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Semelhante a Design-led Approach to Big Data
Semelhante a Design-led Approach to Big Data (20)
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Design-led Approach to Big Data
- 1. Design Led Approach to Big Data ARTI DESHPANDE | JENNIMITCHELL
- 2. Agenda • Expectation setting. • A tangible example. • High level design process. • Value of research. • What does this have to do with Big Data?! • Now what? • Answer all your burning questions!
- 4. We are not data scientists… @KC_Arti Experience Design Manager DST Systems Adjunct Professor JCCC @useagility Experience Director Useagility
- 6. We hear “Big Data”, we picture this:
- 7. What’s the common theme? • Information overload • Information anxiety • Flood of information • Analysis paralysis • Infobesity • Infoxication • Information glut • Data smog
- 9. Searching for an apartment in Chicago when you have more requirements than just how many bedrooms and baths, square footage, or parking is very difficult.
- 10. eLocate fictional company that targets renters unfamiliar with Chicago who are relocating • Charge commission for: - Apartment listings on the site - Visits scheduled through the site - Rentals that result from the site - Promoting professional services like movers, cleaning services, etc • Apartments will list with them if they get a lot of visitors. • They’ll get a lot of visitors if they have flexible and helpful search tools.
- 14. Synthesize to Understand Prototype to Validate Research to Discover
- 15. Research Observations Interviews Surveys Existing data Competitive analysis Discover How people think about activities User goals Business goals Motivations Scenarios Gaps in the experience Synthesize to Understand Prototype to Validate Research to Discover
- 16. Synthesize Personas Mental models Scenarios and storytelling Mapping & Models Understand The people and the business Behavior patterns Communication patterns The possibilities The desired experience Synthesize to Understand Prototype to Validate Research to Discover
- 17. Prototype Sketching Wireframing Rapid Prototyping Validate Our collective understanding Experiential evidence Our frameworks and blueprints Our prototypes before we implement Synthesize to Understand Prototype to Validate Research to Discover
- 19. Cliff’s Notes User Needs Business Goals The sweet spot.
- 20. Conduct stakeholder & user research • eLocate stakeholders • Client stakeholders at apartment complexes • Potential Advertisers • Apartment seekers Synthesize to Understand Prototype to Validate Research to Discover
- 21. eLocate Business Objectives • Provide a unique, fun, and accurate way to find an apartment in Chicago leveraging all of the relevant data available. • Have the largest apartment selection for Chicago available online. • Collect high quality professional services to partner with. • Make more money. Synthesize to Understand Prototype to Validate Research to Discover
- 24. Apartment Complex Objectives • Increase visibility of their apartments. • Increase visits from prospective renters. • Increase actual rental agreements to reduce vacancies. Synthesize to Understand Prototype to Validate Research to Discover
- 25. Professional Service Goals • Increase visibility of services. • Capture business from new comers to the area and keep their ongoing business. • Increased ratings and referrals. • Create partnerships with apartment complexes to be the preferred service provider.
- 26. User Personas Jonah Porter (27 years old) “Where you live says a lot about you…” Jonah is relocating because of a transfer to Chicago from Ann Arbor, MI for his job. He’s been to Chicago several times, but just as weekend trips and is not very familiar with the different areas. About Jonah: • Doesn’t want to bring his car • Has a medium sized dog • Doesn’t cook and orders a lot of takeout • Loves an active music scene and young lively crowd Goals • Live somewhere that reflects his personality & tastes • Make friends and have an active social life. • Select a home that makes his life easier.
- 27. Tasks promote incremental improvement. Goals can lead to disruptive products or services.
- 29. Goal: Save on heating/cooling costs while maintaining optimal comfort.
- 30. What does this have to do with big data??
- 31. Now what? Synthesize to Understand Prototype to Validate Research to Discover
- 32. Remember me??
- 33. Remember this?
- 39. Practical tip: Use the right scale
- 40. Practical tip: Focus on targeted info April May June July Aug Sept Oct 2009 2014
- 41. Practical tip: Make it consumable OK Better Close to what you need: • 5 minutes to grocery store • 8 minutes to Shedd Aquarium • 10 minutes to train • 15 minutes to Millennium Park • 20 minutes to Solider Field
- 42. At a minimum, don’t go overboard.
- 43. Before
- 44. After
- 46. Connect with us. @useagility jenni@useagility.com www.linkedin.com/jenni-mitchell/ @KC_Arti AADeshpande@dstsystems.com www.linkedin.com/artiacharya/
Notas do Editor
- Very informal. Presentations make us nervous but we love a good conversation. Ask questions
- If you came to this session to learn about algorithms or scrubbing data or any of those other fancy terms you heard during the keynote, you’re probably in the wrong place. Who we are: Experienced UX professionals Passionate about making things usable Work across industries Make the complex simple and consumable Pretty. Much. Awesome.
- Those experiences are most often in domains that we don’t know much about. Complex spaces like healthcare or financial services or business process modeling. We’re designers. That’s our domain of expertise. What we’re good at is knowing how to become familiar with business and user goals. We have a lot of tools in our toolkit to figure out how to get businesses and users to their goals in a way that’s intuitive, useful and engaging.
- Big Data to us is like anything else we typically design for. We don’t know much about it to begin with. It’s not our domain of expertise.
- The thing that you love, often brings anxiety to us regular folks.
- There’s tons of data out there, but how do we make a service that’s useful for apartment seekers, while meeting business objectives? What’s your role in this? This is too much data for a user, but not to a data scientist.
- This is how most seekers are used to looking for an apartment. They enter their basic criteria and off they go. But this assumes: users know exactly where they want to live and exactly what they want. These types of searches also only rely on data provided by apartment listers. There is so much data out there in the big wide world. Why not leverage it.
- We often partner with SMEs and business folks during this research. In fact, while we’re focused on the user, you might be spotting the trends that we need to validate are useful to en end user.
- And remember, this is validation WITH users.
- We all know that data can tell a story. That’s absolutely true! Quantitative data is really valuable. However, nothing can take the place of good ol’ fashioned qualitative data.
- Some people think that because “USER experience” is so hot right now, that we don’t take into consideration business objectives. That couldn’t be further from the truth. We strive to understand business objectives because solving user needs without meeting business objectives isn’t going to keep your UX practice around for very long.
- More than just one persona – may be empty nesters or a new family
- Goals are the WHY Tasks are the WHAT Goals stand the test of the time. Tasks are transient.
- Talk about how data that’s available that’s not traditionally associated with searching for an apartment could potentially provide a very smart way of finding an apartment. Kinda like “people who buy x, also buy Y”
- We have created sometimes several different concepts or ways to organize the information. There’s not always one right clear way from the beginning. Or you or I may think we know the right way but that is why this step is so important.
- Finding a place to live is about much more than beds, baths and budget. While those are very important, there are many other elements that will contribute to a positive and happy lifestyle. Based on our research, we know some very specific things about Jonah, and a typical apartment search doesn’t even allow him to input those types of criteria, so how will we help him find where to live?
- This is how most seekers are used to looking for an apartment. How many of you have relocated to a city you weren’t familiar with? I similarly to Jonah, moved to KC for a job when I was 23. I had no real reference point to the city and wasn’t sure where to live. The only advice I received from my job was to not live further ‘east’ than a specific street. The searches then and now assumed one major question: users know exactly where they want to live and exactly what they want. These types of searches also only rely on data provided by apartment listers. There is so much data out there in the big wide world. Why not leverage it.
- Through the research we know a lot of great information about the users. But we also know our client wants to create a fun and engaging way to find an apartment that is different from the norm.
- I’m sure most everyone has taken a buzzfeed or facebook quiz, which star wars character are you? Which Disney princess are you? And here we borrow from that example to create a fun and engaging way to be matched with a neighborhood. Keep in mind this is really where the power of the data comes into play. The data scientists like some of you would help identify the trends and algorithms to make this matching process possible.
- In this phase of the process is where we would re-engage real users, have test and help validate the design. Maybe it’s right, maybe there are tweaks we need to make. In either case, it is much cheaper to do it here than do it later in the process.
- This is where we would stop and engage real users. Ask them to walk through the pages with us, does it function in the way they would expect? This is SO Important. After validating with real users, we will bring these to life. You’ve probably thought all along how ugly. Mention the length of user testing.
- We apply visual design to bring the designs to life. Often we will not share this with users until after we have tested as they can get caught up in the ‘pretty-ness’ of the design and not on wehther or not it actually works or meets their needs. This is also why our testing is task based. Requiring users to physically click through and show how they would interact is much more telling and insightful than asking them to describe what they “like” You may be thinking, where are all the visualizations? What’s important to remember is that our user (in this case) doesn’t care. They want summarized information to help make informed decisions. But we have teasers that allow them to dig in and get to all the details if they want to go deep. In this case, much of the power of the data is in helping them make informed decisions without having to weed through the details. We have a couple of practical tips for the visualizations as Jonah digs deeper.
- We know that many of you do need to use traditional visualizations when building and designing. Here are a few practical tips for how to select the right one. Let’s say Jonah wants to review the average utilities by month. He wants to review the utility bill by month, the trends are much easier to see in a line vs. a bar.
- Let’s say Jonah is looking at the crime rates for three different neighborhoods. Of course he may care about the historical perspective, but really only the last few months or year tops. Allow users to hone into specific data and really get a picture of how it will look.
- We know Jonah cares about the mobility of his neighborhood. At a glance he is looking for high level information to guide decision making. Don’t make him read through the weeds to get there.
- Don’t feel like you need to throw every possible piece of data to the users at once. Provide easy ways for them to layer and visualize the information they care about, and help them find related information they may not even know they care about. Just don’t put it all on the screen at once.
- Take the power of big data and allow users to engage and interact with it in a way they don’t even realize they are.
- Do you use any sort of user research?