SPEAKER: With business intelligence and data analytics
gaining so much popularity these days,
the terms "business intelligence"
and "data analytics" are often defined incorrectly,
oversimplified, or used interchangeably
for speedy communication.
That can lead to confusion, because BI and DA
are quite different.
In this video, we're going to sort that out.
First, let's establish that both BI and DA professionals enable
data-driven decision making in their organizations.
They're also both a key part of their company's data maturity.
Data maturity is the extent to which an organization
is able to effectively use its data in order
to extract actionable insights.
Whether that data includes information
about your customers, staff, products, suppliers, finances,
or something else, it's worthless
if it can't be put to work.
Putting data to work requires skilled professionals
who can apply techniques and technologies to achieve
high levels of maturity.
In this effort, data analysts are primarily
involved with using data to answer questions
about what happened.
On the other hand, BI professionals
aim to achieve higher levels of data maturity
by building data reporting tools, such as dashboards.
Dashboards continuously analyze and monitor data,
so the work isn't complete just because the tool
has been created.
The analysis and monitoring are ongoing.
This is because BI involves establishing repeatable methods
to understand how things are operating.
And by knowing the current state,
company leaders can take action to improve the future state.
For example, a BI professional might investigate
what new sales prospects are being targeted today,
how many customers have we gained this month,
or did our weekly email perform well with new subscribers?
Because BI is all about near real-time rapid monitoring,
BI insights are most effective when
they make an impact right now.
Therefore, the people who use the reporting tools
want to make sure they are practical and efficient.
BI professionals are expert tool builders.
Then it's typically the data analysts
who apply these tools in order to answer a question
or solve a problem by examining the data
through a specific topic or lens.
Again, a big part of BI is creating
dashboards that provide users with clear snapshots
of the current state.
These tools must be impactful and easy to interpret, even
for non-technical people.
So if you like thinking about what
you can create that meets the needs of many different users,
BI will let you do just that.
BI professionals generally work on large scale projects
that are helpful for multiple stakeholders.
Also, as you consider how you will fit in the BI world,
note that BI professionals are experts in data infrastructure
and enjoy the technical side of data analytics.
For instance, if you love working with databases,
big data, and the computer programming language SQL,
BI will give you the opportunity to take
your queries to the next level.
If you earned the Google Data Analytics Certificate,
the content included there formed
your foundational knowledge and experience.
Now this BI program will provide a complementary skill set that
builds on those foundations.
It will also add a lot of very marketable talents
to your resume that will create a compelling combination
of skills for employers to consider,
and will open the doors for more job opportunities.
In addition to the technical side of things,
such as managing large data sets, writing queries in SQL,
and creating dashboards, you'll learn some really
worthwhile professional skills.
These include the ability to juggle
many different priorities, attention to detail,
owning a process, communicating with others,
and learning how to go beyond surface-level requests.
Perhaps most importantly, you'll discover
the value of connecting with others to really drive results.
And that's how you build a career
rather than just have a job.
These are also great things to keep
in mind as you continue planning for your portfolio project.
Consider how you can use this upcoming project
as an opportunity to demonstrate both your technical skills
and your ability to thrive in a complex workplace
with a diverse group of colleagues.
In future lessons, you will continue
to explore the similarities and differences between BI and DA.
The two disciplines complement and rely on each other,
just like the talented people who work in these roles.
Together, they help their organizations advance
on the data maturity scale every day.
If you earned your Google Data Analytics Certificate,
then you're very familiar with the data lifecycle and the data
analysis process.
Data analysts use these phases in their work every single day
in order to gain insights that lead
to informed decision making.
As a quick refresher, the data lifecycle
is a sequence of stages that data experiences, which
include plan, capture, manage, analyze, archive, and destroy.
And the data analysis process occurs in six phases--
ask, prepare, process, analyze, share, and act.
Business intelligence also has phases--
a sequence of three stages that determines the value of BI,
as well as organizational data maturity.
As you've learned, data maturity is a meaningful goal
for any BI team, because high maturity
means their organization is effectively
using its data in order to extract actionable insights.
In BI, the three stages are capture, analyze, and monitor.
As you advance through each one, the process
requires a deeper level of exploration and investigation,
so each becomes more complex.
These phases might be automated or done manually,
but each one adds significant business impact,
which is well worth the effort.
It's important to understand these stages
and how they can benefit you as a BI professional,
so we're going to explore each one of them in this video.
Let's start with capture, which is the "what happened" point
in the BI process.
This stage pertains to static, backward-looking data.
For instance, if you were to query a database
to return a data set about what your customers purchased
last month, that's an example of capturing information.
Or you might access a spreadsheet
that lists profits and losses from the previous quarter.
That's also an example of capturing a record
of certain information.
Records are essential to BI, but they
don't enable users to easily investigate in depth
and really dig into the data, and that hinders the insights
they're able to provide.
Also, because the data in this stage
is rigid and backward-looking, it's not always very useful
for proactive, forward-looking decisions.
Capturing information is a necessary element of BI,
but information alone doesn't let
us know what's working well, how we can improve,
or what to do next.
However, in the following step things
get better, which brings me to stage two,
analyze-- the "why did it happen" part of BI.
You already know a lot about this stage,
but as a quick reminder, data analysis
is when we draw conclusions, make predictions, and drive
informed decision making.
Because the analyze stage explores why things happen,
it's much more likely to result in effective plans
and strategies.
In this way, it enables BI professionals
to better understand key relationships between data
points.
They do this by examining data more deeply,
more broadly, or even side by side,
in order to identify connections that at first might not
have been apparent.
OK, now we've come to the final part of the process--
monitor.
Monitor is the "what's happening now" stage.
And here's where BI professionals really
get to show off their unique skills and expertise.
In this stage, you'll use automated processes
and information channels, such as data models, ETL pipelines,
dashboards, and more.
These exciting tools take the data
that organizations create every single day
and transform it into true intelligence that is easily
available to stakeholders.
These BI tools reveal upswings, downturns, changes, challenges,
opportunities, and much more.
Decision makers then use the insights
these tools deliver in order to proactively work
towards business goals.
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