog
Device Design Day is Almost Here!
There are only a few days left before the third annual Device Design Day! Taking place in San Francisco, Device Design Day is hosted by Kicker Studio and is promising to be a great day filled with discussions on current trends in design and technology.
Macadamian’s Susan Hosking, User Experience Strategist, and Geoff Parker, Development Manager, will be participating in a panel discussion, "Design for Natural Interaction: Multi-Modal Interfaces" from 2:45 to 3:30 pm with Jody Medich, Co-Founder & Creative Director, Kicker Studio, Karen Kaushansky, Principal Interaction Designer at Jawbone, and, Nathan Moody, Co-Founder, Design Director at Stimulant.
Device Design Day (D3) brings together visual, interaction and industrial designers for a multi-disciplinary conversation about the design of consumer electronics and objects with embedded technology.
Click here to Register
Friday, August 3rd
9:30AM - 5PM
San Francisco Art Institute
800 Chestnut Street
San Francisco CA 94133
Macadamian Talks Metro Design on Microsoft Channel 9
Microsoft is setting a new standard of design and cross-platform experience with the advent of Windows 8. We believe companies that are serious about reaching the broadest audience with their applications must now have a plan for Windows 8.
Recently, we partnered with Microsoft to design a Windows 8 expense reporting application for use within Microsoft. This turned out to be an excellent case study in creating an enterprise application with a Metro design, so this week we were invited to share our lessons learned on a recent Channel 9 program.
Last month we announced a new suite of services to help product managers and developers in the enterprise and consumer applications space with strategy, user experience design, rapid prototyping and development of applications for Windows 8. The response we got was very promising, confirming our bet that, while Windows 8 may not be an "iPad killer", it certainly deserves consideration by any teams with a mobile application intended for broad use.
Product Management Research Pitfalls 7
In case you missed the introduction to this series, I’ll be identifying one product management user research pitfall per post. Missed the previous mistakes? Catch up!
Mistake #1 Mistake #2 Mistake #3 Mistake #4 Mistake #5 Mistake #6
Mistake #7: Misuse of A/B Testing
SOMETIMES DESIGN QUESTIONS END UP in an internal debate between proposed solutions. The lead architect is convinced the features should look or function one way, and the product manager has a different theory.
A situation like this can be a good candidate for A/B testing, a fast form of user research where you launch a few designs to different user groups, perform some testing, and compare the results.
A/B testing measures which of several designs produces the most conversions, fewest clicks, fastest time, most intense emotional response, or whatever met¬ric you decide to measure. Online testing tools like verifyapp.com take advan¬tage of crowdsourcing to make this process even more convenient.
But you need to be aware of the drawbacks of A/B testing. First, you have to rely on your best guess as to the real reason why Design A performed better than Design B. You don’t get any feedback on whether or not the user “gets” the system. (This makes it hard to stay in the problem space.)
Also, you can inadvertently commit yourself to a non-optimal solution. Incre¬mental A/B testing finds the solution that, relative to other presented solu¬tions, produced the best results. However, because you always test one solu¬tion against the others, you run the risk of getting stuck with the best you’ve got, not the best possible solution.
A/B testing should be used as a quick cheat, a complement to other forms of research like the contextual interviews, concept walkthroughs, and usability testing that we described earlier.
Visual Discovery the #4 Obstacle to BI and Analytics
This is the fourth in a four part series about the Obstacles to BI and Analytics. If you missed them, please read the first two obstacles: #1 Ease of Use #2Verticalization and #3 Mobile BI.
Recognized industry analyst Cindi Howson predicts the major analytics trend of 2012 and beyond will be visual discovery. Most solutions offer visual analytics in the form of dashboards, allowing the user to monitor specific metrics and KPIs and take action on them. But visual discovery is about exploring data without a pre-determined goal, digging in to different view—heat maps, spark lines, trellis plots—and visually uncovering insights.
Visual discovery is the holy grail of self-service. Users of all skill levels and across different industries explore views of data and spot patterns, from a government worker digging for ways of optimizing a grant program to a sales manager exploring geographic sales history using a virtual map.
GE Healthcare, a leader in healthcare products, recently revealed a new initiative called SimIndia, a visual discovery tool showing health data across a virtual map of underserved populations in India. The tool would be used by government decision-makers to explore the data and make decisions on how to serve different areas, for example by building a new hospital. Howson lists the leaders in visual discovery as QlikTech, Tableau and TIBCO.
Certainly these companies are ahead of the pack, but the race is only beginning. Visual discovery is a nascent field where any software vendor with the right mix of domain expertise, usability and design skills can innovate.
Interactive Dynamics
Howson provides a detailed list of criteria for evaluating a visual discovery solution. Are advanced visualizations such as trellis plots available? Can multiple data sources be displayed at once and joined? Can the user perform tabular manipulations and aggregations? But the most important criterion is simply “ease of use for the author”, which is listed as “essential, a “show stopper” or feature that significantly affects deployability and/or cost of ownership”. Traditionally, the field of data visualization has focused mainly on the creation of charts and graphs that were easy to interpret. Stephen Few is a thought leader in this area, educating the industry in the best practices of table and graph design and dashboard design.
But in visual discovery, these are table stakes. The new challenge is guiding users through the interactive dynamics of a visual discovery session, in other words, the entire flow and experience from starting the initial views of data, to exploring the data, to annotating and recording the results. Jeffrey Heer from Stanford University and Ben Schneiderman from the University of Maryland have published a paper, “Interactive Dynamics for Visual Analysis,” one of the few publications to date that begins to establish best practices around the overall workflow of a visual discovery session.
Product Management Research Pitfalls 6
In case you missed the introduction to this series, I’ll be identifying one product management user research pitfall per post. Missed the previous mistakes? Catch up!
Mistake #1 Mistake #2 Mistake #3 Mistake #4 Mistake #5
Mistake #6: Confusing Focus Groups and Usability Testing
FOCUS GROUPS ARE BEST USED IN THE EARLY STAGES of developing user requirements to provide rich information on the opinions and attitudes of the target audience. They are suited to an exploratory situation — getting a feel for the range of opinions, understanding the reasons underlying prefer¬ences, developing a basic list of user requirements — information that pro¬vides direction in the early stages of development.
Focus groups are not a good source of behavioral data. What people say they will do and what they actually do are two different things.
Our intentions and actions often vary because we can’t predict contextual factors that may alter our behavior in a given situation. We aren’t even fully aware of all our behaviors. Some everyday actions are so commonplace that we can’t remember them. For instance, if you ask someone what features they use on their telephone and how often, and then observe them using the phone, the results are usually staggeringly different.
The more complex the behavior pattern you ask people to predict or recall, the worse people are at predicting or recalling. So how do you get around this problem? That’s where observational techniques like user testing come in.