You're listening to a stage talk titled Digging
for Gold in Import -Export Data. In this episode,
we're joined by my colleague and Bellingcat research
consultant, Catherine de Tolly, who brings a
year's worth of investigative learnings from
exploring the hidden stories buried within global
trade databases. Catherine walks us through key
insights she's uncovered. explains why this kind
of data can be both powerful and perplexing,
and breaks down a real investigation to highlight
common gaps, blind spots, and pitfalls researchers
are likely to encounter. You can find links to
all the resources mentioned in the talk in the
podcast description. This talk was hosted by
me, Charlotte Ma, on Thursday the 4th of December
2025 in the BellyCat Discord server. Hi, hello
all. Thank you for coming to our final Stage
Talk of 2025. If this is your first time listening,
you can find all previous episodes on our RSS
feed or on podcast platforms by searching Stage
Talks with Bill and Cat. I'm sure one of our
lovely mods can pop that in the chat. We talk
about many different topics associated with open
source research, from covering conflict, mapping
environmental damage to building ship tracking
tools. Today, though, we're diving into financial
investigations. Catherine Gittoli, Bellingat
Consultant and general amazing researcher is
here to share her tips and tricks for shifting
through import and export trade data. From finding
the elusive information to sorting out what really
matters in all of the numbers, listening as she
shares her tried and tested know -how. Catherine
has worked on major financial investigations
uncovering who is behind one of the world's largest
deep fake porn sites, The tracking online adds
for dangerous drugs to Chinese trade market sites
and social media. Whilst we talk, you can place
your questions in the chat accessible on the
right hand corner of your screen. But please
remember that this is being audio recorded for
the podcast. So if you don't want me to mention
your username, please add that as a note in your
question. You may have noticed that our cameras
are off for this particular talk as well. Please
don't panic. That's on request of the speaker.
But we will be sharing screens, so just be prepared
to also follow along as Catherine shares her
slides. Okay, Catherine, over to you. Fantastic.
Hello, everyone. It's wonderful to see so many
people here. I didn't know that so many people
were going to be interested in this obscure area
of trade data. And certainly I found when I started
using trade data that it was quite hard to find
people that knew about it. So I'm hoping that
some of you might be experts and you're welcome
to pop notes in the chat while I'm talking if
you think I'm talking rubbish or if something
isn't clear because this has really been this
year has been an area of exploration for me and
as always I'm learning. So I am going to switch
to sharing my screen and I will start my presentation.
So I'm talking about what you can dig for in
trade data. So trade data is really import -export
data, and it's pretty obvious that when goods
leave a country or come into a country, then
data is collected. For instance, because of the
kind of legal processes around shipping, who's
liable for what, when, because of customs duties
and taxations, because governments need to know
You know, when goods have left a country, that
kind of thing. So that's really what trade data
is. What I'm showing on my screen now is a generic
version of what's called a bill of lading. So
bills of lading have been around for donkey's
years, many, many, many decades. And they're
really used to capture data related to a particular
shipment. So there'll be things like the name
of the exporter, the name of the consignee or
the importer. There'll be a description of the
goods, the value of the goods, which country
the goods are going into. These bills of lading
are not necessarily standardized between countries,
but they do tend to capture wherever it's going
to. So I want to really explain to you like why
why use it because I felt like a bit of an outlier
at Bellingcat when I started using trade data
because nobody else really used it and I thought
okay well let's look at other examples of investigations
where trade data has been used. This is one of
my favorite stories and it really inspired me
and I would encourage you to go and look it up
like the BBC. What they did was they had a tip
-off that Indian pharma companies were sending
a very dangerous opioid across to West Africa.
And what they did was they looked at trade data,
so publicly available export data, and they were
able to find that a particular company, Avio
Pharmaceuticals, there were other companies,
but Avio was sending a lot of these drugs across,
and they then sent an undercover journalist in
to interview this charming man who explained
his, let's say, lack of ethics around exporting
dangerous opioids to West Africa. So the trade
data here was really useful for them because
they were able to see, well, who in India is
exporting these drugs in bulk to West Africa?
And then they could use that information to deepen
their investigation, to take the next step. Another
example is the New York Times, and they used
trade data along with other sets of data. They
had a tip off that Boeing parts were making their
way to sanctioned Russian airlines, which they
should not have been. And they were able to use
trade data and this other data to then track
where these goods were going, because they were
obviously not going straight from the US to Russia.
They were going from the US to the UAE to somewhere
else. They were using corporate registries, a
whole bunch of different data, but they were
able to show that effectively sanctions were
being bussed with Boeing parts, which is, you
know, that's pretty big. The next one is using
another set of data, which is Comtrade. That's
a UN system that that countries all submit their
own data to. So they submit their annual trade
data to the UN, who then makes it available in
a system called ComTrade. It's different from
the kind of trade data that I'm talking about
that I'll show you in a minute. This is more
categorized data. If you want to be technical,
it's HS code level data. So you won't be able
to see specific shipments, but you'll be able
to see that a whole bunch, in this case, it was
really cigarettes going into Mali. And they would
then compare that. They could see on Comtrade
the volumes that were going into Mali, and they
compared that to local demand for cigarettes
and local production of cigarettes. And then
they could see that a lot of cigarettes were
being sent from Mali into the Sahel region, and
this was not legal. But again, they used the
trade data to help them kind of kick off their
story and quantify things. But it wasn't the
end point. And I'll keep saying this, that the
data is not the story. The trade data helps you
to get insights into a problem that you're investigating,
but it's obviously not going to tell you everything.
So what I'm going to move on to is to talk about
an investigation that I've been busy with. And
what happened was I started looking at health
and Africa, which is obviously a very broad topic,
but I'm from South Africa and I really wanted
to find an Africa relevant story. And then I
happened to read a UN report which was on drug
trafficking and it mentioned a particular drug.
that they said was taking over from other drugs
that were now more heavily regulated by India.
And I thought, oh, that's pretty interesting.
They were saying, hmm, it looks like the exporters,
the Indian companies might be shifting to a different
drug. And I thought, okay, well, let me have
a look. So I went and I used free trade data.
So the providers that make this data available,
like Import Genius, Export Genius, Volza, there's
so many of them. Some of them make little bits
of free data available online. And I was then
able to, I mean, this was literally manual copy
and pasting, but it was a way for me to learn
and see, okay, is what the UN is saying. about
this particular drug to pentadol. Is it actually
being exported from India to West Africa? And
I thought, okay, just use free traded. And then
I could pick up some patterns. Like I could see
supplier names and I could dig into them a bit.
I could see buyer names. I then went and did
some other research and I was like, oh, well,
that's very interesting. This drug is not legal
in Ghana. How can it be? that it's being exported
from India to Ghana, yet it's not legal. So I
started with free trade data and manual copy
and paste. Now, as I mentioned, there are many
providers. They have vastly different prices.
So some of the top end ones like Panjiva, I think
are like $12 ,000 a year. will provide it free.
Like Import Yeti, if you are doing US data, you
can get free access to data if you're a researcher,
like doing an open source journalist. Import
Genius provides free data as well. We didn't
know this at the time that I started needing
data, so we bought for $1 ,000 a year access
to 52 WMB. So I'm going to just quickly show
you so that it looks a little bit more real what
I'm talking about when I talk about using a trade
data source. You can see this is a pretty standard
kind of UI where you choose your country, you
choose that you want to export or import data,
you choose your date period, and then here I
happen to put the drug that I wanted. You can
also Search by supplier name or buyer name depending
on the country because as I said different countries
provide They capture different data on the bills
of lading so what I did was I then did searches
within 52 WMB and I downloaded The data for Depend
et al and it ended up looking something like
this So this was where I consolidated to pentadol
exports from India to West African countries.
And being a bit of a data monster, this made
me really, really happy because it was, yeah,
I'll show you my favorite picture. The cookie
monster got her data. And finally, instead of
manually copying and pasting into a sheet, which
was never going to scale, I could actually see
proper amounts of data and I could start to play
with it. So as always, and I'm sure many of you
know this, when you get access to data, it is
not perfect. That is not the nature of data.
So I had to do things like clean the data and
it was quite basic things like standardizing
names, for instance, company names. Because a
lot of the data is captured actually manually,
it's the bills of lading or paper, and then some
poor clerk at a customs office at a port of exit
will be capturing the data from this bill of
lading. They can make mistakes or the bill of
lading actually, the data could be just written
wrong. The company name could have a little mistake
in it. So I had to do things like that to standardize
names. so that I could then do analysis on the
data and I'll show you the analysis in a minute.
I had to do things like, you know, tidying up
column formats, which sometimes threw my numbers
off, but it's okay. We all know how to do these
things if you know anything about data. And what
it enabled me to then do Was to make statistics
and this is where the cookie monster gets really
happy because this is when things get interesting
Because to me data talks data tells you a story
if you are able to go in and do some analysis
and Have a look at what? the data says So I used
what I think are pretty basic for me. You can
probably see here. It's a simple sum if I did
the odd sum ifs with multiple criteria. I used
count unique, count unique ifs, that kind of
thing. So really not rocket science. It might
be that some kind of AI could have done this
for me. I'm just kind of a little bit traditional
with my data and I had to do it myself because
I sometimes found as I applied my formula and
thought about how I was going to do things that
I would get other ideas and so on. One day, I
will get my head around using AI for data analysis.
So when I applied my formula to the data that
I downloaded and consolidated, the story started
to emerge. So for instance, you can see here
that, okay, looking at the percentages, over
80 % of the exports of this particular opioid
from India to West Africa, was going to Ghana
and Sierra Leone. Those were noted down as the
destination countries. So that's interesting.
The data is then telling you, okay, you might
really want to look at Ghana and Sierra Leone,
because those are kind of two of the inadvertent
commas, like problem countries. When I did sums
for the top exporters, you can see here that
Okay, there are some bigger exporters we decided
to highlight the top three Because of you know,
there were so many different exporters from India
that I wasn't gonna go through and look at all
of them So we said, okay, let's look at the top
three and it was at least again The data tells
you I feel where to go and look next I hope that
makes sense So that's yeah, that's really What
I did with the little cookie monster did with
the data was downloaded, cleaned, and analyzed.
And again, the data is not the story. The data
is telling you where to go in your story. It's
suggesting to you areas that you might want to
investigate next, but it's not the whole story.
I do just want to point out something else and
this is where we start getting into the mess
of trade data. Where I was using 52 WMB data
and then we got access. We found out that in
fact we could get free access from Import Genius.
So Import Genius gave us access to another data
set and you'll see that there are some pretty
different numbers here. And I got pretty nervous.
Like, you can see here, this column is my calculated
differences between Import Genius's data and
52WMB's data. And at that point, I got pretty
scared. And our editor also got pretty scared
because he was like, wait a second, you can't
keep going with this story. This data is terrible.
I really then had to look into, well, how can
this be? I mean, part of me instinctually knows
that when you're dealing with databases with
millions, if not billions of records, if you
think of all of the shipments globally over,
I don't know how many years, that's a lot of
data. Things are going to get chaotic because
data is never perfect. I think that's the nature
of data. But also, I had to really look into,
well, how can it be that I've got two different
sources with different numbers? And then luckily,
I made contact with William, who is the head
of research at ImportGenius. And he was explaining
to me that, look, the nature of this data is
that it's imperfect. And what you need to do
is you need to make sure that the data is basically
going in the same direction. So if, for instance,
52WMB had been showing me all these numbers,
yet import genius had nothing or very, very little,
then there's likely to be a problem and I need
to look into it further. So these pieces of data
were different enough, but they weren't such
that one needed to say, okay, this has to stop.
So to be honest, I'm still a little bit uncomfortable
with this, but Looking into it further, it became
clear that this is just the nature of the data.
And what I'm going to do now is actually take
a step back and say, well, where does this data
come from? So I've shown you a picture of a bill
of lading, and basically the government bodies,
like customs offices, they capture the data in
the bills of lading into some kind of IT system.
And in a lot of countries in the world, that
data is still, the bills of lading are still
paper. They're not digital. India has actually
gone quite a long way to digitizing. I think
Singapore has completely digitized. The US is
starting to digitize at various, and I think
it's at various ports. There are other countries,
the US has a, sorry, the EU has a system. I don't
know how well it's been implemented. The UN also
has a system that they make available to various
countries where it has been implemented. But
basically what I'm saying is that the data is
in kind of different formats, different databases
for different places. Then what happens is that
online trade data providers buy it as well as
brokers. So the online data trade data providers
are like Import genius or volsor or you know,
any of those other providers penjiva, etc but
then there are also these brokers and those brokers
and providers they combine the sources where
they get all the data they clean and present
it online and this is where I used AI was to
Try and present some of what I was perceiving
to be the chaos around trade data So you can
see at the top here that there are different
data sources, and those data sources will be
like a particular port office, or a particular
country, or a particular province, their trade
data. They will then sell this to data brokers.
These are often regional data brokers, like you
get Asian data brokers, American data brokers,
et cetera. And then you get the online data providers
like Penjiva, Import Genius, et cetera. who either
get the data from data brokers or sometimes they
buy it from the original sources as well. Now
at each of these stages, the data gets cleaned
and it gets put into databases. And to me that
partially explains why there are differences
and I could see it. And when I say differences,
I mean differences between sources. I could see
that sometimes like one provider or one source
would round numbers and another wouldn't run
numbers. Or they use different exchange rates,
for instance, when they convert data into US
dollars. So there's a whole bunch of things going
on with the data, which I think explains well
enough that there's going to be differences between
your sources. But I will say that I wouldn't
We've been quite hesitant to quote specific numbers.
And William of Import Genius did say to me that,
look, if you are going to write a story based
on one trade that you can see in the shipping
data, he really recommends that you go and you
try and corroborate that one trade somehow through
interviews, through other means. I'm not sure
what. To make sure that that trade did happen
You know when you aggregate that problem isn't
as it's not as much of a problem But when you're
looking at a single trade you're gonna really
need to be much more careful because of the kind
of what I'm showing here the sort of chaotic
nature of things So in the summary I went just
by the way on my story We're still busy writing
it and I think it'll come out probably in early
January But in my story, the trade data was really
key because it told us where to look. We could
see that the trade was happening, that huge quantities
of this opioid that is not legal in West African
countries are being exported from India. And
we could see that it was a whole range of countries
that were receiving it. It was actually great.
I had a conversation with the journalist who
was one of the lead researchers on that fantastic
story that I showed you from the BBC. And she
told me that when she was writing her story,
she was looking at the trade data and she was
researching and she thought, I'm going crazy.
I must be going crazy. This can't be happening.
How can it be that an opioid that is not legal
in West Africa is being exported from India to
West Africa and yet it's being shown in the trade
data? And it was really good to hear that from
another researcher from the BBC, because I've
also been feeling like I must be nuts. How can
this be happening? But it is being shown in the
data. So in summary, as I said, trade data, records
and imports and exports, it's messy, but you
can find things in there. There are some free
sources. Import Genius will give you, if you
are like an open source or you're a journalist,
they will give you access. They won't just give
you access to the whole database. They tend to
want to narrow it down a bit, which I understand,
but it's great and they're very helpful. It can
be the start of a story and it can inform your
story, but you're going to have to clean, you're
going to have to analyze, but that can be really,
really useful in investigations. If you want
to talk some more, please contact me. As I said
at the beginning of the talk, I'm learning. I'm
not an expert. I'm learning about this. I found
it really useful in my investigation, but I know
that there are people out there who probably
know a lot more than me. So yeah, there's my
email, Katherine at consultant .pellingcat .com.
And please get hold of me if you'd like. Thank
you, Catherine. And done. That was amazing. Thank
you. One, thank you so much for taking us through
the mess that is trade data. And two, thank you
for using a live example as well to really demonstrate
the point. We've got a few questions already
in. I wanted to first ask you, because you started
to clarify it a little bit. People were asking,
well, how do we know that this data is real,
like the platform? which is providing it, is
how to trust it. You started to explain that
and how you came to the same kind of realization.
But how do you first come across those platforms?
How do you know that what you're on, for example,
52 WMB, is a legitimate trade platform and not
something where there's just a bunch of numbers?
How did you initially come across those platforms
first? Yeah, it's a good question. You know,
we wanted to use somebody fancy like Pangeva
or Lloyd's S &P, but we just, we could not afford
it. So we had to go kind of in inverted commas
the cheap route. We just, we didn't have a choice.
And then when we got access to the import genius
data, that showed us that even though the numbers
weren't exactly the same, and sometimes they
differ quite a bit, that the data was essentially
saying the same thing. So I think that if at
all possible, you try and get two sources. And
I don't think it's in anybody's interests to
just make up a whole bunch of trade data and
sell it. Because somebody is going to figure
out that you're just selling rubbish Like there's
there's too many fields in there and there's
too much data. I I can't see how anybody would
just make it up Even though it's it's imperfect
But as I said two sources I think is a is a really
good idea and it could be that for the second
source You use those free the free data like
I was showing you which was where I started you
might have to do that Yeah, I think for many
people listening who tend to be freelancers or
people who are working in research as side jobs,
that's something that probably will be the step
to take. Tied to that, along the same lines,
somebody asked about the bill of lading and whether
it was scanned. You mentioned that in some countries
it's digital and some countries it's not. Within
the trade databases, do you have access to the
bill of lading or is it just something that uh,
you have to, uh, accept exists in the background
and you don't actually see it. You have to, you
kind of have to accept that it exists. Can I
share my screen again quickly? Okay. So let me
do that. So I'm just going to show you in 52WMB,
you don't get to see the original bill of lading.
But for instance here, when I'm clicking on one
particular shipment, it's showing me the different
fields that I'll see in the bill of lading. But
this is basically what you'll see when you download
the data. So you'll see this same data in sheet
form, which is what I got when I consolidated
everything into this sheet. You'll basically
see that when you download, but you'll see it
all consolidated in a CSV or an XLS. You'll see
bill of lading IDs, although I found some providers
give you that, some don't, but I don't know of
any place where you can go and double check bill
of lading IDs against some sort of central place
where you can check the bill of lading IDs. I
saw one mention, and I honestly can't remember
which provider it was. I saw one that said that
they provide the original bill of lading. Yeah,
one of them said that. I know that Import Genius
said to me that in the US, where you had to digitize,
that they were actually sending where the data
wasn't digitized yet, where still everything
was in paper. that they were actually sending
their staff to the port with a scanner to scan
bills of lading, which sounds like the worst
job in the world, but somebody's doing it. After
the fact, you can probably find an example of
a bill of lading online or I'll attach an image
example to the podcast description. Regarding
the bill of lading, that's a legal document,
right? Do you often find, you mentioned that
sometimes the bill ID isn't there. Is that just
based on the database's preferences or do you
often find that sometimes the bill of lading
isn't filled out? completely and there's data
gaps there. No, I mean, look, I'm not an expert
to say whether bills of lading ever don't have
IDs. I'm sure they must have to have an ID, but
I found that different providers would provide
the bill of lading ID or not. And like 52WMB,
when you download the data, in fact, the bill
of lading ID isn't there, but the declaration
number is there. I don't know why. I don't know
what the declaration number is. But yeah, there
are weird things going on that I don't really
understand. William from Import Genius did at
one point say to me, well, you know, to analyze
trade data, it really helps if you've got like
the level of knowledge of a customs officer,
which Quite obviously I don't have, but I've
literally gone and read the user manual of the
EDI system, which is the system that they use
in India because they've gone quite far into
digitizing. So that system is used at some customs
houses and I've read that user manual because
I was just trying to understand the data that
I'm using that the Indian port authorities then
sell on to brokers, on to data providers. Where
is it being captured? What does the system look
like where this data is being captured? Just
to try and make myself feel a little bit more
reassured about what it was that I was looking
at. Yeah, I had a similar issue when I looked
into tobacco companies and was investigating
that. A lot of their reports used kind of...
uh, lexicon and, um, words that weren't familiar
to me from outside of the industry. So I ended
up reading through their entire catalog of company
reports and company training, so that I could
understand the vague references in their data,
um, which, you know, sometimes you have to do,
um, and just commit to it. Um, G Clairf asks,
and this Fabian, sorry, go ahead. Oh no, I just
wanted to mention that for any of the other data
heads out there, I dreamt of finding a data provider
that had a data dictionary for their data, which
then showed you, you know, what are the definitions
for each field? What's the field format? And
I couldn't find that anywhere. And that really
frustrates me because I feel like as a data provider,
they should do basic things like, you know, at
least explain. what I should expect to find in
the data. But anyway, that's my dream. I didn't
find it. If anyone's listening, maybe that's
a task to do. Just for Catherine's sanity, at
least. Gclef asked, does the shipping data show
the ship carrying the goods? I'd be curious to
see if the ship actually went to where it claimed
or ended up somewhere else. Is that kind of data
available? I didn't see that. Sorry, I'm interrupting.
I didn't see that anywhere. It would have been
lovely. Didn't see that. That's a shame, because
our ship tracking friends in the server would
have loved that. Yeah, I just want to add a little
story there and it's kind of tangentially related.
I was talking with a Ghanaian pharmacist and
I was telling him about the data and he was saying
to me, well, what are the shipping dates? Give
me the most recent dates that you're seeing because
he knew how long shipments took from India to
Ghana. And then I told him some of the most recent
shipments of Tabentadol, and he was like, okay,
great, that's good. Okay, I'm going to tell the
authorities that they need to look out for that
shipment. And that didn't include the shipper
name, but he could at least tell them that goods
had left, a certain quantity of goods had left
India on a particular day and that they needed
to look out for these drugs. And I can't tell
you if anything ever came of that, but I found
it quite an interesting use case for the data.
Yeah. Yeah, that is interesting. Quite a few
people in the chat have been asking, quite surprised,
that the trade data implicitly identifies a drug
that's being shipped to a location where it is
illegal to do so. That is 100 % as the case,
so my mind. totally blew my mind, it made no
sense, but that's exactly what the BBC journalist
was saying. She said she thought she was going
nuts, that there must be something that she's
missing. With the drug that I've been looking
into to pentadol, as I said, it is legal in some
places, and it's legal in India, for instance,
but it's legal up to, in its instant release
dose, it's legal up to 100 milligrams. But in
the trade data, the dosages being sent over were
like 200, 250, 300 milligrams. So those were
even dosages that weren't allowed in India. And
it says that in the product description. So I
can't explain to you why this is all being disclosed
in the trade data, but it's there. And it's with
the BBC story. In fact, that combination of Tepentadol
and Caesopridol, it wasn't legal anywhere in
the world. Yet, if you looked at the trade data,
all of that was in the data. Someone's asked
for a link to the BBC story. I'll pop it in the
chat in a second. How did no one else sound the
alarm bells on this if it's so blatantly visible
that it's illegal? Some of the comments coming
through. Yeah, it's a surprising story. Hopefully
Catherine gets the opportunity to write it up.
Yeah, I don't know. I've really had to delve
deep into the Indian regulatory environment because
there could be some little regulatory glitch
that is allowing this to happen. It should not
happen. Drugs should not be sent to countries
where they're not registered. But there could
be some kind of glitch in the system where we're
not sure. I wanted to ask, because you mentioned
that obviously it's legal in some countries and
others and the trade databases, let's just go
back to away from the story itself a little bit.
Does that cover all global trade or are there
some restrictions and are countries comparable
as well? As you mentioned, lots of different
countries have different ways of filing things,
are the country trade information also comparable?
You mentioned different currencies, for example,
and things like that to be aware of. Yeah, look,
I will start by saying that I've focused very
heavily on India, so I know India the best. What
I have found though, looking at the different
data providers, is that their coverage of countries
is very different. And a lot of them provide
what's called mirror data. So mirror data is
where they can't buy the actual, from the source,
the trade data. So then what they do is they
say, okay, we can't get the trade, you know,
we can't get the trade data for Western Sahara.
But what we're going to do is we're going to
look at all the other countries for which we
have data and look at where they've exported
to Western Sahara. or where they've received
goods from Western Sahara. And then they kind
of build up the data that way. But you won't
find any provider that can give you every country's
data. It just doesn't happen. And in fact, not
all of them disclose it. I saw an article the
other day, actually by one of the trade data
providers, who said that 200 countries make their
trade data available. But I have not. found a
provider that provides all of that data. And
certainly, Import Genius says that they make
available all of the data that actually can be
put online that they could get their hands on.
So to be honest, I don't know which countries
make available and which data is good enough
quality to be made available. But all you can
do is look at the different providers and see
which countries they cover. We've got somebody
in the chat said, our team traced the dark fleet
of oil and built an AI for compliance for this
to support Ukraine. The architecture of how the
routes were set up in the crypto systems connecting
to port authority was fascinating. And then someone
else has put, I work in international trade and
from my experience, the custom officers do not
necessarily check whether something is legal
to import. But they rather focus on the accuracy
of the documents, like whether the actual weight
matches the documents and whether all items are
declared properly. However, this is limited to
Eastern Europe. This is something that we found
when looking at the sanctions on Russian ships
out of occupied Crimea when we were covering
the grain shipping. We also found this where
checks, they'd gone through checkpoints, but
what actually was in the ship wasn't necessarily
checked as thoroughly as it should have been.
Thanks for those personal stories. Please keep
them coming in and if you have any tips for Katherine
as well as she mentioned, because she's diving
into this newly as well, please do pop them in
the chat if you've worked with them previously
and thank you to the person who shared the BBC
article in the chat as well. Somebody's asked,
have you found that the choice of HS codes makes
a difference to the accuracy of data? When a
new product is created or when products enters
a new market, At the exporter, there's usually
a process of figuring out exactly which HS code
is appropriate. There is some leeway in choosing
which HS code applies so things can be mis -categorized.
Have you found that the choice of HS codes makes
a difference? No, you know, because I don't know
if you remember when I showed you the 52WMB UI
where I did my searches. I didn't have to use
HS code. I could search by the opioid's name.
So in fact, it didn't matter what HS code it
had been allocated to. When I looked through
the data, I found that it was very consistent.
It happened to be that the HS code that it was
allocated to was always the same one, but the
HS code did not affect my investigation. When
I showed you that Mali, the cigarettes one that
was using com trade data, and there you have
to do it at HS code level because that data is
aggregated to HS code. But there I think cigarettes
must fit under one HS code quite neatly. With
my story, if I'd been doing it, if I could only
get access to HS code data, I actually wouldn't
have been able to do it because there's so many
different drugs. and medications, you know, that
are put under one HS code that it wouldn't have
been viable. Thanks for that. We've got a few
more minutes for questions. So if you want to
pop some in the chat, please do. I wanted to
ask specifically about project management because
as soon as you showed that spreadsheet and went
through all of the data, I know you said that
you love it and that it's so much fun, but actually
I put in the chat that It gives me a headache
and makes me want to cry. For anyone who gets
a little bit overwhelmed by large data sets,
have you got any tips or tricks just to get in
the right mindset to kind of deal with that amount
of data when you're trying to sift for a needle
in a haystack, for example? Oh, if you're asking
me that question, I... I mean, literally that
blue Cookie Monster that I showed you, I've had
a t -shirt made with that Cookie Monster and
it says, me wants the data. That's how much I
love data. So I feel like I don't necessarily
have tips because I'm a bit of a crazy person.
So I'm probably not like other people. I think
just don't be scared of data. It's basically
bits of text and numbers and rows and columns.
Particularly if you're using Google Sheets, if
you're using the filters, the filters that you
can apply in that top row, even just getting
to know the data through the filters and you
get to understand the data and what's there,
that can be a good place to start. Thank you.
I appreciate that. Someone has put in the chat,
the answer is give it to someone else. And maybe
the answer is give it to Catherine at this point.
It's interesting that you love data that much.
I can see the value in the numbers, I swear.
Someone asked earlier in the chat when you were
talking through your Excel spreadsheets about
whether you've used coding, whether you've used
Python to sort through data before. Because obviously
you mentioned AI, there might be an AI tool out
there, but you like to do things manually. Have
you ever experimented with using code to also
kind of shift through data, large pieces of data?
No. I tried to learn to program R and I started
a course twice and I was just so rubbish. It
was embarrassing. And I think I feel really comfortable
in Sheets because I've been using them for years.
At some point I'm going to spend the time to
learn OpenRefine. to do data cleaning. I didn't
need to use OpenRefine or I felt that I didn't
need to use OpenRefine to clean this data because
it was not actually, it wasn't that bad. And
I could use basically search and replaces to
clean my data. But at some point, especially
with a bigger data set, with a much messier and
bigger data set, I would need to learn something
like OpenRefine to clean my data. But we also
like, we have coders in Bellingcat who dream
in Python, whereas I don't. I probably have nightmares
in Python. So I know I could always just rely
on them to do that for me. We've got a few people
in the comments saying just that I've written
Python scripts that can do the cleansing super
fast as well as deduplicating. and somebody else
put LibreOffice or LibreOffice and Microsoft
Excel now support automating commands through
Python that functionality can come in very handy
for this task. So there you go. Maybe you should
dive away from Google Sheets for a second and
explore Microsoft Excel. I think Owen, I need
to grow up. As we're shifting towards the final
parts of the stage talk, I wanted to ask you,
because we've mentioned that repeatedly, that
data is in the story, right? The data is a starting
point. It's a place to realise the story maybe.
What would be your next steps if you find, as
you said, you find this data that is a little
confusing in terms of consistency, but tells
a coherent narrative. Where do you go from there?
Are you now looking at doing those interviews
that you mentioned to corroborate it? Do you
have to step away from the OSINT angle or are
there OSINT methods that you can take it further
with? You know, for me, I did more. I wouldn't
even say it was... I don't know if it's necessarily
OSINT. I basically just researched. Like I've
got quite a background in healthcare, so it made
sense to me to, when I'm seeing an opioid that's
being exported from one country to another, it
made sense to me to go and look at the regulatory
environment in the importing countries and try
and understand, okay, is it, are they, is this
actually legal to export there? And so it was
really more, there wasn't, other OSINT research
really to do. There was just kind of basic desktop
research to try and make sense of what looked
like something that's impossible, to try and
make sense of it. Because we could see, like
if you look, for instance, in Ghana, to pentadol,
and then it's had different opioid forms. There
have been other opioids that have been used before.
They're called, in the street, the drug is called
Red. And then I could see on social media that
there were posts about it. There they call it
War Name Red. And there was a song, War Name
Red. And I could see videos like on TikTok and
on YouTube or on Facebook of users and the packs
of the pills. And then I could see other local
journalists' investigations of War Name Red and
them talking about the problem of the addictions
in their country. I would say that's... I guess
that's OSINT research, it's using social media,
but it was really to try and understand what
I would call the demand side of the problem.
So the countries in which the drugs are being
used and social media was very useful for that.
Yeah, you're looking at the impact of that trade
and why it matters that you're even reporting
on these numbers as well. Cybers put in the comments,
that's a good point, linking social media content
along with the trade info. You could also, right,
dig into the suppliers that you found and check
whether they also supply legitimate things as
well. I'm guessing they do. I'm guessing that
large suppliers in India at the minute. Yeah,
in fact, we did do that. We're just not sure
whether we can publish it or not. There we did
use actually some more traditional OSINT kind
of digital footprint tracing to try and figure
out some of the suppliers and turned up all kinds
of interesting stuff that I can't really talk
about now because I don't know, you know, whether
we can publish on it or not. But it was very
interesting looking at the suppliers, a whole
range of them. And yes, some of them do supply
actually perfectly normal drugs to countries,
you know, because India is, they call themselves
the pharmacy of the world. And it's not like
the whole of the Indian pharma industry is bad.
It's just certain companies are wantonly exporting
drugs that are ruining lives in Africa. And you
said you focused on Africa because you wanted
to cover the impact. um, local to you, but have
you also found that these shipments are going
to Europe or to the US? Like is, is there ways
to track on these databases if the same shipments
are going to other locations as well, or are
you stuck searching one or two regions at a time?
No, well here I was looking at India export data.
So everywhere that India was exported in this
particular opioid to I could see, and there is
more to be done. Yeah, there are other countries
that I would love to look into. It can be quite
difficult with some of the other countries, though,
depending on how much information they make available
online. Although, in fact, it was possible with
West Africa. Most of the countries are quite
good about disclosing, for instance, which drugs
are legal in their country. Other countries do
not. So I haven't yet looked through the data
to look at whether I can do further investigations,
but I can see that strong dosages of this particular
opioid are being shipped all over the place.
Some people call to pentadol like tramadol, which
was one of the opioids before. It's like it's
the fentanyl of West Africa. It does need that
level of attention. So I'm sure there are other
countries. There were a bunch that I could see
that I would love to dive into at some point.
Wow. That's a big statement as well. The fentanyl
of West Africa. Amazing that you're spending
some time diving into this. We're coming to the
end now. So I wanted to ask lastly, unless anybody
else has any comments that they quick questions
that they want to quickly squeeze in. is if you
have any advice, you've gone through lots of
advice today in the talk, but if you have any
lasting tips for anyone who perhaps is completely
new to financial investigations and now, because
of this talk, maybe wants to delve into drug
data or tobacco data or any suspicious goods.
What would be your main tips for people who are
first starting out? Is there any resources, any
guides that you would shout out for people to
read, any people that they should speak to who
are a little bit wiser on this subject, perhaps
that you found useful speaking to? Yeah, basic
tips for people who are beginning their journey
in this space. You know, sadly, I kept on thinking
I would do a lovely Google search and I would
find the guide to trade data and how to use it
and I didn't find it. It's actually something
that I'd like to write myself. And I was meeting
with a researcher from another NGO in Europe
and she was She had talked to a bunch of investigators
from other organizations and there was a similar
cry of people saying, we know there's trade data
out there. We can see how useful it is. It's
tricky and we need to learn how to use it. How
can we do that? So one of my ambitions for 2026
is to at least start the guide. to write it and
to have people add to it, disagree, agree, whatever
they want to do, but at least get something down
so that we as open source researchers can start
to kind of share our knowledge so that we can
all make better use of this data. Because I was
literally flailing around blind. I'd never used
this data before. And I've just kind of had to
learn as I went along. And I ended up interviewing
I got in touch with one of the, the guy who was
the head of the training Institute in India for
customs officials. I don't know how, why he decided
to speak to me, but he did. And, but I had to
go to that level to try and understand the trade
data because I couldn't find guides online. So
I wish I could say to you, it was there, but
I would love to write it. Watch this space is
what Catherine's saying basically. There will
be a guide soon, I'm sure. Thank you so much,
Catherine, for your time today. It has been so
fascinating to find out what you found out over
the last few months and really dig into the data.
Thank you for taking us through it. Do you want
to remind people of how they can reach out to
you again? Just quickly before we end. Oh yeah,
you're welcome to email me. It's Katherine with
a K, K -A -T -H -E -R -I -N -E at consultant
.bellingcat .com. All right. Pop Katherine a
message if you have any tips for her, but also
if you feel like you want to chat a little bit
further. Within this space, we do have spaces
to chat about financial investigations. Hashtag
money is the place to go to for that. You can
talk about trade data to your heart's content
in that particular channel. So please feel free
to go in there. Obviously respecting any rules
of the server as you're chit chatting. I can
see a subtle knife is currently typing, which
is probably reminding you all of that. But anyway,
wrapping up, thank you so much, Catherine, again
for today. And yeah, we will be back not in two
weeks time, but in the new year with a very special
stage talk from Elliot Higgins. But for now,
thank you for listening, and we'll be back in
January. Thank you all. Thank you for listening
to the stage talk. If you'd like to catch a stage
talk live where you can ask the guest questions,
join the Bellingcat Discord server by visiting
www .discord .gg slash Bellingcat. The music
you've heard is titled Dawn by Newer Self and
is courtesy of Artlist.