Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Â
Prototyping with data at Nokia
1. Prototyping with data
Matt Biddulph, Nokia
Today Iâll 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://ďŹic.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 ďŹlling your early prototype with real data?
Traffic Photo by TheTruthAbout - http://ďŹic.kr/p/59kPoK
Money Photo by borman818 - http://ďŹic.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 ďŹnancial information. But
information visualisation alone does not make a product.
http://www.ďŹickr.com/photos/ďŹnancemuseum/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 - âallow coordination without consensus as
they can allow an actor's local understanding to be reframed in the context of a some wider
collective activityâ
http://www.ďŹickr.com/photos/orinrobertjohn/159744546/
6. no more
lorem ipsum
By incorporating analysis and data-science into product design during the prototyping phase,
we avoid âlorem ipsumâ, 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 ďŹnd problems
earlier.
Photo by R.B. - http://ďŹic.kr/p/8APoN4
8. Prototyping has many potential beneďŹts. 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 beneďŹts. 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 beneďŹts. 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 beneďŹts. 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 beneďŹts. 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
âNoveltyâ is when we are prototyping because we want to know if something is possible.
Perhaps weâre 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
âNoveltyâ is when we are prototyping because we want to know if something is possible.
Perhaps weâre 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
âFidelityâ is when we are prototyping to get an in-depth feel for the quality of a ďŹnished
product, and to see exactly how it should work.
16. Novelty De
li ty sir
id e ab
F ilit
y
âDesirabilityâ is when we are prototyping to see if a product is actually something a user
would want. Even if we ďŹnd that a product is undesirable, that is still a positive result as it
allows us to âfail fastâ 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 âeasy/fast/
cheapâ triangle of software quality, we ďŹnd itâs 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
weâre 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 âeasy/fast/
cheapâ triangle of software quality, we ďŹnd itâs 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
weâre working towards.
19. helping designers
explore data
One of the ďŹrst things we do when working with a new dataset is create internal toys - âdata
explorersâ - 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. Itâs 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 itâs 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 isnât a product. This is one of the visualisation sketches
produced by designer Tom Coates after investigating the data using the heatmap explorer.
Itâs much closer to something that could go into a real product.
24. Philip Kromer, Infochimps
Flip Kromer of Infochimps describes this process as âhitting the data with the Insight Stick.â
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 doesnât have to be made solely of exciting,
complex technologies. With enough data, basic grouping and counting can give massive
insight.
He continues, âI 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.â
26. âWith 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.â rasing Peter Novig
on Quora
paraph
âDmitriy Ryaboy http://b.qr.ae/ijdb
2G
Dmitriy Ryaboy reminds us that the Insight Stick doesnât have to be made solely of exciting,
complex technologies. With enough data, basic grouping and counting can give massive
insight.
He continues, âI 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.â
27. questions
and
answers
Once you start to understand what the data is made of, itâs 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. Itâs important to focus on creative
thinking around the potential products that come from the data, and not get caught up in
technology.
28. âListen to the data.â
âRather than burn
time debating
possible scenarios -
work together to nd
the real answers.â
âPete Skomoroch, LinkedIn
http://qr.ae/vYLr
29. When we started to explore the city data, we wanted ways of communicating the âfeelâ of a
city using everyday language and products. Tom Coates asked, âcan we calculate a Starbucks
index? A metric that indicates how many Starbucks cafes there are per square mile?â
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 isnât interrupted
by the constraints of the technology.
30. âProducts that are
built from data are
often constrained
in ways you didn't
initially expect.â
âPete Skomoroch, LinkedIn
http://qr.ae/vYLr
32. LinkedInâs 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 isnât 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 doesnât confuse normal people with dots-and-arrows overload.
34. nding it
If your company already has large datasets that you can use to create new products, how do
you ďŹnd it? It might be a dataset theyâve 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 weâve 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 âis 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 ďŹnd it on the Hadoop ďŹlesystem yet?â
36. faking it
If youâre working on a new product, you need a way to envisage what itâs going to feel like
when itâs got a million users and the data is ďŹowing through it. In this case, you probably
donât 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 ďŹll 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 ďŹll 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 ďŹll 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. âjust because the âgrainâ
isnât always as obvious as
with wood doesnât mean
itâs not there, it just takes
different skills to nd it.â
âDan Catt, The Guardian
Everything weâve 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. âWe threw out
custom non-
hadoop code
that was faster.â
âJay Kreps, LinkedIn
Think about how you use your data-processing tools when you prototype. Donât let them
slow you down. Optimise for creativity and speed, not technical perfection.
http://www.ďŹickr.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, ďŹnd
problems early
51. Thank you
@mattb | matthew.bidduph@nokia.com
Please email me if this is the kind of work youâd like to be doing.
Photo by Joe Shlabotnik - http://ďŹic.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 youâd like to be doing.
Photo by Joe Shlabotnik - http://ďŹic.kr/p/6DAwoT