![]() ![]() Fortunately, marketing reports on social media engagement will include descriptive analytics by default. 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. Social media is a key touchpoint along the sales journey. 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. Fortunately, we have diagnostic and predictive analytics to help fill in the gaps where descriptive analytics falls short. Descriptive analytics tells you nothing about the data collection methodology, meaning the data set may include errors.Īs you may suspect, although descriptive analytics are useful, it’s important not to overstretch their capabilities.You cannot generalize your findings to a broader population.You cannot use descriptive analytics to predict what may happen in the future.You cannot use descriptive analytics to test a hypothesis or understand why data present the way they do.You can summarize data sets you have access to, but these may not tell a complete story.Okay, we’ve looked at the strengths of descriptive analytics-but where does it fall short? Some disadvantages of descriptive analytics include: īut, of course, being so straightforward means descriptive analytics also has its limitations. Looks at a complete population (rather than data sampling), making it considerably more accurate than inferential statistics.Relies on data that organizations already have access to, meaning there’s no need to source additional data.Is faster to carry out, especially with help from tools like Python or MS Excel.Is inexpensive and only requires basic mathematical skills to carry out.Provides a direct measure of the incidence of key data points.Presents otherwise complex data in an easily digestible format.Advantages of descriptive analyticsĪlthough relatively simplistic as analytical approaches go, descriptive analytics nevertheless has many advantages. First, let’s look at some of the benefits and drawbacks of descriptive analytics. We’ll explore these in more depth in section five. 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. The following kinds of data can all be summarized using descriptive analytics: Nevertheless, descriptive analytics is exceptionally useful for introducing yourself to unknown data. ![]() For this, we need tools like diagnostic and predictive analytics. That’s because while descriptive statistics may describe trends or patterns, it won’t dig deeper. How is descriptive analytics used?ĭata 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. We won’t explore these further here, but you can learn more about descriptive statistics in this post. Where relevant, it also measures the position of various data points, including the interquartile or percentile range.ĭescriptive analytics often presents its findings using reports, pivot tables, and visualizations like histograms, line graphs, pie charts, and box and whisker plots. This includes measures of distribution (frequency or count), central tendency (mean, mode, and median), and variability (such as variance and standard deviation). In this way, descriptive analytics presents what has happened in the past without exploring why or how.īecause it is merely explanatory, descriptive analytics uses basic descriptive statistics. It involves parsing (or breaking down) data and summarizing its main features and characteristics. Of all data analytics techniques, descriptive analytics is perhaps the most straightforward. Ready to get the low-down on descriptive analytics? Let’s dive in. 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: If you’re new to data and want to learn the basics, descriptive analytics is a good place to start. Data analytics is a complex beast, however, involving many different tools and analytical approaches. When presented with new data, the first step a data analyst must take is always to understand what story it’s trying to tell. ![]()
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