Recently, you've been learning about how businesses use data to guide decision
making, answer questions and solve problems.
In this video, we'll investigate how nonprofits use data analysis to pursue
their unique goals.
Nonprofit groups are created to further a social cause or provide benefit to the
public. As the name suggests, their main purpose is not about profit, but to
foster a collective public or social advantage.
There are some very rewarding and inspiring opportunities for data
professionals in the nonprofit sector.
In particular, data can be applied in order to help these groups more
effectively anticipate and respond to the greatest areas of need.
For instance, maybe a US charity that provides bicycles for children would
like to determine which neighborhoods are most in need.
They could ask their data professional to access the US Census Bureau.
The professional would use their talents to navigate the Census database,
identify key metrics and summarize findings with analyses and data
visualizations. This report would highlight where there are larger numbers
of school-aged children in need who would benefit from the resources of this
program, and there you go.
Data insights led to informed decisions about where this nonprofit can do the
most good. Now, nonprofits do more than use data.
Many of them also collect it.
As you likely know, public entities and government agencies can be excellent
resources for useful data, and much of it is open data that's available for
general use. As you likely know, open data is data that is available to the
public. It's free to use and guidance is provided to help navigate the data
sets and acknowledge the source.
While sourcing open data is a good way to interact with data on your own, there
are other opportunities that enable you to refine your skills while helping
others. Data volunteers contribute to many projects that help nonprofits
benefit communities all over the world.
To find out more, here are some organizations to check out.
First, the Data Science for Social Good Foundation was launched at the
University of Chicago back in 2013.
In 2020, they trained forces with UNICEF to analyze various aspects of air
pollution around the world to help monitor children's health.
DataKind launched in 2011 in New York City with chapters in the United
Kingdom, Bangalore, San Francisco, Singapore, and Washington, D.C.
This organization analyzes the cost of environmental cleanup in different
underserved communities to guide restorative efforts.
You can view both foundation's latest efforts through the links in the
transcript for this video.
Another option for putting your data skills to good use are hackathons.
A hackathon is an event where data professionals and programmers come
together and collaborate on a particular project.
The goal is to create a solution to an existing problem using technology.
Some examples include developing better tools for predicting extreme weather
events, creating tech to help elementary school kids learn important
reading skills and identifying ways that community development groups can
use their data to advance home accessibility and affordability.
Volunteering your data skills to public projects is an excellent way to
contribute to the greater good while gaining experience and networking with
others in your field. Coming up, we'll take a deeper look at some data-oriented
projects in the public sector and how they're making an impact around the
world. 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. And 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, and 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.
So 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 are 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% 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 teams 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 tees to support the local team.
Plus, they note that although this market is high growth, it only
represents 40% 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.