When presented with new data, the first step a data analyst must take is always to understand what story it’s trying to tell. Data analytics is a complex beast, however, involving many different tools and analytical approaches. So which one should you use?
If you’re new to data and want to learn the basics, descriptive analytics is a good place to start. But what exactly is descriptive analytics, and how does it work? In this post, we’ll dive deep on the topic, answering all your questions, including:
- How does descriptive analytics work?
- How is descriptive analytics used?
- Advantages of descriptive analytics
- Disadvantages of descriptive analytics
- Descriptive analytics use cases
- Key takeaways
Ready to get the low-down on descriptive analytics? Let’s dive in.
1. How does descriptive analytics work?
Of all data analytics techniques, descriptive analytics is perhaps the most straightforward. It involves parsing (or breaking down) data and summarizing its main features and characteristics. In this way, descriptive analytics presents what has happened in the past without exploring why or how.
Because it is merely explanatory, descriptive analytics uses basic descriptive statistics. This includes measures of distribution (frequency or count), central tendency (mean, mode, and median), and variability (such as variance and standard deviation). Where relevant, it also measures the position of various data points, including the interquartile or percentile range.
Descriptive analytics often presents its findings using reports, pivot tables, and visualizations like histograms, line graphs, pie charts, and box and whisker plots. We won’t explore these further here, but you can learn more about descriptive statistics in this post.
2. How is descriptive analytics used?
Data analysts can use descriptive statistics to summarize more or less any type of data, although it helps to think of it as the first step in a more protracted process. That’s because while descriptive statistics may describe trends or patterns, it won’t dig deeper. For this, we need tools like diagnostic and predictive analytics. Nevertheless, descriptive analytics is exceptionally useful for introducing yourself to unknown data.
The following kinds of data can all be summarized using descriptive analytics:
- Financial statements
- Social media engagement
- Website traffic
- Scientific findings
- Weather reports
- Traffic data
The list goes on! Essentially, any data set can be summarized in one way or another, meaning descriptive analytics has an almost endless number of applications. We’ll explore these in more depth in section five. First, let’s look at some of the benefits and drawbacks of descriptive analytics.
3. Advantages of descriptive analytics
Although relatively simplistic as analytical approaches go, descriptive analytics nevertheless has many advantages. Descriptive analytics:
- Presents otherwise complex data in an easily digestible format.
- Provides a direct measure of the incidence of key data points.
- Is inexpensive and only requires basic mathematical skills to carry out.
- Is faster to carry out, especially with help from tools like Python or MS Excel.
- Relies on data that organizations already have access to, meaning there’s no need to source additional data.
- Looks at a complete population (rather than data sampling), making it considerably more accurate than inferential statistics.
But, of course, being so straightforward means descriptive analytics also has its limitations. Let’s explore some of these next.
4. Disadvantages of descriptive analytics
Okay, we’ve looked at the strengths of descriptive analytics—but where does it fall short? Some disadvantages of descriptive analytics include:
- You can summarize data sets you have access to, but these may not tell a complete story.
- You cannot use descriptive analytics to test a hypothesis or understand why data present the way they do.
- You cannot use descriptive analytics to predict what may happen in the future.
- You cannot generalize your findings to a broader population.
- Descriptive analytics tells you nothing about the data collection methodology, meaning the data set may include errors.
As you may suspect, although descriptive analytics are useful, it’s important not to overstretch their capabilities. Fortunately, we have diagnostic and predictive analytics to help fill in the gaps where descriptive analytics falls short.
5. Descriptive analytics use cases
Now we’ve covered the theory around descriptive analytics, how can it be used in the real world? While descriptive analytics only focuses on what has happened, not why, it remains a valuable first step in the broader data analytics process. Let’s take a look.
Tracking social media engagement
Social media is a key touchpoint along the sales journey. The ability to measure and present engagement metrics across a complex constellation of campaigns and social networks is, therefore, vital for determining the most successful approaches to digital marketing. Fortunately, marketing reports on social media engagement will include descriptive analytics by default. Clicks, likes, shares, detail expands, bounce rates, and so on are all measures of social media engagement that can be easily summarized using descriptive techniques.
For instance, perhaps a company is interested in knowing which social media account is driving the most traffic to their website. Using descriptive statistics, visualizations, and dashboards, they can easily compare information about different channels. Similarly, marketing teams can look at specific shareable content, perhaps comparing videos with blog posts, to see which results in the most clicks.
While none of this information draws direct conclusions (in that it doesn’t measure cause and effect) it’s still valuable. It helps teams to devise hypotheses or make informed guesses about where to invest their time and budget.
Streaming and e-commerce
Subscription streaming services like Spotify and Netflix, and e-commerce sites like Amazon and eBay all use descriptive analytics to identify trends. Descriptive measures help determine what’s currently most popular with users and buyers. Spotify, for example, uses descriptive analytics to learn which albums or artists subscribers are listening to. Meanwhile, Amazon uses descriptive analytics to compare customer purchases. In both cases, these insights inform their recommendation engines.
Netflix, meanwhile, takes this use of descriptive analytics even further. A highly data-driven company, Netflix uses descriptive analytics to see what genres and TV shows interest their subscribers most. These insights inform decision-making in areas from new content creation to marketing campaigns, and even which production companies they work with.
Learning management systems
From traditional education to corporate training, many organizations and schools now use online/offline hybrid learning. Learning management systems (or LMSs for those in the know!) are a ubiquitous part of this. LMS platforms track everything from user participation and attendance to test scores, and—in the case of e-learning courses—even how long it takes learners to complete. Summarizing this information, descriptive-analytical reports offer a high-level overview of what’s working and what’s not.
Using these data, teachers and training providers can track both individual and organization-level targets. They can analyze grade curves, or see which teaching resources are most popular. And while they won’t necessarily know why, it may be possible to infer from the data that videos, for example, are more popular than, say, written documents. Presenting this information is the first step towards improving course design and creating better learner outcomes.
6. Key takeaways
This post has offered a full introduction to descriptive analytics. We’ve learned that:
- Descriptive analytics is the simplest form of data analysis, and involves summarizing a data set’s main features and characteristics.
- Descriptive analytics relies on statistical measures of distribution, central tendency, and variability.
- It provides an overview of varied data types, from financial statements to surveys, website traffic, and scientific data.
- A key advantage of descriptive analytics is that it requires only basic math skills and allows you to present otherwise complex data in an easily digestible format.
- The main disadvantage of descriptive analytics is that it only summarizes data; it doesn’t draw conclusions or test hypotheses.
- We can use descriptive analytics to measure things like social media engagement, content curation, and learner outcomes.
To learn more about data analytics, or to try some test exercises, why not sign up for this free, 5-day data analytics short course? You can also supplement your knowledge with the following introductory topics:
- Standard Error vs. Standard Deviation: What’s the Difference?
- What Is Data Visualization and Why Is It Important? A Complete Introduction
- The 7 Most Useful Data Analysis Methods and Techniques
Descriptive analytics is a type of data analytics that looks at past data to give an account of what has happened. Results are typically presented in reports, dashboards, bar charts and other visualizations that are easily understood.What is descriptive in analytics? ›
Descriptive analytics is a type of data analytics that looks at past data to give an account of what has happened. Results are typically presented in reports, dashboards, bar charts and other visualizations that are easily understood.What does descriptive analytics answer? ›
Descriptive Analytics is the examination of data or content, usually manually performed, to answer the question “What happened?” (or What is happening?), characterized by traditional business intelligence (BI) and visualizations such as pie charts, bar charts, line graphs, tables, or generated narratives.What is descriptive analytics quizlet? ›
Descriptive Analytics. It is a preliminary stage of data processing that creates a summary of historical data to yield useful information and possibly prepare data for further analysis.Does descriptive analytics answers the question what has happened? ›
The simplest way to define descriptive analytics is that it answers the question “What has happened?”. This type of analytics analyses the data coming in real-time and historical data for insights on how to approach the future.What type of analytics is descriptive? ›
Descriptive analytics is the analysis of historical data using two key methods – data aggregation and data mining - which are used to uncover trends and patterns.How to do a descriptive analysis? ›
- Step 1: Describe the size of your sample. Use N to know how many observations are in your sample. ...
- Step 2: Describe the center of your data. ...
- Step 3: Describe the spread of your data. ...
- Step 4: Assess the shape and spread of your data distribution. ...
- Compare data from different groups.
A descriptive analysis is an important first step for conducting statistical analyses. It gives you an idea of the distribution of your data, helps you detect outliers and typos, and enable you identify associations among variables, thus making you ready to conduct further statistical analyses.What does descriptive analytics focus on __________________? ›
Descriptive analytics is focused only on what has already happened in a business and, unlike other methods of analysis, it is not used to draw inferences or predictions from its findings.Which of the following are examples of descriptive analytics? ›
- Tracking course enrollments, course compliance rates,
- Recording which learning resources are accessed and how often.
- Summarizing the number of times a learner posts in a discussion board.
- Tracking assignment and assessment grades.
- Comparing pre-test and post-test assessments.
The first stage of business analytics is descriptive analytics, which still accounts for the majority of all business analytics today. Descriptive analytics looks at past performance and understands that performance by mining historical data to look for the reasons behind past success or failure.What is a descriptive method quizlet? ›
descriptive research methods. scientific procedures that involve systematically observing behavior in order to describe the relationship among behaviors and events. naturalistic observation. the systematic observation and recording of behaviors as they occur in their natural setting.What questions do descriptive statistics answer? ›
- What is the percentage of X, Y, and Z participants?
- How long have X, Y, and Z participants been in a certain group/category?
- What are, or describe, the factors of X?
- What is the average of variable Y?
If you want a good example of descriptive statistics, look no further than a student's grade point average (GPA). A GPA gathers the data points created through a large selection of grades, classes, and exams then average them together and presents a general idea of the student's mean academic performance.What are the parts of descriptive analytics? ›
- Measures of Frequency: * Count, Percent, Frequency. * Shows how often something occurs. ...
- Measures of Central Tendency. * Mean, Median, and Mode. ...
- Measures of Dispersion or Variation. * Range, Variance, Standard Deviation. ...
- Measures of Position.
Primarily, a descriptive research question will be used to quantify a single variable, but there's nothing stopping you covering multiple variables within a single question. How often do you buy mobile apps for fitness purposes? How much would you be willing to pay for a men's lifestyle magazine?What is descriptive analysis types and advantages? ›
Descriptive analytics helps everyone in the company make more-informed decisions that guide the business in the right direction. It reveals patterns that might otherwise be hidden in raw data, enabling managers to see at a glance how well the business is performing and where improvements may be needed.What are the tools used in descriptive analysis? ›
They include both numerical (e.g. central tendency measures such as mean, mode, median or measures of variability) and graphical tools (e.g. histogram, box plot, scatter plot…) which give a summary of the dataset and extract important information such as central tendencies and variability.What is the difference between descriptive and analytics? ›
Academia recognizes two major types of writing—descriptive writing and analytical writing—which are both used in non-academic situations as well. As you might expect, descriptive writing focuses on clear descriptions of facts or things that have happened, while analytical writing provides additional analysis.What are the 4 types of analytics? ›
Modern analytics tend to fall in four distinct categories: descriptive, diagnostic, predictive, and prescriptive.
Descriptive, or qualitative, methods include the case study, naturalistic observation, surveys, archival research, longitudinal research, and cross-sectional research.What is descriptive method also known as? ›
Descriptive research is often referred to as "hypothesis generating research." Depending on the data collection method used, descriptive studies can generate rich datasets on large and diverse samples.What is the descriptive method based on? ›
Descriptive research methods can include surveys, observational studies, and case studies, and the data collected can be qualitative or quantitative. The findings from descriptive research provide valuable insights and inform future research, but do not establish cause-and-effect relationships.What is descriptive statistics in simple words? ›
Descriptive statistics are brief informational coefficients that summarize a given data set, which can be either a representation of the entire population or a sample of a population. Descriptive statistics are broken down into measures of central tendency and measures of variability (spread).What are the three basic descriptive statistics? ›
What are the 3 main types of descriptive statistics? The 3 main types of descriptive statistics concern the frequency distribution, central tendency, and variability of a dataset.Why is descriptive statistics easy? ›
Descriptive statistics involves summarizing and organizing the data so they can be easily understood. Descriptive statistics, unlike inferential statistics, seeks to describe the data, but does not attempt to make inferences from the sample to the whole population.What is descriptive analytics and example? ›
Descriptive analytics can help to identify the areas of strength and weakness in an organization. Examples of metrics used in descriptive analytics include year-over-year pricing changes, month-over-month sales growth, the number of users, or the total revenue per subscriber.What is descriptive statistics in data analytics? ›
Descriptive statistics refers to a set of methods used to summarize and describe the main features of a dataset, such as its central tendency, variability, and distribution. These methods provide an overview of the data and help identify patterns and relationships.What is an example of descriptive? ›
She gave a descriptive account of the journey. a talent for descriptive writing a poem full of descriptive detail The black cat was given the descriptive name “Midnight.” The book is a descriptive grammar.
As compared to descriptive studies which merely describe one or more variables in a sample (or occasionally population), analytical studies attempt to quantify a relationship or association between two variables – an exposure and an outcome.
A descriptive analysis is an important first step for conducting statistical analyses. It gives you an idea of the distribution of your data, helps you detect outliers and typos, and enable you identify associations among variables, thus making you ready to conduct further statistical analyses.What is an example of descriptive analytics in healthcare? ›
In direct healthcare practice, for example, descriptive analytics can be used to determine how contagious a virus is, by examining the rate of positive tests in a specific population over time.Does descriptive statistics use mean? ›
Descriptive statistics is a statistical measure that describes data through certain numbers like mean, median, mode, etc. so as to make it easier to understand and interpret.Which of the following is an example of descriptive statistics? ›
The correct answer is the standard deviation. A descriptive statistic (in the count noun sense) is a summary statistic that quantitatively describes or summarizes features from a collection of information.What is an example of a descriptive statistic in business? ›
Examples of descriptive analytics include KPIs such as year-on-year percentage sales growth, revenue per customer and the average time customers take to pay bills. The products of descriptive analytics appear in financial statements, other reports, dashboards and presentations.