This course will expose you to the data analytics practices executed in the business world. We will explore such key areas as the analytical process, how data is created, stored, accessed, and how the organization works with data and creates the environment in which analytics can flourish. What you learn in this course will give you a strong foundation in all the areas that support analytics and will help you to better position yourself for success within your organization. You’ll develop skills and a perspective that will make you more productive faster and allow you to become a valuable asset to your organization. This course also provides a basis for going deeper into advanced investigative and computational methods, which you have an opportunity to explore in future courses of the Data Analytics for Business specialization.
6. The Tools of the Data Analyst
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From the course by University of Colorado Boulder
Introduction to Data Analytics for Business
329 ratings
University of Colorado Boulder
329 ratings
Course 1 of 5 in the Specialization Advanced Business Analytics
From the lesson
Analytical Tools
In this module we’ll learn about the technologies that enable analytical work. We’ll examine data storage and databases, including the relational database. We’ll talk about Big Data and Cloud technologies and ideas like federation, virtualization, and in-memory computing. We’ll also walk through a landscape of some of the more common tool classes and learn how these tools support common analytical tasks.
Meet the Instructors
David TorgersonInstructor
We've covered a lot of ground in our module on analytical technologies.
We've talked about data storage,
including a detailed look at the relational database.
We've also outlined a number of different types of tools that exist
in the world of data and data analytics, both traditional and
emerging, including exposure to ideas like big data and cloud computing.
In this video we're going to take a look at how data analysts
actually use some of these tools.
For now, we're going to do this at a really high level.
You'll have plenty of opportunity in later modules and courses to go deeper and
do exactly how certain operations and analyses are done.
There are a lot of different tools and techniques out there and
we couldn't possibly cover them all.
However, we will cover just a few common ways analysts work with data and
how one might think about using one type of tool versus another.
We'll start by looking at three broad methodologies a data analyst might employ
to access and analyze data.
Let's call the first method the intermediate file approach.
In this approach we extract data from a database or
other location where data is stored, and we export the data we need into
a standalone file like a text file or Excel file.
This often involves writing SQL code against the database to extract
just the data we need.
We then import the data into an analytical tool like Excel, a business intelligence
tool, or a statistical software package or programming environment.
Once the data is in the analytical environment I can
execute whatever type of analysis is desired.
Note that this approach assumes that all the data I need is already integrated in
one database environment.
So why might I use this approach?
For starters, you might not be able to connect your analytical tools directly to
your database due to security or stability concerns.
Or you may not have time to set up all the permissions and
network connections required to make a direct connection.
It might be the case that you need to extract the data at one point in time and
analyze it later or analyze it offline.
You might also want to drive the same data set into multiple analytical tools.
In these cases, it's more convenient to store the data in an intermediate file and
import it for analysis.
Finally, by breaking access into two steps I have a little mores control and
visibility into each steps, which might be useful for data validation and
quality control.
A second method might be called the direct connection approach.
Wth this approach we connect our analytics tool directly to a database or
other data source using what's called an open database connectivity, or
ODBC connection, or some other application program interface or API connection.
Broadly, APIs are standard mechanisms for exchanging information between programs,
and ODBC is one special case of an API used to connect to databases.
Most analytical tools have the ability to connect directly to the most
common database systems and a number of other common data sources.
In this approach, I use the analytical tool interface to set up a connection and
identify the data I want to access.
Usually this involves the same ideas as SQL queries on a database.
The tool executes the extraction operation in the background,
and that can proceed with whatever analysis is desired.
There are a couple nice things about the direct connection approach.
First, it cuts out a couple of steps in the process
since I don't need to export and import data using an intermediate file.
Secondly, a direct connection can be set up to
automatically refresh as the underlying data changes.
Meaning that my analytical work can be refreshed as well
to reflect the most recent information.
On the downside, it can require a bit of set up to establish the actual connection
to the database.
And it's a little bit harder to be sure that the extraction process is happening
as intended.
Offline analysis can be a bit trickier as well
as we need to make sure we have a copy of our data stored locally.
The last method we'll cover is what we'll call the downstream integration approach.
It's quite often the case in data analytics that the information we need is
located in a bunch of different locations and formats.
While it would be nice to have everything on one warehouse, it doesn't always makes
sense to spend the time and energy to put everything there prior to analysis.
Especially if we're not entirely sure those sources will turn out to be
important.
The newer and more complex the analysis, the more likely it is that we'll need to
do at least some integration in the analytical environment.
In cases like this, we generally use a broader set of API and ODBC connections as
our analytical tool of choice to connect to several sources concurrently.
And use additional functionality in the tool to integrate the data
construct analytical data sets.
We then proceed, as we would, using the other methods.
Of course, there are a number of other approaches that we might take when
working with data to get the results we're looking for,
including hybrids of the ones we've discussed here.
For example, I might perform certain manipulations in Excel,
and then import the results into a more sophisticated analysis tool.
I can also do the opposite, using an advanced tool to
isolate some set of data that I want to incorporate into Excel.
Perhaps into a business or financial model.
The approach you take in any situation will depend on what it is
you're trying to do.
But one thing we haven't really discussed is when you'd want to use
one tool versus another.
This is a really complicated question and the answer depends not only on
the capabilities of the tool itself but on the skills of the analysts, the nature of
the data environment and even the organization in which an analyst works.
In our video on data and analysis tools we broadly discussed what functions
each type of tool is designed to perform, but we also saw that there's
quite a bit of overlap in the capabilities of different tools.
What one analyst finds really easy to do in one application
another analyst might find more intuitive in a different application.
That having been said, here are a few ideas that you can start with
that I've drawn from my own experience in leading analytical teams.
But you'll have to discover what works best in your environment.
Let's start with Excel.
Excel is really great for quick and dirty analyses.
Basic charts and graphs are for when you want to share your analysis with business
partners who don't have access to more sophisticated tools.
Excel also makes certain types of manipulations really easy,
like calculations that depend on multiple rows of data.
And it's a great environment for trial and error around really
complicated calculations since you can see every formula in every cell.
It also turns out that an awful lot of financial modeling and
business case development is done in excel.
When analysis outputs are used as the inputs to these Excel models,
it can be convenient just to start in that environment.
On the flipside, Excel is not necessarily a great tool for sharing data broadly or
for developing standard reports or dashboards.
There's also a size limitation.
The baseline Excel product can only handle about 1 million rows of data,
which may not be large enough for some analysis.
Although with Microsoft's power pivot plug-in, larger data sets can be handled.
Even with less than 1 million rows,
performance can be sluggish on anything but high-end machine.
Let's move on to business intelligence tools, which includes standard reporting,
data visualization, and data exploration tools.
These tools are a good choice for a wide variety of analytical needs intended
to make complex manipulation of data easier and faster than other tools.
It goes without saying that the analysis requires extensive exploration or
advance visualization techniques.
Tools suited to those operations will produce better results.
Additionally, some organizations will have tools that permanently sit on top of
a data environment and provide pre-built data structures like cubes, or
predefined calculations that facilitate repeated analytic operations.
Business intelligence tools are also preferable in cases when the output of
analysis will be shared broadly or turned into a standard report
since they typically include more advanced scheduling distribution functionality.
Statistical modeling and
advanced programming tools are the obvious choice when we need to do
highly sophisticated analysis, especially using advanced analytic techniques.
There are ways of incorporating more advanced techniques into both Excel and
other applications using plugins or complimentary applications.
In fact, we'll be using some of these in later courses.
However, few of these work were really well at scale,
where we need to analyze very large datasets, or
where we need to drive the results of our analysis back into business operations.
There are also cases where we want to have a really high degree of flexibility,
where we need very fast performance or
where we want to build analytics directly into other software or data processes.
In these cases, we might want to code our analytics from scratch
using a programming language adept at performing data manipulation.
The downside of this approach is that we have to build things from scratch.
We give up a lot of the simplification that's provided by more user
friendly tools.
So what should you take away from this discussion?
First, you should recognize that there are a lot of different ways of
navigating through the analytical process.
But there are a few common ways that we'll apply more often than not.
Secondly, when it comes to tools,
there's always more than one way to accomplish anything.
But there are some broad considerations that can help you pick which tool is right
for the job.
But like we said at the beginning of this module, the most powerful tool you have as
a data analyst is the one right here between your ears.
If you have a solid thought process and
a good foundation of knowledge about your data environment,
you'll be well down the road to success regardless of the tools you choose.
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