What is Descriptive Analytics? A Definition

Business Intelligence (BI) tools and data science can seem like magic at times, uncovering hidden trends from reams of raw data and empowering business leaders like you to make the right decisions and achieve key initiatives, even when resources are limited.

Descriptive analytics forms the foundation of business intelligence, as we’ll break down in this article. It provides business leaders with answers to the questions surfaced by crude data and helps them make decisions to guide their business in the right direction.

In fact, our recent Martech Research survey (methodology below) found that 72% of respondents considered their analytics tools to be either moderately (46%) or very (26%) effective at helping them achieve company goals.

But BI tools can also feel arcane to new and less experienced users, with complex interfaces and a seemingly endless stream of new terms and acronyms.

Business analytics are only useful if users can understand them, and descriptive analytics is the bridge between impenetrable, raw data and actionable insight.

Let’s take a closer look at descriptive analytics, starting with a definition.

Descriptive analytics is a type of data analysis that helps business leaders distill raw data into an actionable and easy-to-understand format that answers the question, “What happened?”

In fact, Gartner defines descriptive analytics as “the examination of data or content … 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.”

Descriptive analytics is one of the four main categories of data analytics, along with diagnostic analytics, predictive analytics, and prescriptive analytics.

Data analytics allows businesses to examine large volumes of data methodically, and descriptive analytics, in particular, helps business leaders gain clarity into the historic performance of their business.

It provides the foundation for all business analytics.

For example, if a produce supplier had a decline in revenue from one quarter to the next, descriptive analytics might help pinpoint the source of that decline as a drop in orders of tomatoes in a specific market among a demographic during a week. In comparison:

• Diagnostic analytics helps understand why this happened: A drought caused less flavorful tomatoes.

• Predictive analytics helps determine what will happen: An ongoing water shortage will lead to more low-quality tomatoes.

• Prescriptive analytics helps navigate what the produce supplier should do: Research a new variation of tomatoes that requires less water.

Once this information has been uncovered using data analytics, the next step is to summarize and distribute key findings. Data visualization helps users package and present those key findings in an easily understandable format so that they can be shared with stakeholders.

As you can imagine, each of these types of data analytics can be greatly beneficial to businesses, but they all start with descriptive analytics.

Ready to get started with data analytics at your business? Here’s a short video to help you get started on the right foot:

While software can take a lot of the legwork out of data analysis, it is possible to run descriptive analytics using just a spreadsheet. (Note that you’ll need the Analysis ToolPak add-in for Excel to perform more complex analytics.)

If your business is relatively small, your data set is mostly contained to one or two sources, or you don’t have the resources to invest in a business intelligence platform, you might be a good candidate for performing descriptive analytics with a spreadsheet.

Once you have your chosen data set in your spreadsheet (for example, monthly sales data), you can use the descriptive statistics function to generate basic statistics like total sales, average revenue per sale, and standard deviation.

Here are a few examples of businesses that might benefit from basic descriptive analytics using only a spreadsheet:

• A landscaping company trying to determine average revenue per job

• A florist trying to determine year-over-year customer growth

• A mechanic trying to determine the total number of jobs completed per month

Want to see it in action? Check out a tutorial showing how to pull some simple descriptive statistics in Microsoft Excel:

Descriptive statistics in Excel (Source)

Unless you’re a data analyst or data scientist (or you have one on staff, in which case, you’re ahead of the game), you’re probably wondering where to even start with your data analysis.

Descriptive analytics should be your first stop when running data analytics because descriptive analytics (“What happened?”) informs the other three types of analytics.

This can be both an advantage and a drawback. On one hand, descriptive analytics provides the foundation for all business intelligence. But on the other hand, descriptive analytics only helps reveal what happened without directly revealing why it happened, what might happen next, or what you should do.

For this reason, descriptive analytics should never be the end of the story. It should always lead into diagnostic analytics, predictive analytics, and/or prescriptive analytics.

Descriptive analytics is also most useful when performed regularly. For example, you can’t generate month-over-month sales data if you only run analytics a few times per year when you notice a dip or a spike in revenue. This is where BI dashboards that automatically keep track of historic data can be especially helpful.Rather than wrestling with financial reports and pouring through spreadsheets of historical data to perform a descriptive analysis manually, it is highly recommended that business leaders leverage a BI tool to streamline this process and save time.

Here is a basic four-step process for using descriptive analytics to gain valuable insight into your business performance:

1. Select a BI tool that integrates with your existing software. Our BI software directory allows users to filter by integrations with hundreds of different platforms. Your accounting software may also have a marketplace of BI apps to browse such as QuickBooks.

2. Determine which metrics you want to investigate. Examples include revenue, new customers, and social media engagement. Refer to your business goals, and where you’re falling short as a starting point.

3. Pull and prepare relevant data. Depending on the type of metric you’re investigating, this data set could come from your accounting and finance software, your marketing software, or your ERP software.

4. Use your BI tool to perform a descriptive analysis. This process will differ depending on the specific tool, but all tools should allow users to prepare reports and charts to present to stakeholders and save dashboards to run again in the future.

Now that you are familiar with the definition of descriptive analytics, its applications, and tips for scaling to your business needs, you might be ready to explore more BI tools and resources. GetApp has you covered.Check out these related articles from our resource library:

• GetApp's Top-Rated Analytics and Data Software Tools for the Future of Work in 2022

• Future of Business Analytics: 5 Predictions for 2025 and Beyond

• 5 Advantages of Self-Service Business Intelligence

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