Prototyping with data at Nokia

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Prototyping with data at Nokia

  1. Prototyping with data Matt Biddulph, NokiaToday I’ll be talking about how we do new product development for Ovi at Nokia, by buildingprototypes that use real data as early as possible in the design process.Photo by Joe Shlabotnik - http://flic.kr/p/6DAwoT
  2. Whether youre a new startup looking for investment, or a team at a large company whowants the green light for a new product, nothing convinces like real running code. But howdo you solve the chicken-and-egg problem of filling your early prototype with real data?Traffic Photo by TheTruthAbout - http://flic.kr/p/59kPoKMoney Photo by borman818 - http://flic.kr/p/61LYTT
  3. As experts in processing large datasets and interpreting charts and graphs, we may think ofour data in the same way that a Bloomberg terminal presents financial information. Butinformation visualisation alone does not make a product.http://www.flickr.com/photos/financemuseum/2200062668/
  4. We need to communicate our understanding of the data to the rest of our product team. Weneed to be their eyes and ears in the data - translating human questions into code, and queryresults into human answers.
  5. prototypes are boundary objectsInstead of communicating across disciplines using language from our own specialisms, weshow what we mean in real running code and designs. We prototype as early as possible, sothat we can talk in the language of the product.http://en.wikipedia.org/wiki/Boundary_object - “allow coordination without consensus asthey can allow an actors local understanding to be reframed in the context of a some widercollective activity”http://www.flickr.com/photos/orinrobertjohn/159744546/
  6. no more lorem ipsumBy incorporating analysis and data-science into product design during the prototyping phase,we avoid “lorem ipsum”, the fake text and made-up data that is often used as a placeholderin design sketches. This helps us understand real-world product use and find problemsearlier.Photo by R.B. - http://flic.kr/p/8APoN4
  7. data problem 1:how do you use it?
  8. Prototyping has many potential benefits. We use this triangle to think about how to structureour work and make it clear what insights we are looking for in a particular project.
  9. NoveltyPrototyping has many potential benefits. We use this triangle to think about how to structureour work and make it clear what insights we are looking for in a particular project.
  10. Novelty li ty id e FPrototyping has many potential benefits. We use this triangle to think about how to structureour work and make it clear what insights we are looking for in a particular project.
  11. Novelty De li ty sir id e ab F ilit yPrototyping has many potential benefits. We use this triangle to think about how to structureour work and make it clear what insights we are looking for in a particular project.
  12. Novelty De li ty sir id e ab F ilit yPrototyping has many potential benefits. We use this triangle to think about how to structureour work and make it clear what insights we are looking for in a particular project.
  13. Novelty De li ty sir id e ab F ilit y“Novelty” is when we are prototyping because we want to know if something is possible.Perhaps we’re prototyping a new kind of algorithm, or a new kind of user experience.
  14. Novelty De li ty sir id e ab F ilit y“Novelty” is when we are prototyping because we want to know if something is possible.Perhaps we’re prototyping a new kind of algorithm, or a new kind of user experience.
  15. Novelty De li ty sir id e ab F ilit y“Fidelity” is when we are prototyping to get an in-depth feel for the quality of a finishedproduct, and to see exactly how it should work.
  16. Novelty De li ty sir id e ab F ilit y“Desirability” is when we are prototyping to see if a product is actually something a userwould want. Even if we find that a product is undesirable, that is still a positive result as itallows us to “fail fast” and cancel a project before wasting time on fully implementing it.
  17. Novelty De li ty sir id e ab F ilit yMost prototypes are testing a mix of these three factors. But just like the classic “easy/fast/cheap” triangle of software quality, we find it’s hard to build a prototype that tests all threepoints of the triangle. This is why we discuss the triangle in advance, so that we know whatwe’re working towards.
  18. Novelty De li ty sir id e ab F ilit yMost prototypes are testing a mix of these three factors. But just like the classic “easy/fast/cheap” triangle of software quality, we find it’s hard to build a prototype that tests all threepoints of the triangle. This is why we discuss the triangle in advance, so that we know whatwe’re working towards.
  19. helping designers explore dataOne of the first things we do when working with a new dataset is create internal toys - “dataexplorers” - to help us understand it.
  20. explorers & data toysFor example, we have been investigating the possibilities of analysing the server logs of OviMaps, our mobile and web mapping app, to create a data-driven view of cities.This is a section of Ovi Maps centred on Los Angeles, California.
  21. LA attention heatmapWe built a tool that could calculate metrics for every grid-square of the map of the world, andpresent heatmaps of that data on a city level. This view shows which map-tiles are viewedmost often in LA using Ovi Maps. It’s calculated from the server logs of our map-tile servers.You could think of it as a map of the attention our users give to each tile of LA.
  22. LA driving heatmapThis is the same area of California, but instead of map-tile attention it shows the relativenumber of cars on the road that are using our navigation features. This gives a wholedifferent view on the city. We can see that it highlights major roads, and it’s much harder tosee where the US coastline occurs. By comparing these two heatmaps we start to understandthe meaning and the potential of these two datasets.
  23. But of course a heatmap alone isn’t a product. This is one of the visualisation sketchesproduced by designer Tom Coates after investigating the data using the heatmap explorer.It’s much closer to something that could go into a real product.
  24. Philip Kromer, InfochimpsFlip Kromer of Infochimps describes this process as “hitting the data with the Insight Stick.”As data scientists, one of our common tasks is to take data from almost any source and applystandard structural techniques to it without worrying too much about the domain of the data.
  25. Dmitriy Ryaboy reminds us that the Insight Stick doesn’t have to be made solely of exciting,complex technologies. With enough data, basic grouping and counting can give massiveinsight.He continues, “I cant tell you how many times Ive seen people get excited about doing K-means and random graph walks only to discover that actually what they want is a group-by,count, and standard deviation.”
  26. “With enough d ata you can er patterns and facts discov le counting that you using simp iscover in small data cant d ophisticated statistical using s and ML app roaches.” rasing Peter Novig on Quora paraph –Dmitriy Ryaboy http://b.qr.ae/ijdb 2GDmitriy Ryaboy reminds us that the Insight Stick doesn’t have to be made solely of exciting,complex technologies. With enough data, basic grouping and counting can give massiveinsight.He continues, “I cant tell you how many times Ive seen people get excited about doing K-means and random graph walks only to discover that actually what they want is a group-by,count, and standard deviation.”
  27. questions and answersOnce you start to understand what the data is made of, it’s great to get into a fast cycle ofquestions and answers between designers and developers. This is where familiarity with bothyour software tools and the data itself becomes critical. It’s important to focus on creativethinking around the potential products that come from the data, and not get caught up intechnology.
  28. “Listen to the data.”“Rather than burntime debatingpossible scenarios -work together to ndthe real answers.” —Pete Skomoroch, LinkedIn http://qr.ae/vYLr
  29. When we started to explore the city data, we wanted ways of communicating the “feel” of acity using everyday language and products. Tom Coates asked, “can we calculate a Starbucksindex? A metric that indicates how many Starbucks cafes there are per square mile?”Using Apache Pig, I was able to answer that question with a few lines of script and a ten-minute Hadoop job. Quick answers like this mean that the creative process isn’t interruptedby the constraints of the technology.
  30. “Products that arebuilt from data areoften constrainedin ways you didntinitially expect.” —Pete Skomoroch, LinkedIn http://qr.ae/vYLr
  31. from explorers to products
  32. LinkedIn’s Maps product - http://inmaps.linkedinlabs.com - is a lovely example of acompany using its core data, some smart algorithms and info-visualisation to communicateproduct possibilities. Posted on their LinkedIn Labs site, this isn’t a mainstream consumerproduct, but has done a great job of building buzz in the geek segment of their audience.The real challenge is to take great data-processing like this and use it to power a feature onthe main LinkedIn site that doesn’t confuse normal people with dots-and-arrows overload.
  33. data problem 2:where do you get it?
  34. nding itIf your company already has large datasets that you can use to create new products, how doyou find it? It might be a dataset they’ve licensed from a partner. It might be the logs of anexisting product that can be analysed to extract user activity. It might be buried in abusiness-reporting data warehouse.
  35. At Nokia in Berlin we’ve been working hard to improve our understanding of what data wealready own. Josh Devins, who is in charge of data gathering and analytics, started simple bycreating a data matrix on a wiki. Each line lists a source of data - an appserver log, a mysqldatabase, a partner datadump - and catalogues attributes such as “is it timestamped? does ithave user IDs? how frequently is it collected? what date did we start collecting it? who is theresponsible team? can I find it on the Hadoop filesystem yet?”
  36. faking itIf you’re working on a new product, you need a way to envisage what it’s going to feel likewhen it’s got a million users and the data is flowing through it. In this case, you probablydon’t have the datasets you need already available in your organisation.
  37. In this case, we turn to open web APIs and datasets. Working on an app with a social graph?Create dummy users from a crawl of the Facebook or Twitter social graph APIs. Need to fill itwith fake user content? Use real blog posts via RSS feeds to seed the CMS.http://www.blogperfume.com/new-27-circular-social-media-icons-in-3-sizes/
  38. In this case, we turn to open web APIs and datasets. Working on an app with a social graph?Create dummy users from a crawl of the Facebook or Twitter social graph APIs. Need to fill itwith fake user content? Use real blog posts via RSS feeds to seed the CMS.http://www.blogperfume.com/new-27-circular-social-media-icons-in-3-sizes/
  39. In this case, we turn to open web APIs and datasets. Working on an app with a social graph?Create dummy users from a crawl of the Facebook or Twitter social graph APIs. Need to fill itwith fake user content? Use real blog posts via RSS feeds to seed the CMS.http://www.blogperfume.com/new-27-circular-social-media-icons-in-3-sizes/
  40. When we worked on a prototype of improving a mobile photo gallery with social and dataintelligence features, we realised that the demo would be more powerful if it was full of yourown pictures. So we made a prototype that starts by asking you to log into your Flickraccount, and then populates itself with your own photos.
  41. “just because the ‘grain’ isn’t always as obvious as with wood doesn’t mean it’s not there, it just takes different skills to nd it.” —Dan Catt, The GuardianEverything we’ve talked about in this presentation brings a data way of thinking to existingprocesses and skills. Working with real data takes practice and experience.
  42. “We threw out custom non- hadoop code that was faster.” —Jay Kreps, LinkedInThink about how you use your data-processing tools when you prototype. Don’t let themslow you down. Optimise for creativity and speed, not technical perfection.http://www.flickr.com/photos/russss/3630698158/
  43. summing up...all points of the triangle helped by dataknow what data is available to you, inside and outside your teamuse real data to unite design and tech, contextualise prototypes, answer questions, findproblems early
  44. Novelty De li ty sir id e abF ilit y
  45. Novelty real data helps all points of the triangle ty De eli sirFid ab ilit y
  46. look for analoguesin existing datasets
  47. explore thedata together
  48. Thank you @mattb | matthew.bidduph@nokia.comPlease email me if this is the kind of work you’d like to be doing.Photo by Joe Shlabotnik - http://flic.kr/p/6DAwoT
  49. Noki a is hirin g! Thank you @mattb | matthew.bidduph@nokia.comPlease email me if this is the kind of work you’d like to be doing.Photo by Joe Shlabotnik - http://flic.kr/p/6DAwoT

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