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>> Not all the data that is available to brands

and certainly not to us as marketing analyst are paid for.

There's several sources of free data.

Here are some of the ones that I have found

to be very very valuable in just collecting raw data

or some pretty simple metrics.

The US Census Bureau site,

the Bureau of Economic Analysis site, and the Bureau

of Labor Statistics site all provide really valuable

information about consumers here

and the population here in the US.

Data.gov as well as the Center for Disease Control

and Prevention also provide additional views

of consumers here in the US.

UNdata, the site there provides information for consumers

and people in the world outside of the US and gives much more

of a worldly view and worldly set of data for analysis.

In terms of processed data that I like,

Gapminder World is a fantastic site full of trends

and other information that is very valuable,

as well as the World Resource Institute.

And then the American Fact Finder takes that raw data,

processes it a bit and serves up something that is--

that is very valuable in the form of good, solid metrics.

Visual data is another source of information

and there's some great sites out there

that provide either graphic visualizations,

charts and graphs that you can use.

Flowing Data's a great site.

Information Aesthetics.

Information is Beautiful;

David McCandless' site is a real stand out--

stand out example of that.

Good Transparency, Visual Complexity, Marketing Charts,

all of these sites provide great visual data that you can use

in presentations to pull insights from right away

and to use in other forms.

And the list doesn't end there.

This is clearly not an exhausted list but there are so many sites

out there providing information.

The trick is for you as an analyst to get comfortable

with what sites give you the data that you need

to analyze your industry, your company

or your business venture, whatever it is

that you want to-- you're working with.

Those kinds of sites, what we're typically talking

about there are really what you'd classify

as bulk downloads.

It's a great deal of information that you're pulling

down in one or two steps, right.

But there are other techniques that you can use

as an analyst to access data.

Particularly I'm talking about using API's, tapping--

tapping API's or web scraping.

Very sophisticated techniques for sure

but once you master tapping into an API, say Twitter

or Google maps, you have a tremendous amount, a very rich--

rich data available to you.

And then once you learn how

to do web scraping every single page

on the worldwide web becomes an open source

of information for you.

And so if having sites that are designed to collect information

for you to use is not enough mastering these techniques

really opens up the whole world of the web to you

and turns everything into-- into a data source.

Now we're at a caution.

There is such great information out there and data available

to us but when we are collecting it we have to be clear

to be collecting good, solid, clean data.

The value of the analysis that we produce at the end

of our process is only going to be as good

as the data we put into it.

And so there's some biases that we need

to be-- need to be aware of.

One is questionnaire bias.

So if we are collecting voice of customer data

and we're using a survey it's important for us to realize

that that questionnaire can't be leading towards a consumer.

It can't be biasing them towards a certain action or answer

because if that-- if that survey instrument is the data we're

pulling in is slanted the analysis we produce we produce

on the backside is going to be slanted as well.

So being aware and working

against questionnaire biases is very important.

A second is sampling, sampling bias.

So if we are drawing some conclusions from a small sample

of a larger population it's important to make sure

that that sample is representative

of the entire population.

If it is not, if it is bias in any way,

the results we produce are not going

to translate to the bigger group.

And so you're-- you're left with having analysis

that does a good job of describing that sample

but not the broader population.

So that's a bias that we need to be cognizant of

and need to work to correct.

When you pull a sample out of a big group make sure

that that sample is indeed representative of the big group.

And then a final bias is just interpretation bias.

So this is a tendency of an analyst to be looking

for an answer in the data rather than just looking

at the data fairly and allowing it to tell the story back to us.

If you've done that you've walked into an analysis

with a preconceived way that you want it to go

or you are expecting it to go.

And as a result of that bias your analysis is going

to be slanted in that-- in that way.

You're basically going to get the answer that you're looking

for not the right answer.

So lesson six, Data Data Everywhere,

what did we talk about?

We saw that marketers seek data to better understand customers,

that targeting is king for brands

and the use of data to do that.

We saw that spending on qual research though has been

in decline and that the growth of online data and the abundance

of online data may explain why that is so.

We saw that today there are many many sources of valuable data

out there and as an analyst it's important for you to just figure

out which ones do I-- Do I understand?

Which ones do I know?

And what are my go to sources for data?

And then we saw how important it is for bias to be countered

so that we don't-- we don't poison the analysis

that we're conducting with bad data.

A little bit of supplemental reading for this lesson

to go a little deeper.

An article on the future of qual research

that even though the numbers are down now shifts in ways

that qual is directed that it's taking advantage

of the digital space and the great opportunities there.

And then ways that companies are using data

to influence consumers.

Getting an understand from a company's view

of how they can use data can help you as an analyst

as you're conducting your analysis.

Lesson 6: Data, Data, Everywhere, Part 2

市场营销专业人士所需的数字分析：理论市场分析

与讲师见面