Data visualization serves as a powerful method for conveying data insights in a manner that is easily comprehensible. It plays a pivotal role in transforming raw data into meaningful insights. Those insights drive decision making. Two of the main purposes of data visualization for business leaders are tied to the exploration and explanation of descriptive statistics.
You don’t have to be a data scientist to use descriptive statistics. In fact, you are probably collecting, working with, making decisions from, and communicating with these types of numbers every day. If you’ve ever compared averages or plotted a trend line as factors to consider when choosing how to move forward, you’ve harnessed the power of descriptive and diagnostic analysis to optimize the benefits of data driven decision-making.
Data visualization enhances the insights that data analysis can yield by providing clarity and focus. This is true of both exploratory as well as explanatory analysis. This article will examine the role data visualization plays in communicating the data insights born of common descriptive statistical analysis. The practical application of these concepts will be illustrated with real-world examples from the nonprofit and healthcare administration spaces.
Descriptive Versus Inferential Analytics
To set the stage, it's first important to understand a bit about the numbers we are trying to communicate. There are various types of data analysis in business, which range from descriptive to prescriptive and fall along a spectrum from hindsight to insight to foresight.
Descriptive statistics commonly used by many professionals to summarize and analyze data include measure of central tendency (mean, median, mode), standard deviation, variance, range, and percentiles to name just a few.
Descriptive statistics is like taking a snapshot of your data. It's all about summarizing and describing the key things you see in your information without trying to guess things about a bigger group. It helps you get a clear and simple picture of your data, showing you things like what's in the middle, like an average, and how spread out your data is.
Inferential statistics, on the other hand, are typically the domain of data scientists and aim to provide foresight. The predictive analytics go a step further. It's like using clues from a small part of your data to make educated guesses about a whole bunch of similar data. In business, this helps us make predictions, test ideas, and understand bigger trends based on what is seen in a smaller group.
COMMON DESCRIPTIVE ANALYTICS IN BUSINESS
The following table contains a list of ten common descriptive analysis measures used in business reporting.
DESCRIPTIVE MEASURE | DESCRIPTION |
|---|---|
Mean (Average) | The sum of all values divided by the number of values. It represents the central tendency of data. |
Median | The middle value in a dataset when it is ordered. It's less affected by outliers compared to the mean |
Mode | The most frequently occurring value in a dataset |
Standard Deviation | A measure of the dispersion or spread of data points from the mean. A higher standard deviation indicates more variability. |
Variance | The average of the squared differences from the Mean. It measures how much each data point varies from the mean. |
Range | The difference between the maximum and minimum values in a dataset. It provides a simple measure of the spread |
Percentiles | Values that divide a dataset into 100 equal parts. The 25th, 50th (median), and 75th percentiles are commonly used. |
Interquartile Range (IQR): | The range between the 25th and 75th percentiles, which provides a measure of the spread around the median. |
Frequency Distribution | A table or graph that shows how often each value appears in a dataset. |
Histogram | A graphical representation of the frequency distribution, useful for visualizing the data's shape |
While indeed data scientists are present and needed to do the deep dive for predictive analysis, most business leaders are working with, making decision from, and needing to communicate the data generated from descriptive statistical analysis. Every decision maker can benefit from learning how to use data visualization more effectively.
Data Visualization: Making the Data Easier to Understand
Before going further, let’s establish a common language around what is meant by data visualization. Data visualization involves representing data in a graphical or pictorial form, making it easier for users to identify patterns, trends, and outliers.
When you're dealing with descriptive statistics, you use pictures and charts to make things easier to understand for your audience, even if that audience is only you. These graphical representation tools, like bar charts and graphs, help you see your data in a way that makes it clear and easier to work with.
Selecting the best visualization tool for your data will help those insights be communicated, whether gleaning insights for yourself or sharing them with others.
Data visualization is one of the essential aspects of learning how to more effectively communicate with data. It is one of the six data storytelling essentials you should know. Fortunately, it is a practical skill that can be acquired and applied.
In the next section, we’ll look at visualizing data analytics with examples from both the nonprofit and healthcare spaces.
Visualizing Business Data Analytics in Nonprofit and Healthcare Leadership
Data visualization is best understood through practical business applications. The following two examples are from spheres with which I am personally familiar, nonprofit leadership and healthcare administration. Through these relatable scenarios, we will explore the difference that data visualization can make in exploratory and explanatory analysis.
A Data Visualization Example from Nonprofit Leadership
Imagine a nonprofit organization looking to improve its fundraising efforts. By visualizing historical donation data, they can explore patterns in donor behavior, such as donation frequency and amount based on demographics.
Here is an example of what the data and simple analysis might look like:
Exploratory Visualization involves using visuals to dive into a new dataset, creating graphs and charts to understand patterns, anomalies, and trends for personal
comprehension. This stage is vital for unearthing hidden insights within the data.
This exploration might reveal that donors from a specific age group tend to contribute with more frequency or greater amounts during certain months,
enabling the organization to tailor its fundraising campaigns accordingly.
These two graphs illustrate the
difference in visualization techniques and how they can reveal distinct insights into donor behavior.
Each visualization reveals a different insight into the data. The bar chart for average donation frequency indicates that the 25-35 age group gives more frequently on average albeit in smaller amount. The line graph trends giving over time and reveals that May and July garner higher than average donation amounts.
A Data Visualization Example from Healthcare Administration
Consider a healthcare administrator analyzing patient wait times in a hospital's emergency department. After conducting exploratory data analysis, they discover that certain times of the day exhibit significantly longer wait times.
Here is an example of what the data and simple analysis might look like:
Explanatory Visualization
Once insights have been extracted through exploratory visualization, the focus shifts to effectively communicating these findings to a broader audience. Clarity and simplicity are paramount in explanatory visualization, aiming to convey complex information understandably.
To convey this information to hospital staff and stakeholders, an explanatory visualization, such as a straightforward stacked bar chart, can effectively showcase peak wait times during the day, prompting discussions on resource allocation and process improvements.
Descriptive Statistics: The Backbone
Both exploratory and explanatory visualizations rely on descriptive statistics. Unlike predictive or inferential statistics, which data scientists use for forecasting and making inferences about populations, descriptive statistics solely focus on summarizing and visualizing patterns within the present dataset.
It's crucial to emphasize that while not everyone needs to become a data scientist, it is safe to assume that every business leader does need to become a business analyst of sorts. Becoming data savvy is essential for anyone looking to critically examine data, extract meaningful insights, and communicate them effectively. These skills empower individuals to navigate the data with which they work and make informed data-driven decisions.
Summary
Data visualization serves as a powerful tool for both exploration and explanation in various domains, including nonprofit work and healthcare administration. By harnessing the capabilities of descriptive statistics and visualizations, professionals can uncover hidden insights and communicate their findings effectively, ultimately driving data-driven decision-making across industries. Whether you are exploring new data or explaining your discoveries, data visualization is the key to unlocking the full potential of your data.
Roseanna Galindo, ECBA, CAVS is Principal at Periscope Business Process Analysis and a champion for data literacy, the human experience in healthcare, and leaders of volunteers everywhere. Learn more about Roseanna and her blog, The Periscope Insighter, by reading the opening post, Venn The Time Is Right
Roseanna is available for training, keynotes, and executive coaching. Visit PeriscopeBPA.com for more information.
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