let's dive into today's topic.
hi everyone.
I am Jason Gans.
I am the CTO at Data for Progress.
alright, so quick agenda.
We are going to start with a first
intro, an intro to polling and kind
of the data and with just tickle
challenges in running a good poll.
And as part of that, we are going
to have a live demonstration.
we're going to do a quick
poll of all of you today.
To see what our favorite
ice cream flavor is.
And then we're going to work through
that as if that was a real poll to
kind of get a sense of what this
looks like on real operational data.
so I don't know if you want to
share that link in the chat.
awesome.
And then, people want to take
that now, or we will, we'll
pause for a sec to, To do that.
then when we are going to look at the data
operations and a practical usages of how
we use dbt at Data for Progress and how
we help use that to benchmark our polling,
to make sure that the data that we're
using is actually accurate and represent
the real world conditions and making sure
that we're also doing everything we can
to pick up on quickly shifting trends.
and then finally, we're going to talk
about the importance of democratizing
your data within your organization.
so this is really important, obviously in,
political polling, but it's also important
in any sort of data organization.
All right.
My side stuck there.
Yes.
So first off.
What is Data for Progress?
So Data for Progress progress is a think
tank that uses data to push for progress.
The change in America, we
support things like the green,
new deal and Medicare for all.
that being said, this is a technical
presentation, which will hopefully
be useful for anyone no matter
what your organization does.
And I have promised to stay away
from any sort of thorny divisive.
Topics that are going to cause
people to clamor in the chat.
So that means we will not be discussing
today, whether you should be using
lagging or leaving comments in your SQL.
Alright, Let's get into it.
I'm sure most everyone that is on here
has been falling with different amounts
of intensity, the polling leading up
to the election that we have coming up.
But what is a pole?
We always think of a poll.
When, when we're reading it as
the result, we are showing 45% for
one outcome, 43% for another, but.
To get to that outcome.
There's a lot of really complicated and
any issues that you have to determine,
which come down to two things, there
is your sample, which is the actual
raw data that goes into your poll.
And then there is the weighting of
the poll, how you ensure that, that
your raw sample is transformed to
be representative to the population
that you're trying to target.
so at this point point, let's do a
quick pause from the presentation
and look at the data from our survey.
so for this survey, we
were looking and seeing.
What the favorite ice cream flavor
of people within the community was?
so we have a really nifty tool set
up is allowing us to actually she run
this on live data or the big queary.
And so you can see here, we have
our raw results coming in and.
Each row here is, one
respondent for each you.
So we are asking some simple demographic
questions here, age, and then, what
type of ice cream flavor people want.
for example, this row is under 30 and.
This person like cookies and cream.
And so the first thing to know when
looking at at polling data is it's
always really important to start with the
micro and then move up the background.
It helps get a sense, crew,
the actual industry, all
respondents are to remember that.
We're not just working with aggregated
data here, we're working with real
opinions for real people and staying
grounded in that is always super,
super helpful when trying to make sure
that you are using that data well.
and then once you have a sense of
what the raw data looks, you can
go and look at a summary view.
Looking at our responses here, it
looks like cookie dough and mint
chocolate chip are kind of running away
with it with cookie dough at 31% and
chocolate chips at 22% of the phone.
so this would be our
Ross sample for our data.
Our poll went out into the
field and we got this back.
But when we're looking at this,
we know that the Pew say we
were trying to determine the.
Preferences, not just of the people in
this chat, but of dbt users overall.
And we think that dbt users are 50%
under the age of 30 and 50% over the
age of 30 that's where waiting comes in.
When you take a survey and you weigh
that, you say, okay, how many of these
people actually in my sample, So how
do they match my target population?
Because I'm going to guess that we
have a relatively younger crowd here
and we don't actually have 50% under
30 and 50% over there, eight, so we
can go and we can do survey waiting in
this to say, all right, we're going to
modify the weight for this such, that.
such that we match it.
Exactly.
So we can, I can see here, this
is our dbt project and actually
let's, let's do a run real quick.
so what we're doing here is
we're taking the raw data we're
taking in we're processing it.
We're applying weights to
make sure it's representative.
And then we're going to look at
those weights and see what the
actual true ice cream preference is.
If we had a representative survey, So the
good news here is we, this one home, our
weight modifier represents how different.
We think the person is from the actual,
if we were to have a one to one, imagine
if we had exactly camp, our sample
population targets and weight modifier
for everyone would be wow, but it looks
like we have had slightly way more
people that are under 30 than over 30.
So we're waiting.
The people that are under 30 down a little
bit to say, okay, in a representative
world, there would be slightly less
people that looked like you and slightly
more people we're in the, in the over 30
people you said, so drum roll, please.
We are going to find out the
representative favorites, ice
cream flavors of the dbt community.
And all right, great.
So it looks like cookie dough and mint
chocolate w are still the winners.
And there's actually very
little difference here between
the weighted and the unwell.
And so it looks like in our estimation
of what this population would look
like, we actually did really good.
So if we go into the field and
we see that our population.
our predicted population and our
actual population are super similar.
That's always a great result because
that means that the people you are
collecting closely match the predicted
demographics of the election or whatever
it is you are trying to match up.
Now, we just use a very simple
waiting schema where we've picked
one demographic and we just
said it's 50% over 50% on that.
And the real world, it gets a lot
more complex in figuring out what
you weight on and how you weight
on it is super, super important.
So a good example of this is that in
2016, there was one dimension that
was often not being weighted on that
caused people in the point community
to miss an extremely important
trends and led them to underestimate.
Donald Trump's chances of winning
the election and that was educated.
So what we saw in 2016
is more college educated.
People were getting really
amped off to answer calls.
When wa when people were dilate, when
people were asking, do you want to take
a survey college educated people were
extremely amped up to take that fall and
say, I'm not voting for Donald Trump.
I'm voting for Hillary Clinton and.
So we got more college
educated people in our polls.
And what that meant was that
college educated people were
overrepresented and people without
a college education who were more
likely to support Trump underrated.
So what you saw is that if you
were waiting on education, then.
You would see Clinton with a
four point weed, which is still
more than she had by then.
She actually hit bias.
There were other methods, but if you
weren't accounting for education,
then you saw Hillary Clinton having
an, having an eight point lead.
So figuring out what demographics
to wait on, what populations to
wait on is incredibly important for
making sure that you are actually
getting a representative survey.
That's kind of the basics of weeding.
Let's take a look at overall D the
challenges involved when running a poll.
So the first is you have a survey idea and
you write, you're like, Oh, we're going
to go into the field and test something.
And the first thing that you do with
any poll is you write a questionnaire.
When you're, when you're writing a
questionnaire, this is one of those
things that people might not think of
as explicitly a data or a non-data task.
But the way that you write that question
is going to ultimately have huge, huge
implications on how your poll comes out.
A couple of examples of this.
Are, are you ready?
I think your responses, because if
you're not randomizing your responses,
then people are just going to be more
likely to pick the one that's up top.
Are you asking people if they agree
or disagree with something because
people in general like being agreeable.
So if you give them a, do you agree
versus do you disagree as opposed
to two binary options that they
can select between you'll often
find hugely different results?
And super importantly is.
You have to be able to take complex
topics and boil them down, such that they
can be expressed in a few sentences to
a population that is largely consisting
of people without a college degree.
And you have to make sure it
actually makes sense to them
because otherwise they're not
actually capturing anything useful.
So you have to really be able
to practice a large amount of.
Placing yourself into other people's
shoes and figuring out is how, how would
other people react to this question?
And you can't, you need to try
really hard to remove your own
preconceptions when writing that
question other, otherwise they're
just going to get data which already
confirmed your preestablished beliefs.
So we have our questionnaire,
we have it written.
Now it's time to go into the field.
This is where it gets really interesting.
How do you actually get
people into a survey?
So there's a number of different ways
that you can get people to take a survey.
Yeah, you can dial them on their
phone and ask them with live humans.
This was for a while considered to
be the benchmark gold standard method
of getting people to take polls.
The problem with this is that
you are mostly done on landlines.
And I don't know about you all in
the chat, but I personally don't know
just about anyone that has a landline.
So thinking about the mode and how you
actually get people into your survey
has really important data considerations
and the things here to think through
our, do you want to do it by phone?
Are you texting them
a link to your survey?
There's also preexisting panels where
you can go and kind of get people who
have opted in to take surveys, but then.
You're surveying people who have opted in
to take surveys, which you'll be shocked
to know sometimes behave differently
from people that haven't done that.
So thinking about how to kind of take
all of these disparate ways of getting
people into your POL, some of which have
strengths and weaknesses that compliment
each other there and put them together.
They're into one single data product
is a really interesting story.
Any data challenge?
And then finally, once you have your
raw data, then it's time to wave it.
So this is where you get into
considerations of things like
DUI on education, which as we
already went through answer is yes.
There's, there, there's a number of other.
Questions in terms of how you
weight your data, what goes into
it and, how you set your targets.
They can, that's hugely
important implications for the
data that actually comes out.
So really when, whenever
you're looking at a poll.
That comes up in the news that
people are talking through.
It's important to think, how did
they write their questionnaire?
How do they feel that and
how did they weight it?
And if you're able to get the answer
for that, then you're going to
be a lot more able to know what.
The strengths and weaknesses of this
particular poll are because there are
different types of ways of doing this that
have different strengths and weaknesses,
and being able to kind of look at that
and analyze it can give you a much clearer
sense of how, how the poll was created.
So for us, as we're running our polls,
there are so many different trends that
we want to track to ensure that our sample
is getting collected accurately and that
our weights are getting applied correctly.
and so this has been a problem.
Well, that had been going on ever
since we first started running polls,
which is how would you actually
track this track the trends and make
sure you're staying on top of it.
And that is where we found.
We were able to get huge, huge
value of implementing dbt into
our post-survey analysis stack.
So our actual survey waiting
is not done in SQL in dbt.
Like this example was that the
survey weighting is done in Python.
But where dbt fits in is dbt allowed us
to create the intelligent data model.
We actually compare our trends across
arrays and over time such that it
wasn't just kind of me going in and
fiddling with Excel spreadsheets
and sending pictures to people.
But we have Y interactive dashboards,
which contain the historicals of all.
All of our surveys and let people filter
across, different demographic breakdowns.
It lets them look at the raw data, but
the way the data really see what trends
are going on, both in the raw data and
the way to data so that we can know.
what movement is happening, what, what we
think is real movement, what might just be
movement in terms of who is responding and
B being able to kind of really get a good
sense of intuition about what's going on.
Alright, so getting into the
nitty gritty a little bit.
So the way this works is, so a survey
is created is fielded in the survey
platform for us, that is Qualtrax.
And then from there we use a custom
built ETL to load the data out of
Qualtrics and into our Postgres database.
and then in the post Gress database
is where the weights are applied and
initial data quality checks are done.
From there.
Once the Weezer applied,
we run dbt on this.
Data.
And then, that gets fed into a Periscope
dashboard where every time a survey comes
to the field, we do a couple of quick
checks, sending the checks, make sure
the data looks good and then kind of say
set it aside for a way there, when we
do our, our big data quality reviews.
and so why dbt, why are we
using dbt for this project?
And.
Honestly, it's been such a
lifesaver in terms of being able
to take data, which was not.
Transformed for analysis data that we
were using for our internal operations,
and then turn it into data, which is
super easy to qualify model, super easy.
The, the summarize and we are able to
create summary, views and tables such that
you can actually get in, use the data and
answer questions quickly that we know.
Very soon after a survey comes out of
the field, are we hitting our benchmarks?
Is this data looking like, like, like
it has all the hallmarks of a high
quality Paul, that's going to give
us a truly representative response.
Alright.
And so what does this look like?
When our survey comes out of the field,
it first comes into our database.
As a wide table, and wide tables are
pretty easy to work with by themselves.
There's one response per a record,
and you have your column for all your
demographics, a column for weights and
a column for each of your questions.
And so when you're analyzing
one survey, this is actually a
relatively easy thing to work with.
The problem.
Way do many tables.
so we do somewhere and the order
of two to four surveys per week.
And so very quickly, our database just
started getting littered with these one
off tables where we just couldn't compare
across them, trying to get them all into
one place and do cross survey analysis
and pick up on the trends, which are
so important when thinking through how.
Are things shifting what's moving.
How are different geographies behaving?
So it was pretty clear early on that we
couldn't just do analysis on the wide
table because it's so important to be able
to, when you're doing this picture and
that goes fully across all of your data.
So you can really know what's going on.
So from there, we moved our data into
the long table and the great thing
about long Creek, if they were easily
storing all of our survey information
and just the three to five tables.
And so these tables were
super, super, super simple.
And basically every response,
every Saturday, it was
turned into a key value pack.
So field, age value under 30 field
favorite ice cream value chocolate.
The problem with this is that
while enabled us to get all of
our historicals into a few tables.
It was very difficult to
work with you and open it up.
You would try and query it.
The queries were slow.
You had to join the data.
It's with self a bunch of times to be
able to get anything into this thing.
And so we technically had all the data
together so that we could operate off
of it, but it was really, we couldn't do
anything with it in terms of analysis.
That's where we went.
And we started working on transformed
long tables and the transformer long
cables combined the best features of
both the wide and the long tables.
So we were able to have all of
our demographic and waiting.
information on every row, but
then everything that was just a
normal question continues to be
a key value pair in the survey.
So once you're doing that, then you
can pretty easily work with this data
and query it as you would expect.
So once that was done, the whole
organization suddenly had access to all of
this information and we were able to then.
Use this as the base to, create a
series of macros that allowed us to
have summary payables, which we use
to capture all of the most important
tracking trends that we have, things
like fair, favorable course race and the
benchmarks that are really important for
us to be looking at, as time goes on.
and so we, When, when we decided to
invest in this, we, we, we wanted
to think through what that I meant
for us as an organization, as a
data focused organization, and as
a progressive organization, we, we
thought was really impressive, have
a philosophy of how we use data.
And so there are a couple
different ways that you can.
Think through this.
and obviously, I'm coming in
here with a bit of an agenda,
but let's walk through that.
the first is what I like is to
call the Wiz kid or gut feel
way of making data back to them.
And so that's where in every ornament
and you have a couple people, maybe
they're an executive, maybe, maybe,
maybe they're high level analysts,
so kind of control all of the numbers
and they will occasionally other
people in the organization see other
numbers, but mostly they exist too.
No, all of the trends B be the ones
that are in charge of knowing the data.
And if they're good, then
this can be helpful sometime.
But the bad thing is this puts you
heavily reliant on a few stakeholders
and end users of individual biases.
Individual plans, spots
are hugely magnified.
If you're kind of expecting a couple
of people within the organization to be
the holders of the data and the decision
makers on the data, And so this is largely
the way that political data was done,
prior to, prior to the last four years.
And then after in, in, in the democratic
party, I cannot speak to the other side.
but I would imagine it.
and then, so after.
After 2016, there was a huge move towards
using it incredibly fancy data science
techniques to kind of be like, Oh,
well clearly we missed something there.
The way to fix this is to have
the most complicated algorithms
to the deepest neural net.
and the thing about this is like, it's.
You can definitely pick up on trends that
less sophisticated methods, Mick myth.
it sounds really fancy and impressive
when you're going out and you're trying
to sell people on your data techniques.
But what you find is that you
actually have all the exact same
problems of the, with kids where
you end up heavily early rural.
I just want a couple of stakeholders
and individual biases are magnified,
but then also individual biases
there scared your complexity.
Spirals out of control.
These systems are very fragile
and very difficult to debug.
So for a lot of reasons,
this ends up being.
Something that sounds great at the
start can have trouble when you're
trying to actually implement them
into practical decision making.
So there's a third way.
What I like to call the town
hall method of using data.
And with this, you allow
as many people as possible.
Preferably everyone in the organization
to have access to the data and be
able to walk through, understand
things and make their own decisions.
and.
Critically, you don't just
hand everyone over pile of data
and be like, all right, great.
We're a data based organization.
Look at this dashboard like this is it.
What you do from there is then you have
time where people get together or they
walk through data and they discuss it.
I'm sure a lot of people here have
gone through dashboard group use
sessions where everyone's in the
room and everyone's a clam rank.
Why is this number off?
Why is this number down?
And like, the truth is.
It does get messy, just like it
is in our democracy, get messy.
But when you commit to doing this and
you commit to giving everyone a voice
and letting people come forward and
actually pull out trends and do their
own analysis and you give them the tools.
To work through the data themselves,
and then a voice you find that you
actually are able to pick up on things
that get minced, whether you're trying
to keep data heavily clustered in a
couple of stakeholders, or if you're
trying to, to, purely go through a
complex machine learning approach.
and so what this looks like for
us is we just had a, we have
multidisciplinary expert reviews.
So thing is where different people on
the team are good at different things.
We have people that are experts
in the actual methodology.
Here's how you get pulled
into the hands of individuals.
We have quantitative experts, people
who are very knowledgeable, but
statistics, political experts, subject
matter experts coming in, who has
no, the actual policy that we're
polling that we're working through.
And what we find is that when you
get these people in the room and you
give them access to data, which can
be worked through filter presented.
Quickly and you can drill
down and see what's going on.
Then you're able to much more
quickly surface things that need
to be discussed and come to a,
come to a conclusion on them.
So ultimately it requires, it
requires the commitment and requires
everyone taking time out of their
day to get together and review it.
But when you do this, you get
the best of individual expertise
from all the people on your team.
You get the best of actually understanding
what's going on your data and you listen
to people because I think, I think
that's super important for all data
professionals per member, is that while
we might be good at getting data together
in one place, Everyone is an expert in
their own job and at their own life.
And so this is something we focus
on really heavily in polling, when
we try and make sure we have empathy
for the survey takers and that they
are the expert in their opinions.
And it's something that, and we try
and also focus on internally where
everyone, whether they're analysts,
whether whether junior or senior.
Is able to take the elements of the
organization and the work we're doing
and use the data to be able to make
us a more effective organization and
put out more, more accurate data.
And so the thing I want to end
on is the way this works best is,
that we have found is when you
think of data as a utility, okay.
Utility is something that
at its best just works.
It's not complicated.
It's not that there are different people
have varying levels of access to it.
Although that is obviously what we see
in real life, in too many situations.
But when it works, then people
can start to rely on it.
They can start to know it's there.
And yeah, when you think of data as
a utility, it used you think, Oh, I'm
going to be able to look at this number
and have accurate, well modeled data.
I'm going to be able to use that to
go about my day to day big decisions.
and, and to be able to use this
in, in the work then, and yeah.
I don't know of any others technology
that is more of a force multiplier in
doing that then dbt, because dbt actually
really allows you to create data, which
is intelligently modeled and organized so
that people who probably, I don't want to
sit around and write long people, queries
can actually have access to it and see it.
and it's been super, super exciting
to be able to work on this and, We
really appreciate the time to take a
walk through with you all today and,
be happy to, take any questions now.
Excellent.
Oh my goodness.
why did Johnny from ice cream to,
the politics of trailing and leading
commas and everywhere in between?
some really good takeaway, I
think for all data teams there.
so for those of you on the call,
I encourage you to just put
some questions into the chat.
and then I'll sort of call on you
to ask those questions out loud.
fortunately my coworker
Janessa has started this off.
So Janessa, are you able
to, ask the question.
Do you want me to start with the question
about uppercase dbt or I blame the people.
I blame the people who named it.
Not, not personally responsible.
Yeah.
Okay.
I will accept that.
Jason, I was just curious why
you went with this workflow.
I think that with survey data,
it's usually like the data
volumes are small enough.
That Excel is just fine.
and, and my guess is that there are
a lot of analysts who might feel a
little bit daunted by using dbt to
do this, but it seems like the trade
offs were, were worth it for you.
I'm curious if that was ever something
that your team debated about, or if you
just knew this was the way to go or,
you know, what your thinking was there.
That is such a good question.
and so Excel is.
Totally totally fine for survey
data when you're analyzing a single
survey and honestly, most surveys
we got, I still open it up in Excel.
as a first check, I kind of look into
a few rows, make sure the data looks
good, where we start to lose efficiency
when doing Excel is, so two things.
One is there's a lot of.
There there's a lot of benchmarks that we
know we want to look at for every survey.
And so I would find myself going
into every survey we did, we did,
I would make like the same six
pivot tables, and then I would go
and I would send them to the team.
It was like, all right, here's how
we're doing on, on these variables.
And that worked pretty well,
to get those six, but then.
There are two things that
happen after you do that.
The first is someone asks, okay,
well, how does this compare to last
week has compared to two weeks ago?
And so when you do that, then I'm
like, Oh God, cause I would have
to go find the Excel file for that.
and then do the same
thing over and over again.
So it was, repeating benchmarking
statistics that were super,
super useful to be able to get
dashboarded is a thing one.
And then thing two is kind of.
The power and flexibility of being able
to drill down in, like you've relatively
standard BI things like a filter,
different filters and different slices
and views was really, really helpful to
be able to apply across all of those.
Because if I took our six
benchmarking metrics and then it's
like, okay, well, what does that
look like for Democrats under 45?
Then all of a sudden it's
redoing that six more times.
If we want to look up that five different
ways on every Thursday, it starts
to get a little bit less manageable.
So once we w we were, we were
doing that for awhile, but then
we, we, we reached the point where.
In order to be doing all of the
interrogation of the data that we're
committed to doing, to being able
to make sure that we're actually
following through and really making
sure we were diving deep into it.
Then we had to have a system that allowed
us to do it a little less painful.
Excellent.
I actually ended up XL the other
night and I had felt really nostalgic.
I don't know, Jason, if you feel the
same way after like being in sacred
land, working at Excel together, a good
pivot table, it feels great yesterday.
That's really cool.
Mila, are you able to introduce yourself?
I forgot to nudge Nessa to do that.
and to go ahead and ask you a question.
Yes.
Hello everyone.
Mila from Fishtown here.
I have a really, possibly the
controversial question to ask him.
so I've crossed paths with a number
of, political operatives and lobbyists
career lobbyists over the years.
And, One thing that I find really
interesting is kind of the landscape has
been described to me as you have nowadays
in the last couple of years, really data
driven people on one end of the spectrum,
but then you have political people and
pundits on the other end of the spectrum.
And the truth is, is no matter how
data-driven they think they are.
They really aren't.
it's just kind of a thing that, they're.
Education on how to interact with data.
So a lot of people treat polls late
key problem with about 40 years
of political science research,
which is that the statistics that's
been used there is just bunk.
Al obviously there are
things that we can improve.
And I'm curious with you guys
really trying to use more
sophisticated data methods.
If you've run into kind of a
culture problem, or if you run
into trying to communicate these
issues with people that is a.
Fantastic question.
All right.
Well, let me, so the question
being, how, how do we.
Get people to use our data when there
are people who are mainly purely
politics, people, not data people.
And also how does this interact with
potentially polls being a noisy indicator?
So the first response PR is that, so our
executive director has a little quote that
he drills into us all the time, which is.
People don't care how much
they know until, you know,
they know how much you care.
And it sounds like, you know,
like it's a cute little aphorism,
but it's really, really true.
And one of, one of the things that we
have balanced kind of as like a newer
entrance on the data landscape is
that there is a cleavage in terms of
organizations who you are data first.
And can.
Who will follow the data wherever
they think the data goes.
And this is often combined with an attempt
to have very complicated data methods.
And then, then there are organizations
who are mission first with the data,
informing the mission and informing
not the strategy, but the tactics.
And As an organization who is
focused on the strategy of the
values that we hold with, then
the tactics being, using the data.
We find that we're much more able to
make inroads with people because we
go in and we, we, we don't say we know
how to do your job better than you do.
We know exactly what you do.
We say you're doing so much, like
you're doing all the right things.
How can we help?
And when we do that, we find that
we are able to get, yeah, people do.
They're very excited and eager to use
the data and learn about the data.
So it really comes to having a
commitment to showing people that we are.
With them, we care about them.
We care about what, what they have to say.
so that's that, that's the first answer.
The second answer being in terms
of the, people who are skeptical
pulling in general, We kind
of, we proved that empirically.
So one of the projects that we
did over the democratic primary
was we released a series of polls
predicting different state outcomes.
And we were depending on how you
measure it, either the first or second,
most accurate polling operation in
the country, I had a long running
kind of decades, old institutions.
and so we, we combine it with one.
just empirical evidence that the data
that we have actually reflects reality.
And this is really important because
obviously people are going to think,
Oh, well, you guys are just going
to say whatever fits your narrative.
And we actually won't because
that's not helpful for us.
So it's being willing to say
what we actually read the data
to be, even when that's done
doesn't necessarily fit with our.
Goals, but it being the assessment
that we have, but only doing that under
the larger lens of having people know
that we care about them or with them.
And that this is a kind of
longterm strategic partnership.
Thank you so much.
I could talk about this for hours, but
we'll let other people to have them.
we have a question from Jeremy.
I do just want to reiterate
to the rest of the community.
you're welcome to ask questions as well.
My coworker is a very, very intelligent
question, but we'd love to hear from the
very, very intelligent community as well.
But in the meantime, Jeremy,
do you wanna jump in?
Hey Jason, this was a great presentation
and thank you so much for giving it.
I have a.
Joke first question there that you're
welcome to answer or ignore at your will.
but I'm, I'm curious because we're
thinking a lot about data quality
and communicating the commitment
to data quality right now.
how do you feel like that changes
when you are working with data sets
that are very public facing and
the stakes can feel really high
versus maybe at a SAS company where.
If you've got one wrong, number one
time, you know, there's like social
capital to make up for it later on.
Yep.
It just means it's much more important to
focus on that quality because as someone
that previously was at a fast company,
presenting metrics, and now is helping,
with the public facing polls, the stakes
feel a lot higher here, which is all
the more important that we focus on.
Getting good data and
actually using it well.
also I want to thank Jeremy who has been
a huge help for us as we were getting
dbt set up and helping, make sure
that we are using it to its fullest.
but yes it is.
There's no good other as there
is no good answer other than.
It raises the stakes substantially,
which means that data quality,
down in governments, doing, making
sure that you're doing all these
checks is even more important.
It's also more important to be.
Transparent.
So we try and be really
transparent with our messages.
This is a little different than, some,
you'll see a lot of like how secrets or
like, like rehab, I am doing it, polling
those like just our special sauce.
and we kind of try and do things
the opposite and you're increasingly
seeing that happen as well.
So if people are following kind of the,
Election forecaster Wars on Twitter,
which God help you because they are,
they're, they're a lot to follow.
so G Elliot Morris, who is, Who is doing
the forecasting for the economist has been
throwing a lot of, throwing that, see that
Nate silver, because Nate silver has been
artificially in using uncertainty into
his model because his model was telling
him that one outcome is very likely in
the election, but need some of them.
I actually think that the
outcome is that certain.
Yeah.
Like there's like these probably, right?
Like the data is telling us.
This election looks not particularly
close, but the thing about the
particularly presidential election data,
there just aren't very many, I points that
obviously we are in a very unusual year.
So a lot of transparency and
humility is, and realizing that we
can't perfectly predict everything.
We can't perfectly know.
Everything has been something
that we try and focus on a lot.
I'm actually, I'm going to ask another
question sort of leading on from that.
So Jeremy sort of alluded to,
your previous role where you were
working at like, you know, sort of
more, I think VC funded startup.
and sort of how your relationship
with, with data quality has changed.
I'm curious whether there's like any
other, like maybe on the most surprising
aspects of like, if someone's used to
working, in the, for profit sector, if
they go into, you know, working for a
PAC or working in, another nonprofit,
how those, like how experiences can
be really different, how like those
experiences can be quite the same.
Yeah, it's a great question.
so I think that the, the thing that was
not necessarily surprising to me, but w
what was the kind of big F Th the, the
biggest learning that I had and the one
that I try and share with other people
who are maybe in the attack BC world and
are trying to get involved in politics
and political data is how much there is to
learn and to spend a lot of time listening
to why things are the way they are.
Okay.
So I was, I started getting involved
with that for progress, just as a
volunteer at the beginning of 2018.
And it was about two years of.
Just like not knowing anything
and just like updating Squarespace
pages and like really just
listening and watching and learning.
And then over time, finding
small ways that maybe tech can
help improve the operation.
I think there is a sense that I think.
I wanted to believe.
And Oh, a lot of people were like, Oh,
we can kind of fix a lot of the politics
just by applying the right technology.
And, you're always throwing enough
big data at it or anything like that.
The, the, the reality is it's the
people that are working on this.
Are incredibly smart,
incredibly, incredibly talented.
They've been working on it for decades
and there are, there, there are big
systemic reasons where I think why
things are the way they are and that
it is very possible to get in this,
into this and to hopefully be able to.
Nudge things a little bit to make
some processes more efficient,
but to not necessarily like,
think that, Oh, we'll go in.
We will, you know, apply agile.
What we need is we need to
apply agile to campaigns.
And just to really go in and listen, the
experts, listen to the people that have
been working on this and then find things.
Like dbt, being able to, take our, our
data transformation, which are incredibly
impactful and to look for those areas
where we can actually make them make
big impact is just, it requires a
lot of listening and kind of learning
about why things are the way they are.
