I good morning everyone and uh see ya
yes thank you for inviting me to sleep
with you this morning um does this
middle one hop up as well no okay well
I'll have to sort of look over here then
um so I wanted to talk to you about how
we as practitioners are using data and
really sort of picking up on sort of
Christopher's taught this morning how do
we think about setting the right
conditions for use of our data using it
properly and sort of thinking about and
working very closely with our previously
team to make sure we're safe guarding
our customers data as we do this I'll
give you a little bit of background
about Transport for London many of you
know most of you have used our services
even sort of been coming here for the
conference Londoners of course use our
services all the time but we we provide
to boss street network services traffic
signals walking and cycling facilities
and I have a cable Emirates Airline
cable car and so we help people move
around London and but for us it's not
just about thinking about providing a
tube service and we have to think about
it as a tube service but we really want
to think about our customers and we need
to think about what is our purpose
Transport for London it sets out what
we're doing and this is really to help
London work and grow and make life in
London better and some of the challenges
we're facing is we have a growth of
population in London where 8.6 million
Londoners today we're expected to be 10
million by 2030 which is a phenomenal
sort of increase and we have to meet
that challenge of providing services
with growth and population and we also
can have to understand the rising
expectations of our customers in getting
around and certainly this is the
transformation of the digital world has
meant that our customers expect us to
sort of understand their travel and
provide services to that and so we
really need to understand what our
customers want and they they want to
understand who we are and what we're
doing they want to understand that we're
providing them reliable
variances on the on the network services
and a good customer experience value for
money and we need to show them that
we're thinking about progress and
innovation but what underpins all of
this is trust means they have to trust
that we are doing the right thing and we
have to understand that trust is our
license to operate so this is crucial
that we must get this right and so I'll
talk to you a bit about our data now as
I said London London's big in our data
is big and there's a whole set of range
of sources of data that we use in the
organization we have and I'll talk a lot
about how we use our ticketing data and
and that is our oyster ticketing our
contact us payment information and
magnetic tickets we also have a social
media data that we sort of use to
understand how our services are running
we use and I'll talk also about our best
location data and in particular how we
use that to understand how our services
are running and provide information back
to our customers and operators asset
data where our trains are and then we
have a range of sort of network
monitoring sensors on our road so
cameras CCTV we have we have traffic
signal data that provides information
about how our traffic signals are
running and then we also have just
generals with other traffic information
sensors in the road and then you'll see
sort of a dotted line and I'm going to
talk about this a little bit later which
is about apps and mobile Wi-Fi data man
this is a dotted line because this is
something that we are currently
exploring and this is actually an area
where we've gotten a huge amount of help
from the ICO in terms of giving us
guidance about how do we approach this
question because I'm going to talk to
you later about the potentials here and
how we need to think about this where
have we can unlock this this oil here
but being very careful about how we do
this and safeguard our customers and our
data and so really we take very very
seriously how we protect our personal
data as we think about our big Gator
approach and so we we do this in the
range of ways we think about how do we
have a transparent approach to privacy
so at the end you'll see the sort of the
link to our privacy pages on the on our
website we provide information at
point that we cook that we collect the
information but we also direct people
for more information on our website and
we provide this in a clear sort of
language so that people can really
understand it and understand how we're
using the information we have we do
privacy impact assessments and my team
works very very closely with the privacy
and data protection team and we really
sort of set out we discuss what we're
trying to do and think about the think
about this and then we should make sure
that all the controls are in place to
have properly sort of manage our data
and we have a regular engagement with
the ico we rely on their guidance so the
guys in some big data the guidance on
anonymization the februari guidance on
Wi-Fi have all been hugely helpful and
of course the twelve step so that sheet
that Christopher held up earlier has
also been very helpful for for us um and
then of course we this is something
that's that we look at it the senior
level and we make sure that all of ours
have employees who are handling customer
data and personal data are really
understand the importance of it we have
a training we make sure that we have
controlled access and we think about the
types of data the people access to do
their jobs and we're very precise about
who gets access to what data and so a
little bit of statistics about our data
so we have 26 million of the sort of
retail sort of Blue Oyster cards used in
about a year and we have about 4 million
of those that are registered with us we
have a concessionary photo card schemes
which are basically sort of discount or
free travel and we have 1.6 million of
these oyster photo cards used in the
past year we have nine point three
million contactez payment cards used in
the past 12 months this is an increasing
number because we're seeing a great
take-up of contactless payment on our
network this is so in addition to the
sort of the oyster card which many of
you will have traveled with this is a
basically able to use your bank card on
our network where you can just sort of
tap and go and we sort of introduced
this I'm in September 2014 and it's been
sort of a huge success for us in terms
of getting customers to not have to buy
their top up their oyster card and then
travel you can just sort of take your
bank card and travel
the network we then have a number of
sort of congestion charging and accounts
and customer records from the low
emission zone sent him there Cycle Hire
and then license taxing private hire
drivers and then our camera network so I
wanted to start and talk to you first
about our travel pattern analysis so
this is so this is what you how do used
to be used some of this data on that we
have in this first example it's just
just a small question that says okay
when you're looking at the network you
can sort of see how to do things change
over time and this is where we can sort
of take data I mean look at winter
wonderland so for those who are not
Londoners we have every year into the
November about a three-week sort of
festival in Hyde Park where we set up
sort of so fairground stalls and and
it's sort of very it's very fast tube
and it's very crowded particularly the
stations right around Hyde Park and so
we look we look at sort of to see how
how these sort of station performs the
week before who's coming and going
versus a week of the festival and it's
just a way to sort of show how do you
see in numbers what people would sort of
see it's a by observation but it's
something that's of a straightforward
exercise that we can do to sort of show
what's happening and we can sort of
compare at year after year and we look
at that trend this is an example where
we look at what happens when we have a
closure of the network so we have been
doing a lot of infrastructure
improvements Tottenham Court Road was
closed for a while depending at the time
it was either close to the central line
which the red line or the Northern Line
which is a black line because we've been
bringing in a new cross rail station so
one of the questions that we were asked
is okay well where did people go when
the station was closed or and then this
is when the stations open where were
people before and how did they switch
back and so again this is sort of an
interesting question to sort of see
what's happening it's interesting and
from an academic and intellectual point
of view it's also really useful because
we have conversations with retailers
because they you know they were
concerned so what's happening who's
who's traveling where are they going are
we losing people are people not
traveling and this is you can sort of
say okay we're we're looking at the
points of where customers are entering
and entering and exiting on the network
and see what they do when something
changes and so that's the work that
we've done on the tube that's actually a
lot of that data is just entry and exit
data we have aggregated that up and once
we do that you can actually open that
data up and so one of the things we're
also trying to do is provide as much
open data as possible we've done some
aggregated buckets of these sort of
entry and exit data and we have it on
our website we have some sort of sample
data sets out there and we're looking to
do more that this example I'm going to
talk to you about is an area where
actually it's we need more sophisticated
analysis because this is answering a
different question right the tube
question is actually fairly
straightforward because you have a tap
when someone enters you have a tap on
someone exits and you know what the
journey is you know who's coming in now
it's a fairly easy count but the
challenge we had was what do we do for
the bus network because you have a tap
on on our buses nobody caps off which
meant that before we should have built
an algorithm we were reliant upon people
handing out surveys so we did two things
we had people standing with clipboards
so like seeing how full buses looked
seeing where the bus was at a given
point in time like really manually
recording where the buses were and we
also had people handing at paper surveys
to say where did you come from where did
you go to but of course the survey is
very expensive you can only do a limited
period of time there is survey bias
because people going short distances
aren't going to fill out a survey so
it's not it's not as precise it's not as
helpful in terms of planning our bus
network so we worked with MIT we
actually do a number work with
universities where we gave them a
pseudonym eyes datasets under a
nondisclosure agreement and we work with
them to tackle somebody really
interesting questions on this so we we
worked with them to build an algorithm
which essentially takes we know where an
oyster card is because it records the
sort of location and the time stamp and
we know where someone taps on to
bus so if someone agrees on the bus the
bottom of the green and then we but they
don't we don't know where they got off
but we know where they tap on again so
if you see a first tap on the green line
and then you see the second tap say at
stop X on the red bus route we make an
inference that likely people exited on
stop a and or if someone's on the on the
underground next you assume that they
got off at the stop closest to the
underground to stop be and that well
you're not a hundred percent confident
that gives you a sort of a good
indication of how people are traveling
on the network and so we do this some of
these really interesting but just
interesting is not enough it's got to be
actually useful particularly when you're
still looking at customer information
customer data you always have to ask the
question about why are we doing this um
and what's the business value and what's
the business benefit and we use this to
start with one of the good examples was
in 2014 putney bridge had to close for
emergency repair works and we had the
sort of we found out very quickly with
sort of emergency works we had to figure
out what would happen because people
could walk across the bridge and people
could cycle across but you couldn't
actually take a bus across who could
drive cross so suddenly we looked had to
say how do you tackle this and so we
looked at weeks with the data and we
found forty thousand unique oyster cards
and they made a hundred and eleven
thousand bus journeys across the bridge
there were about eight hundred and forty
thousand journeys on those bus routes
but you know some of them were miles
away from the bridge so it didn't matter
you really wanted to understand who is
crossing the bridge and then about half
of these journeys started or ended close
to the bridge so for these journeys you
would actually find that you would
people would take one bus get off walk
across the bridge or walk across the
bridge get on a bus and it was be one
bus trip for them not ideal because it
had to walk across the bridge but it's
one bus about half actually have the bus
journey right in the middle and so were
these customers they were going to have
to take two buses and so for these
customers we did a bus transfer card so
we had to know roughly to scale the
problem and think about how does it help
our customers
and so we created this it wasn't very
high-tech it's fine with facing paper
card we hand it out but it worked and
then we also sort of we plan some
additional routes around the area so
people could severe out and we send
email to those who would be affected
just to minimize the impact and that
takes us over to how we're using to a
bar our customer information to provide
relevant and sort of timely emails to
our customers and we do this who rested
our customer database and we look at how
do we provide information that customers
will find useful by understanding their
travel patterns the recent travel
patterns and then sending them
information so if someone lives in South
London we won't necessarily send them
something about East London we say we've
there just cuz you don't we send out I
probably can send out 20 or 30 emails a
day we have to make sure we're sending
them out that are relevant to the
customers and we also will sense a very
broad base sort of information if
there's I'm a big event or strike or
weekend closures but otherwise we want
to get very precise and useful
information and what we do is you've
done surveys to see how relevant people
find this information and we do have a
good to return we're eighty-three
percent of our customers say that the
email services are useful so then this
is the what next piece right because
this is all we have to be thinking about
what do we do next with our with our
data how can we help provide better
services to our customers and of course
we all have to do this in the context of
the gdp are so we are we are sort of
doing more work on this right now and
we're sort of really working to further
embed even more emphasizes privacy by
design and a date of minimization
culture and we are really looking
through how we review how we're seeking
and obtaining and recording our consents
we're looking about you the difference
of Asus in which we carry out different
types of data processing we're
strengthening and testing our data
breach processes and procedures we and
then we also want to look at documenting
our data processing activities I'm
really going through very precise
exercise to do this and this and and
then lastly we really sort of have to
understand an update and right in plain
English our privacy notices and just
make sure that we are as clear and as
trans
and as possible and this goes back to
the sort of this regulatory importance
and also this fundamental need to make
sure our customers understand and trust
and we earn their trust to make sure
we're taking this seriously so and then
I want to talk to you a little bit about
what we would be doing further and we're
still exploring some of the stuff and
this is where we're thinking about how
do we challenge in the challenge of it
okay okay good back and how do we think
about the challenge of a right rising
numbers of customers on our our services
how do we help manage our service better
and provide useful information to them
and there's a range of ways but we were
thinking of doing this this is an
example where we look at customer
journey times from tap to tap on the
network we segregate them up and then we
look at what a regular sort of usual
journey time might be and then we also
can provide that information on what the
current journey time is and this is
helpful so if there's a problem on a
line you might say oh actually you know
it I know normally this takes 10 minutes
but it's taking 14 minutes that's
probably okay I mean if things are
congested things slow down a bit because
people are the doors have to stay open
longer as people getting on and off it's
just generally slower you might not get
the first train but it's useful
information to be transparent to our
customers about what's happening on the
network and then of course if you do a
problem you can proactively tell them
and they may take a different route or
change your travel and this is where
again we're thinking about how do we
provide this information about just
general to the state of the network and
this is an example where we're looking
at crowding and actually our Victoria
Line trains they actually the most
useful precise data about how heavy a
train is at the moment they weigh
themselves from the signalling system
and so you can take that which is really
cool our other trains do that too
although the Victoria Line trains give
is the best data right because some of
the others don't the they don't get as
much detail but it's you can see you
know how heavy a train is when it's
empty and then you make assumptions on
how have how heavy the trains are when
you have people on them and yeah yeah
you make some assumptions about that way
to people but yeah but it gives you a
sense
and then so then what we can do is if we
know how people are where people are on
the network out or and historically have
either historically or in real time you
can begin to predict what the trains are
going to look like when they get to your
station um which is particularly useful
if you have different routes you might
go to you know so you know for example
if you're a green park maybe you take
the Piccadilly line into the Victoria
Line the Victoria Line may be faster but
if it's really crowded and you have to
wait three trains to get on it you're
better off taking the Piccadilly line
same for you student for example with
the northern line and the and the
Victoria Line and so we can sort of
provide this information to customers
that is relevant and useful to them this
is sort of direction we're trying to
explore um the other thing we're looking
at doing is how if people want to opt in
for even more personalized services
we're exploring how this might work so
if people were to choose to register
their device I and we could provide them
even more specific information relevant
to them and you might choose to register
your device on a desktop laptop and you
might even choose to share your location
and this is something where the ICA was
really helpful in terms of beginning to
explore this with us and giving us some
suggestions on how to approach this and
they suggested we do some customer
research which was sort of hugely
helpful just to make understand what our
customers were thinking would they value
this I'm is this a service that if you
could opt in what people like it so we
did a little bit of research on this
we're just to say okay we gave them for
different types of scenarios of how we
might collect their data and we asked
different sort of groups about what they
felt and customers were positive about
this they said they understood
particular understood the purpose why
they really felt that they wanted to
understand the benefits to them and when
we explained we could do some of these
things like provide very specific
information about the route that they
were going to take in the mornings if it
was delayed and send them a message they
were comfortable with that you know
understanding they could opt in to get
this information but and they were clear
that they want it they we needed to be
clear we needed to be transparent about
what we're doing we needed to explain it
we needed to communicate why we were
doing this
and and actually deliver benefits and
make it useful to them and that was
really important to make sure that we
were doing that and then we would also
need to sort of reassure our customers
and we were taking some data protection
and protecting their data and securing
their data and so some of the things
that we're exploring now would be how we
might do this with sending sort of
notification some of this we can do this
already so we do refunds so in fact my
team today are looking at the impact of
yesterday we had a problem on the
Jubilee Line where we had some sort of
customers who were affected by service
outage we had some signal failures and
so we already have a people can go on to
our website if they were delayed for
more than 15 minutes and claimed for
their journeys they put in their journey
information and we validate did they
actually travel and then we offer them a
refund back but we on a bad day and it
looks like yesterday is a bad day we'll
see how many people are affected and if
it was a significant impact we will
proactively just push out refunds to
customers who were affected but we're
also looking at other notifications that
we could do particularly things like if
we sort of can push information to
mobile devices in the future send people
text messages email them and certainly
the more information we have about
customers travel patterns a better week
more relevant information we can give
and this is the trade-off right so we if
we our customers want to share that
information with us we can sort of
design relevant information that's
specific back to them but it's making
sure that we have the right trade-off
and that they feel that there's value a
tangible value that they'll see at a
trust in us that we have to deliver on
and so this is where we're going in
terms of how we're so thinking about the
integrated network it's a last bit also
to just say is that we also interested
in in the sort of connection between
people who are are driving on the
network and walking and cycling and when
they're traveling on the public
transport network and so again thinking
about ways we can understand how they're
using our network end-to-end is really
helpful for us to help provide
information how they can use it better
so that's essentially we for now and
here's our link to our privacy pages and
I email so thank you very much for
inviting me to speak