Presented at Strata 2011 http://strataconf.com/strata2011/public/schedule/detail/16901

1. Prototyping with data
Matt Biddulph, Nokia
Today I’ll be talking about how we do new product development for Ovi at Nokia, by building
prototypes that use real data as early as possible in the design process.
Photo by Joe Shlabotnik - http://flic.kr/p/6DAwoT

2. Whether you're a new startup looking for investment, or a team at a large company who
wants the green light for a new product, nothing convinces like real running code. But how
do you solve the chicken-and-egg problem of filling your early prototype with real data?
Traffic Photo by TheTruthAbout - http://flic.kr/p/59kPoK
Money Photo by borman818 - http://flic.kr/p/61LYTT

3. As experts in processing large datasets and interpreting charts and graphs, we may think of
our data in the same way that a Bloomberg terminal presents financial information. But
information 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. We
need to be their eyes and ears in the data - translating human questions into code, and query
results into human answers.

5. prototypes are
boundary objects
Instead of communicating across disciplines using language from our own specialisms, we
show what we mean in real running code and designs. We prototype as early as possible, so
that we can talk in the language of the product.
http://en.wikipedia.org/wiki/Boundary_object - “allow coordination without consensus as
they can allow an actor's local understanding to be reframed in the context of a some wider
collective activity”
http://www.flickr.com/photos/orinrobertjohn/159744546/

6. no more
lorem ipsum
By 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 placeholder
in design sketches. This helps us understand real-world product use and find problems
earlier.
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 structure
our work and make it clear what insights we are looking for in a particular project.

9. Novelty
Prototyping has many potential benefits. We use this triangle to think about how to structure
our work and make it clear what insights we are looking for in a particular project.

10. Novelty
li ty
id e
F
Prototyping has many potential benefits. We use this triangle to think about how to structure
our 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
y
Prototyping has many potential benefits. We use this triangle to think about how to structure
our 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
y
Prototyping has many potential benefits. We use this triangle to think about how to structure
our 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 finished
product, 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 user
would want. Even if we find that a product is undesirable, that is still a positive result as it
allows 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
y
Most 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 three
points of the triangle. This is why we discuss the triangle in advance, so that we know what
we’re working towards.

18. Novelty De
li ty sir
id e ab
F ilit
y
Most 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 three
points of the triangle. This is why we discuss the triangle in advance, so that we know what
we’re working towards.

19. helping designers
explore data
One of the first things we do when working with a new dataset is create internal toys - “data
explorers” - to help us understand it.

20. explorers & data toys
For example, we have been investigating the possibilities of analysing the server logs of Ovi
Maps, 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 heatmap
We built a tool that could calculate metrics for every grid-square of the map of the world, and
present heatmaps of that data on a city level. This view shows which map-tiles are viewed
most 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 heatmap
This is the same area of California, but instead of map-tile attention it shows the relative
number of cars on the road that are using our navigation features. This gives a whole
different view on the city. We can see that it highlights major roads, and it’s much harder to
see where the US coastline occurs. By comparing these two heatmaps we start to understand
the 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 sketches
produced 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, Infochimps
Flip 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 apply
standard 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 massive
insight.
He continues, “I can't tell you how many times I've 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
can't d
ophisticated statistical
using s
and ML app roaches.” rasing Peter Novig
on Quora
paraph
–Dmitriy Ryaboy http://b.qr.ae/ijdb
2G
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 massive
insight.
He continues, “I can't tell you how many times I've 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
answers
Once you start to understand what the data is made of, it’s great to get into a fast cycle of
questions and answers between designers and developers. This is where familiarity with both
your software tools and the data itself becomes critical. It’s important to focus on creative
thinking around the potential products that come from the data, and not get caught up in
technology.

28. “Listen to the data.”
“Rather than burn
time debating
possible scenarios -
work together to nd
the 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 a
city using everyday language and products. Tom Coates asked, “can we calculate a Starbucks
index? 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 interrupted
by the constraints of the technology.

30. “Products that are
built from data are
often constrained
in ways you didn't
initially 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 a
company using its core data, some smart algorithms and info-visualisation to communicate
product possibilities. Posted on their LinkedIn Labs site, this isn’t a mainstream consumer
product, 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 on
the 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 it
If your company already has large datasets that you can use to create new products, how do
you find it? It might be a dataset they’ve licensed from a partner. It might be the logs of an
existing product that can be analysed to extract user activity. It might be buried in a
business-reporting data warehouse.

35. At Nokia in Berlin we’ve been working hard to improve our understanding of what data we
already own. Josh Devins, who is in charge of data gathering and analytics, started simple by
creating a data matrix on a wiki. Each line lists a source of data - an appserver log, a mysql
database, a partner datadump - and catalogues attributes such as “is it timestamped? does it
have user IDs? how frequently is it collected? what date did we start collecting it? who is the
responsible team? can I find it on the Hadoop filesystem yet?”

36. faking it
If you’re working on a new product, you need a way to envisage what it’s going to feel like
when it’s got a million users and the data is flowing through it. In this case, you probably
don’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 it
with 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 it
with 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 it
with 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 data
intelligence features, we realised that the demo would be more powerful if it was full of your
own pictures. So we made a prototype that starts by asking you to log into your Flickr
account, 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 Guardian
Everything we’ve talked about in this presentation brings a data way of thinking to existing
processes and skills. Working with real data takes practice and experience.

42. “We threw out
custom non-
hadoop code
that was faster.”
—Jay Kreps, LinkedIn
Think about how you use your data-processing tools when you prototype. Don’t let them
slow 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 data
know what data is available to you, inside and outside your team
use real data to unite design and tech, contextualise prototypes, answer questions, find
problems early

44. Novelty De
li ty sir
id e ab
F ilit
y

45. Novelty
real data helps all
points of the triangle
ty De
eli sir
Fid ab
ilit
y

47. look for analogues
in existing datasets

50. explore the
data together

51. Thank you
@mattb | matthew.bidduph@nokia.com
Please email me if this is the kind of work you’d like to be doing.
Photo by Joe Shlabotnik - http://flic.kr/p/6DAwoT

52. Noki
a
is
hirin
g!
Thank you
@mattb | matthew.bidduph@nokia.com
Please email me if this is the kind of work you’d like to be doing.
Photo by Joe Shlabotnik - http://flic.kr/p/6DAwoT