Original subtitles

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.