Information Design for an Instrumented World

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Information Design for an Instrumented World Hannah Donovan, 10 October 2011

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Hello! I’m Hannah

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Who are you?

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I have a secret to tell you.

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“The solution is the problem.”

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What about you?

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What you’re not going to learn this morning.

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By albyantoniazzi on ﬂickr

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We need to stop grasping for the perfect visualization and return to the basic language of charts and graphs. Only then can we begin to uncover the relationships the data has to oﬀer. – Brian Suda Photo credit: andré.luís on Flickr

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Photo credit: Alex Pounds

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Olivier Gillet says: MAKE Photo credit: Alex Pounds

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Olivier Gillet says: MAKE A Photo credit: Alex Pounds

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Olivier Gillet says: MAKE A POINT. Photo credit: Alex Pounds

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So what are we going to explore today?

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The details.

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The details are not the details. They make the design. – Charles Eames

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We people, we have a lot of details now. We live in an instrumented world

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Most of our instrumented world is measured in terms of attention data.

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ACTIVE PASSIVE scrobbling location tracking health monitoring posting checking in tweeting

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You guys… This is kind of crazy.

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New conceptual breakthroughs are invariably driven by the development of new technologies. – Don Norman Photo credit: Piemont Share on Flickr

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~2005 ~2010

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Web APIs become popular ~2005 ~2010

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Web APIs become popular Moore’s law applied to data storage ~2005 ~2010

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Web APIs become popular Moore’s law applied to data storage Big data ~2005 ~2010

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Web APIs become popular Moore’s law applied to data storage Big data Ability to build real-time interfaces ~2005 ~2010

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Web APIs become popular Moore’s law applied to data storage Big data Ability to build real-time interfaces Cloud computing ~2005 ~2010

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Our job is to make sense of this instrumented world and all the information in it.

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1. COMMON FORMATS AND PATTERNS ARE EMERGING

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For us: be aware and inquisitive, so we can choose the right tool for the job For users: they will expect certain things to work in certain ways

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2. THE AMOUNT OF PERSONAL DATA CAN BE OVERWHELMING

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For us: spoiled for choice, we have more decisions to make than ever before. For users: signal vs. noise is becoming a common problem.

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3. DATA HAS DISTINCT TIMING

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For us: we need to have sharp presentation skills for conveying the speed of the data For users: they care, and will often expect things to be in real-time.

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4. OUR DATA TRAILS ARE STARTING TO GET LONG

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For us: we’re faced with a new challenge of how to reﬂect this meaningfully to users For users: they are becoming increasingly aware of their history

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OUR TOOLKIT Part I

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1. UNDERSTANDING THE DATA

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Use the WW brief: What do you want, and why do you want it?

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Use the WW brief: What do you want, and why do you want it? (It’s your job to ﬁgure out how to do it).

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WHAT the goal WHY use case evidence hunch etc.

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2. GETTING THE DATA

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Is it a data dump or is it live? If it is live, then you are probably relying on an API (your own or external).

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An API: Collectively, an API is a bit like a “styleguide” — it deﬁnes vocabularies and conventions

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Basically, “Here’s the stuﬀ you can get, and the format you’ll get it in”

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Getting the stuﬀ you want out: An API allows you to call methods. A method is a structured way for asking for a particular bit of information from an online service.

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Something like, “Hey, I want some info about this thing” “How many?” “10, and be sure to include the picture bits”

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Don’t clean up API vomit!

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If the service is currently being worked on by your team, establish a dialogue with them about this.

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Types of questions I like to ask: What parameters can it have? How expensive is this? What can we compare this against?

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If the answer is “no”… Explain what you want and why you want it. Let them ﬁgure out how ;-)

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3. DESIGNING THE DATA

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1. Sketch UI with pen & paper 2. Get the data in-page 3. Design the UI in-page

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Design patterns for visualising personal data Part II Photo credit: number657 on Flickr

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Feeds Answers the question “what’s been happening recently?”

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Twitter, Facebook

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Ranked Lists & Leaderboards Answers the question “who’s winning?”

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Ranked lists & leaderboards: Foursquare, Last.fm

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User-facing Stats Good for showing a user’s overall performance/usage and answers the question “How am I doing?”

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User facing statistics: Flickr Pro, Amazon Author Central

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Counters Good for showing less than three key statistics about a user, and answers the question “How am I doing?” at a glance.

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Counters: Hype Machine, Twitter, Foursquare, Dribble, Lanyrd

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Sparklines Good for showing a huge amount of data in small space, and can answer questions about trends within a sentence.

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Sparklines: From Edward Tufte’s ‘Beautiful Evidence’, Flickr, Amazon

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Line Graphs Good for showing continuous data and visualising trends. Line graphs are good for answering questions like “How did it look during ____?”

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Line graph: Run Keeper, Withings Body Scale

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Bar Charts Good for visually comparing discreet data and very versatile as the data in a bar chart can be ordered however you want. Great for answering questions like, “which one is___?”

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Bar chart: Last.fm, Nike+, Brian Suda’s ‘Designing with Data’

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Sentence (yes, the sentence!) Good for contextualising data in a conversational tone. Great for answering questions that could use a bit of personality.

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Sentence: Huﬀduﬀer, Last.fm

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Realtime Search Good for ﬁltering out signal in vast amounts of real-time noise. Answers the question “what is happening with x right now?”

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Sentence: Twitter, Google

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Favlikelovestar+1 Good for services that have lifestream data that people want to hug; use these for that visceral “I want to keep this! I love this!” response.

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Favlikelovestar+1: Instagram, Favstar, Spotify

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Reblah Good for services that want to cater to lazy usage. Responds to the impulse “I want to make this part of my identity too”

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Reblah: Tumblr, Twitter

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Thumbs & Stars Good for services that depend on ratings for good content to bubble to the top. Answers the question “what do people think is best”?

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Thumbs & Stars: eBay, iTunes store, YouTube, Last.fm images

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Notiﬁcations Good for important bits of real-time activity people don’t want to miss out on. Often fosters serendipity.

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Notiﬁcations: Facebook, Google+, Android, Email

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And remember to layer: At ﬁrst sight, reveal the bare minimum With contextual UI, reveal more For the discerning, link to the source

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What: re-envision Shazam’s tagged track UI, using some of the patterns we just talked about. Why: we could use any music API out there to show more relevant data about what you just found/remembered. SKETCH IT!

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SKETCH IT!

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SKETCH IT!

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SKETCH IT!

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Personal & proﬁle data Part III

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1. IN ‘N OUT DATA

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Home: reﬂecting incoming data

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Home: Feedville. Population, all of us.

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Proﬁle: reﬂecting outgoing data

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Proﬁle: new Facebook

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Take a minute to remember personal editorial.

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Take a minute to remember personal editorial. Proﬁle: MySpace circa 2006

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2. IT’S ALL CONTEXT, BABY

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ABOUT THE: Individual Aggregate ON: Goal-driven device Browse-based device phone PC iPad TV me friends group network

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2. CASES

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Logged out, looking at some data Logged in, looking at my data Logged in, looking at someone else’s data Logged out, looking at no data (yet) Logged in, looking at where my data will go Logged in, looking at where someone else’s data will go DATA IS PRESENT NO DATA YET! ANONYMOUS MINE SOMEONE ELSE

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Tip for dealing with cases: Keep your own UI gallery

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Cases: Logged in, looking at where my data will go

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Cases: logged in, looking at my data

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Cases: logged in, looking at someone else with no data yet

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Another tip: Lay oﬀ the lorum ipsum.

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SKETCH IT! What: re-envision an eBay seller proﬁle screen, for at least 2 cases. Why: There’s a ton of data at hand, and very little revealed about this person you’re about to fork over cash money to.

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Time Part IV Photo credit: alphadesigner on Flickr

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Real time Recent past (~1 day ago) Past (~1 week ago) History (archives) Now Joined

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Realtime

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Recent past & Past

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History

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Time shifting

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SKETCH IT! What: would Twitter look like if it showed what you’d been up to for the last few months as well? Why: because nobody’s done it yet :)

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Our data trails are getting long Part V Photo credit: Gonzak on Flickr

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How do we organise these long data trails?

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We’ve all been so distracted by The Now that we’ve hardly noticed the beautiful comet tails of personal history trailing in our wake. – Matthew Ogle