Hello and welcome to this lecture where I just want to highlight some of the key reasons behind data visualization, and how important it is to visualize your data in the most appropriate way.

As the saying goes, a picture can tell a thousand words, and this is also true when you display your data within a graph or chart. Let’s take a quick look at some of the benefits in using data visualization.

• A fast and effective way for humans to process data: When you have a large data set that shows a vast amount of data it can be very difficult to understand what that data is telling you when it is laid out in a table format, for example within a spreadsheet.  For many of us, our brains are simply not able to interpret data when it's like this in an effective and measurable way.  Instead, we need a simplified visual representation of that data, display within a chart to provide us visual clarity and definition of what the data contains

• It helps to drive business decisions: Businesses are very data-centric and are now able to collect and store vast amounts of data across every process and system being used far easier than they ever could before.  Having access to such data can help to drive business decisions in a very strategic way. Depending on what the data is showing the business can help you drive focus into one area that might be a bottleneck of sales, or invest more heavily in one part of the business that is showing a strong positive trend.  And that brings me nicely on to the next benefit, being able to identify trends.

• Identify important trends: Depending on the type of visualization you use to represent the data can really help you to determine trends over time amongst a data set.  These trends can help your organization identify and understand both positive and negative results from your data, which without visualization could be very difficult to highlight.

• Highlight data relationships: Being able to spot and identify relationships within your data is key, it can help you to both drive future business decisions in the right direction and also to make corrective actions elsewhere.  For example, suppose one of your regional sales representatives is hitting great results, but for some reason, your other regions are doing particularly bad, you can dive into the positive sales data to understand data relationships on the positive sales and correlate that back to the other regions to see what’s missing.  

• The consumption of data increases: Being able to store copious amounts of data is one thing, but being able to utilize that data is another.  By visualizing table results and other statistical information, enables multiple teams to draw upon the visualizations, such as charts, and use them in their own way, within their own departments.  Having a quick reference to a visualization allows the data to be shared easily with multiple recipients that can quickly and easily process the data without having the technical knowledge of how that data was collated.  

So these have just been a few advantages of being able to visualize data.  Now I mentioned while talking about these, both charts and graphs, but is there a difference between them? These two terms are used interchangeably, however, they are considered to be different from each other, put simply: 

Charts: A chart itself displays information using tables, diagrams, and indeed graphs.

Graphs: A graph, however, presents data in a visual mathematical format, usually along 2 dimensions, allowing you to see visual relationships of the data set in question.  

When using charts, there are a variety of ways to present your data, depending on what type of data you are trying to show.  For each use case, there will be a specific type of chart, for example:

• Relationships: You might be trying to present data that shows relationships between data points, so here you might use scatter or bubble charts

• Comparisons: If you are trying to compare data between two or more data sets, then perhaps a Bar, Column or Line chart would be most appropriate

• Distributions: When looking at the distribution of data across an entire data set I shall be looking at how histograms might be your best solution

• Compositions: Finally, if you are looking to form a composition chart which presents the part-to-whole relationship of a data set.  The most common way of doing this is with a pie chart, but I shall also be looking at a stacked column chart, 100% stacked column chart, and a tree map.

So now have an understanding of some of the benefits of data visualization and why you would want to present your data using visualization techniques.  Let me now dive deeper into the different types of charts that I just highlighted that you could use for individual use cases.  Over the coming lectures, I will focus on Relationships, comparisons, distributions, and compositions.

Lectures

Course Introduction - Visualizing Data Relationships - Visualizing Data Comparisons - Visualizing Data Distribution - Visualizing Data Composition - Course Summary