All language subtitles for 03_important-ethical-considerations-for-data-professionals.en

One of the most important

responsibilities
for those of us in

data-centered
careers involves how

we protect our organizations,

manage and protect data.

This has a lot to do with

communication exchanges

between a company
and its customers.

As you've been learning,

almost all communication
generates data,

whether it's a shopping receipt,

confirmation of an order,

or even earning customer
loyalty points.

Businesses have a big
responsibility to their customers,

especially when it comes to

maintaining and
protecting user privacy.

Any data gathered from

individuals or consumers
is referred to

as personally identifiable
information or PII.

PII permits the identity
of an individual to be

inferred by either direct
or indirect means.

This includes things
like biometric records,

usernames, and Social Security

or national
identification numbers.

Because this
information is often

associated with
medical, financial,

and employment records,

PII is sensitive and must
be managed with great care.

After all, when
someone's personal data

is improperly handled,

they become vulnerable
to identity theft,

fraud, and other issues.

Recently, there have been
great efforts to take

a wider view of

data collection practices
and protect individuals.

Industries are trending
towards aggregate information.

This is data from a
significant number of

users that has eliminated
personal information.

By aggregating the
data and removing PII,

this protects people
and gives them

more control over
their own data.

Similarly, as more industries
become interconnected,

the amount of data available
to them increases.

Just as with aggregate
information,

the more data collected,

the more likely it
is that it will be

representative of
a wider population

rather than a single user.

A key thing to keep
in mind is that

data gathering is a
task managed by humans,

and that process can be informed

by different backgrounds,

experiences, beliefs,
and worldviews.

These and other types
of biases can affect

the way that data
is communicated

and how the results are shared,

which in turn can have an
impact on business decisions.

Effective data
professionals know that,

whether collecting,
analyzing, interpreting,

or communicating sensitive data,

bias should always
be considered.

So be very careful when

interpreting data where
there is a clear source

of bias and be on the lookout
for subtle biases as well.

In addition to thinking
through bias in the data,

data professionals
should also try

to emphasize that there can be

a multitude of possible
interpretations

for every data insight.

The main trick is avoid

jumping to conclusions until

you've really done
your homework.

One method of
addressing bias is to

make sure that the
data that you're

working with has the
same characteristics

as the greater population
that you're interested in.

In data analytics, this
is called a sample.

A good sample is a
segment of a population

that is representative of
that entire population.

Here's an example.

A clothing company is

analyzing sales in their
highest growth market.

They want to determine
what color shirts will

be most popular in
the upcoming season.

One person notes that

red and blue shirts
accounted for 80 percent of

their sales in this market over

the past three months.
This is a big number.

So they suggest ordering
lots of red and blue shirts,

but another person
points out that

the local sports team's
colors are red and blue,

and this team had recently
won a championship.

It's very likely that sales

of red and blue shirts will have

spiked as consumers purchase

tease to support the local team.

Plus, they note that

although this market
is high-growth,

it only represents 40 percent

of the retailer's total sales.

With all this
information in mind,

decision-makers at
this retailer instead

choose to evaluate
color popularity

over a full year and
across all markets.

This will provide a much
more complete picture.

We'll investigate more about
bias later in this program,

and as you progress,

you'll discover many
more strategies

for ensuring that
you're aware of bias

and proactively
working to counter

it in all of your data work.