By mid-2018, 2.5 quintillion bytes of data were generated daily. We need to make decisions fast, and there isn't enough time to comb through that many millions (or quintillions) of rows of data to get answers to our most important questions. Transforming it into a visual format that is easy to understand is key to an effective presentation of critical information. Through visualization, data sets that are otherwise hard to interpret are presented in the simplest, often most useful, form. But what informs the choice of the graph that will help you achieve the most compelling presentation? This five-minute read has all the tips you need to choose the right option your data set.
What Is Data Visualization?
Simply put, data visualization is the technique used to transform a raw data set into graphical values to make the information contained therein easier to scrutinize and understand. It gives you the power to make informed decisions about day-to-day choices that have long-term success implications much faster.
Graphs, bars, charts, scatterplots, and heat-maps are just some of the ways of displaying data. Through this, just about anyone can recognize trends, parameters, or patterns. Human beings perceive the information presented on a visual or imagery form in a better way compared to plain text data. The key is to make this reality work for you—business users who are empowered to make decisions using quick information founded on good data is the bedrock of a successful long-term enterprise.
Choosing the best graph type to visualize your data set
Different graphs have the ability to tell different stories. To pick the right type of graphical visualization, you must consider the type of data set you have. If well designed, a graph can convey the strongest points that best highlight your information. If done wrong, users may not feel confident in the data or the person presenting it and not adopt the points trying to be driven home.
Below are a few commonly used visualizations and use cases for each:
1. Line graph
Line graphs are designed to display minor changes in a particular variable over a specified period. Change in customer count, revenue or net promoter score over a period of time are examples of items track in line graphs. Line graphs are also great for tracking to different data sets next to each other. Customers counts in two different countries, or the performance of two different product lines can easily be compared. Unlike bar graphs, tracking small changes in your data is better indicated in a line graph.
2. Pie chart
This type of data visualization seeks to present relative parts of data on a pie slice. Each slice represents a proportion of the aggregate data. This allows you to compare different segments of your information and visually indicate the values that fit in each category. For example, time spent on daily activities as shown here. Time is the common measure and the activities broken out allow the user to compare/contrast the time spent in each category. This is especially useful for looking at time spent on different work projects, products revenue related to total revenue, specific campaigns as part of marketing budgets, etc. Although pie charts can be as accurate as needed, they are great for getting a quick gauge on related items versus diving into too much detail.
3. Bar graph
Like line graphs, bar graphs are used to track the changes in values in data set. Changes can be compared over time, such as revenue year over year, or other items such as competing cities. Bar graphs have the ability to compare multiple values in one visualization which make them very versatile.
4. Area graph
Area charts are a time-series chart which allow the user to visualize relationships of items over a period of time. This makes it easier to track the trends of multiple items as they relate to each other. For example, let's say a marketer wanted to see total website sessions and source of traffic month by month for the current year. Having an area chart which tracks the sources - social, organic, paid search, referral, etc. will allow the marketer to see the trends of each and how each source relates to each other's performance.
5. Combo Chart
A combo chart uses two graphs to present multiple variables in the same layout. An example can be the fusion of a line and bar graph. This type of hybrid graphical representation is appropriate when you have two series of data with varying degrees of measurement that can only be indicated in different formats. A great example of this is total revenue by month, year over year (represented by a bar graph) and a line graph tracking the average revenue per transaction. This gives the business user a look at the correlation between the dependency of customer purchase price, and the health of the organization. Combo charts are a great tool to use when trying to tell a story about how two seemingly unrelated items actually have a significant relationship.
Similar to a bar graph, a histogram is precisely vital while working with numerical variables. This visual data presentation groups data into several categories called bins, to show the frequency distribution of data over a certain interval. Unlike a bar chart where height matters, the total area of the histogram tells the frequency at which occurrences happen within a bin. An example of this could be the number of employees whose salaries range between 80-90k, 91-100k, 101-110k and so on.
Scatterplots borrow an element of line graphs in that plots on an axis highlight trends in statistical data. Similarities of different variables can be tracked, so the reader can easily understand the relationship between two sets of variables such as cost per acquisition (x axis) and total revenue per sale (y axis). Scatter plots allow you to see clusters of data as well as outliers, giving the user an understanding of what "normal" should be as well as the opportunity to identify anything that seems out of the ordinary to investigate further.
8. Time-series graphs
This type of data visualization portrays snapshots of data identified over a duration of time. think of it as a picture flip book, where each page represents a different snapshot in time. It is commonly applied to determine the pattern of a trend that has changed at an identified time interval. A great example of this is the price of a stock over a period of time.
Helios Can Help With Your Data Strategy
Depending on what you strive to achieve with your data, the above graphs can be a sure way to present your data. If you need help understanding how data influences your business strategy, Helios can help. Our analytics systems are designed with clarity and user interaction in mind. Want to find out more? Call us today to set up a free consultation, a demo, or just ask a couple of questions: 888-510-3779.