Name: Harnessing the power of data post-covid
Uploaded: 2022-09-23T12:59:26.912Z
Duration: 411.03
Description: The topic of "big data" has received considerable hype in recent years, especially in the post-Covid period. Not only big companies but also governments are trying to find the right answer about how to harness big data effectively. Three data leaders Amelia Eddleman, Natasha Hillier, and Susan Sumida summarize their views on how to effectively harness data.

Harnessing the power of data.vtt

English

{{00:00:31}}
We really would like to understand
a bit of a perspective on

{{00:00:35}}
what do we do with all the
data that happened pre-COVID.

{{00:00:37}}
We of course know that people
have changed their behaviors.

{{00:00:40}}
There's different types of data that
we're collecting now, in this sort

{{00:00:43}}
of new world that we're living in.

{{00:00:44}}
And so what does that mean
from a longitudinal perspective

{{00:00:48}}
and has it really changed or
has it not, not that much.

{{00:00:51}}
I'm going to direct my first question
over to you, Amelia from Google.

{{00:00:56}}
So obviously working from Google,
working for Google, we'd love to hear

{{00:01:01}}
your perspective about how you and
your team are not paralyzed by all

{{00:01:07}}
that data that they be collecting.

{{00:01:10}}
But actually, how do
you really harness it?

{{00:01:12}}
And the power of it in
a very actionable way.

{{00:01:15}}
It's so easy to be
completely overwhelmed with

{{00:01:19}}
data even just pre COVID.

{{00:01:21}}
It's been a lot harder lately given how
plentiful and volatile data has been.

{{00:01:26}}
And on top of that, I'm sure
I'm not alone that my own time

{{00:01:30}}
to consume and make sense of
data has definitely shrunk.

{{00:01:34}}
So when you think about like
the title of this data has

{{00:01:36}}
definitely felt like a foe lately.

{{00:01:38}}
So if you feel that
way, you're not alone.

{{00:01:40}}
I can't say I've nailed it, but
there have been a few things that

{{00:01:43}}
I think have been helpful when it
comes to my team and I really sorting

{{00:01:46}}
through everything that's available.

{{00:01:47}}
And the first thing is focus.

{{00:01:49}}
So specifically focusing
on business objectives.

{{00:01:51}}
So there's all this data out there,
but only some of it is actually

{{00:01:54}}
applicable to the decisions
you have to make immediately.

{{00:01:57}}
And so looking at what are
those immediate decisions?

{{00:02:00}}
My team started triaging everything
that we got as a request.

{{00:02:03}}
All of our current projects on this
really ruthless prioritization rubric

{{00:02:07}}
and anything that wasn't essential,
we had to set aside because there was

{{00:02:10}}
just way too much happening at once.

{{00:02:12}}
And the other thing is
team cross-collaboration.

{{00:02:16}}
And so we actually have a bunch
of marketing research teams at

{{00:02:19}}
Google and we report up through
different sort of chains of command.

{{00:02:23}}
And, we had to put together an
internal effort to bring a whole

{{00:02:27}}
bunch of different research together
because we found out we were all

{{00:02:30}}
sort of going after a lot of the same
questions, which is super problematic.

{{00:02:33}}
And that might seem really basic but
at a company of over a hundred thousand

{{00:02:36}}
people with just dozens of products
and audiences and stakeholders it's

{{00:02:41}}
actually super complex to do that.

{{00:02:43}}
And so we were really proud that
we were able to do that actually

{{00:02:46}}
for the first time to bring a
whole bunch of different research

{{00:02:49}}
requests into one streamlined set
of work so we could all use it.

{{00:02:54}}
And the last thing is when you
have all this data, like we do all

{{00:02:57}}
the time, I really try and focus
on synthesis, because I think as

{{00:03:01}}
researchers, we often want to be
the source of new information.

{{00:03:04}}
We want to collect it ourselves
so that we really believe in it.

{{00:03:07}}
And lately the problem has not been
how much information we don't have,

{{00:03:12}}
it has been how much we already have.

{{00:03:14}}
And so we have to keep that instinct
and check and really do a better

{{00:03:17}}
job of not adding to the flood
of data and instead using what

{{00:03:22}}
already exists to develop a super
actionable point of view that is

{{00:03:25}}
specific to our business objectives.

{{00:03:27}}
So focus, cross-team collaboration
and synthesis are all the things

{{00:03:32}}
that I would say we're really focused
on to maintain all the data that

{{00:03:36}}
we've got, and actually use it.

{{00:03:38}}
There's just too much data and it's
like a big deluge of information.

{{00:03:43}}
Okay.

{{00:03:43}}
It's like coming at you and you need to
be able to take a look at it and focus

{{00:03:48}}
on what's important, but at the same
time, not be too granular that you're

{{00:03:53}}
not actually taking a look at other
things that you should be considering.

{{00:03:56}}
So again, it's like the
elements of good judgment.

{{00:03:58}}
It's like, yes, you need to focus.

{{00:04:00}}
Yes.

{{00:04:00}}
You definitely have
to synthesize things.

{{00:04:02}}
But at the same time, you have to
make sure that there is diversity

{{00:04:06}}
in the data that you're looking at.

{{00:04:09}}
It's not just about validation
because you might miss that

{{00:04:12}}
one thing that actually, it's
like an aha moment for you.

{{00:04:16}}
It's like, oh, we didn't consider that.

{{00:04:18}}
That's something that
we should be pegging on.

{{00:04:20}}
The other thing is it has to
be relevant, but it also has

{{00:04:23}}
to be narrow or actionable.

{{00:04:26}}
And how we do things, at least in
the back, it's a global organization,

{{00:04:31}}
we have regional concerns and
then we have in country concerns.

{{00:04:34}}
We need to really react
in an agile fashion.

{{00:04:37}}
So we do our daily stand-ups,
we go over what's important.

{{00:04:41}}
And then we actually come up with
actions that are, you know, basically

{{00:04:45}}
tailored against what we found out
and then we do retrospective, so that

{{00:04:49}}
we'll see, okay, let's validate this.

{{00:04:50}}
Did we do the right thing?

{{00:04:52}}
Is it showing the things that
we were supposed to achieve?

{{00:04:55}}
So, again, going back
through definitely focused.

{{00:04:58}}
Yes, you need to synthesize, but you
have to be diverse in all of the things

{{00:05:02}}
that you are looking at and you also
need to do retrospectives on all of

{{00:05:06}}
those things that you've actioned on.

{{00:05:13}}
Focus is really, really
important, especially now.

{{00:05:17}}
There's so much data out there.

{{00:05:19}}
There's so many demands on the business
and the research that we're doing

{{00:05:23}}
that it's really important to focus
on, on the right things and focusing

{{00:05:26}}
on the things that we know we could
answer with a survey versus things we

{{00:05:31}}
don't need to answer with the survey.

{{00:05:33}}
I read an article paraphrase, the head
of insights at Slack, who I don't know

{{00:05:38}}
personally, but who said essentially
like be a teacher, not an Oracle.

{{00:05:42}}
The pressure of being
an Oracle is too much.

{{00:05:45}}
And, instead of being the bottleneck,
which is a problem, be the enabler.

{{00:05:50}}
So think like dashboards, archives,
like just give people the information

{{00:05:54}}
they need to make better decisions and
trust that they'll do as good a job as

{{00:05:58}}
they can, and don't be the bottleneck.

{{00:05:59}}
Us folks who are researchers,
we look at the data and whenever

{{00:06:05}}
you go into a packed room with
executives, the bias usually is,

{{00:06:10}}
well, what is the hippo going to say?

{{00:06:12}}
The hippo being the highest paid,
you know, opinion in the room.

{{00:06:17}}
So identify the hippo and then just
go along with what he has to say.

{{00:06:20}}
No, you do not go along with the hippo.

{{00:06:23}}
You have to focus on the data you
have to see what is relevant and

{{00:06:27}}
the piece of advice I would give you
as you identify the hippo, or then

{{00:06:31}}
identify your data and then challenge
the biases that are in the room.

{{00:06:36}}
Because you have to speak truth,
and that's why you're there.

{{00:06:39}}
You're not there to validate you are
there to provide what you see is true.

We really would like to understand
a bit of a perspective on

what do we do with all the
data that happened pre-COVID.

We of course know that people
have changed their behaviors.

There's different types of data that
we're collecting now, in this sort

And so what does that mean
from a longitudinal perspective

and has it really changed or
has it not, not that much.

I'm going to direct my first question
over to you, Amelia from Google.

So obviously working from Google,
working for Google, we'd love to hear

your perspective about how you and
your team are not paralyzed by all

But actually, how do
you really harness it?

And the power of it in
a very actionable way.

It's so easy to be
completely overwhelmed with

It's been a lot harder lately given how
plentiful and volatile data has been.

And on top of that, I'm sure
I'm not alone that my own time

to consume and make sense of
data has definitely shrunk.

So when you think about like
the title of this data has

So if you feel that
way, you're not alone.

I can't say I've nailed it, but
there have been a few things that

I think have been helpful when it
comes to my team and I really sorting

So specifically focusing
on business objectives.

So there's all this data out there,
but only some of it is actually

applicable to the decisions
you have to make immediately.

And so looking at what are
those immediate decisions?

My team started triaging everything
that we got as a request.

All of our current projects on this
really ruthless prioritization rubric

and anything that wasn't essential,
we had to set aside because there was

And the other thing is
team cross-collaboration.

And so we actually have a bunch
of marketing research teams at

Google and we report up through
different sort of chains of command.

And, we had to put together an
internal effort to bring a whole

bunch of different research together
because we found out we were all

sort of going after a lot of the same
questions, which is super problematic.

And that might seem really basic but
at a company of over a hundred thousand

people with just dozens of products
and audiences and stakeholders it's

And so we were really proud that
we were able to do that actually

for the first time to bring a
whole bunch of different research

requests into one streamlined set
of work so we could all use it.

And the last thing is when you
have all this data, like we do all

the time, I really try and focus
on synthesis, because I think as

researchers, we often want to be
the source of new information.

We want to collect it ourselves
so that we really believe in it.

And lately the problem has not been
how much information we don't have,

And so we have to keep that instinct
and check and really do a better

job of not adding to the flood
of data and instead using what

already exists to develop a super
actionable point of view that is

So focus, cross-team collaboration
and synthesis are all the things

that I would say we're really focused
on to maintain all the data that

There's just too much data and it's
like a big deluge of information.

It's like coming at you and you need to
be able to take a look at it and focus

on what's important, but at the same
time, not be too granular that you're

not actually taking a look at other
things that you should be considering.

So again, it's like the
elements of good judgment.

You definitely have
to synthesize things.

But at the same time, you have to
make sure that there is diversity

It's not just about validation
because you might miss that

one thing that actually, it's
like an aha moment for you.

That's something that
we should be pegging on.

The other thing is it has to
be relevant, but it also has

And how we do things, at least in
the back, it's a global organization,

we have regional concerns and
then we have in country concerns.

We need to really react
in an agile fashion.

So we do our daily stand-ups,
we go over what's important.

And then we actually come up with
actions that are, you know, basically

tailored against what we found out
and then we do retrospective, so that

Is it showing the things that
we were supposed to achieve?

So, again, going back
through definitely focused.

Yes, you need to synthesize, but you
have to be diverse in all of the things

that you are looking at and you also
need to do retrospectives on all of

Focus is really, really
important, especially now.

There's so many demands on the business
and the research that we're doing

that it's really important to focus
on, on the right things and focusing

on the things that we know we could
answer with a survey versus things we

I read an article paraphrase, the head
of insights at Slack, who I don't know

personally, but who said essentially
like be a teacher, not an Oracle.

The pressure of being
an Oracle is too much.

And, instead of being the bottleneck,
which is a problem, be the enabler.

So think like dashboards, archives,
like just give people the information

they need to make better decisions and
trust that they'll do as good a job as

Best Practice

The topic of "big data" has received considerable hype in recent years, especially in the post-Covid period.
Not only big companies but also governments are trying to find the right answer about how to harness big data effectively.
Three data leaders Amelia Eddleman, Natasha Hillier, and Susan Sumida summarize their views on how to effectively harness data. 

thumbnail_Susan Sumida.jfif

xsmall_Susan Sumida.jfif

Susan Sumida.jfif

Susan Sumida

Director Research Operations and Programs 

medium_Natasha Hillier.jfif

small_Natasha Hillier.jfif

thumbnail_Natasha Hillier.jfif

xsmall_Natasha Hillier.jfif

Natasha Hillier.jfif

Natasha Hillier

Head of Customer Insight CMB & GBM Marketing 

thumbnail_Amelia Eddleman.jfif

xsmall_Amelia Eddleman.jfif

Amelia Eddleman.jfif

Amelia Eddleman

 Head of Market Research and Insights for Consumer Apps 

Introduction

Google`s Amelia Eddleman points out three key elements about harnessing big data effectively: Focusing on business objectives, team cross-collaboration and synthesis. 

Chapter 1: Amelia Eddleman

Natasha Hillier agrees that there is too much data and you have to focus on what you need. It takes a good judgement.
Data has to be relevant but also it has to be narrow and actionable. 

Chapter 2: Natasha Hillier

Chapter 3: Top advise for harnessing data 

With so much data out there and so many demands from the businesses, focus could be the essential point. 

Susan Sumida 

"Be a teacher not an oracle"
Being an oracle carries too much pressure, so instead of being a bottleneck be an enabler. 

No need to validate what the executives are looking for. Data researchers should challenge the biases and tell the truth about what they see in the data. 