What is Descriptive Analytics? Definition, How it works and Advantages

What Is Descriptive Analytics?

There are four distinct kinds of business analytics: diagnostic, descriptive, predictive, and prescriptive analytics. Each one asks a different question:

• Descriptive analytics. What happened?
• Diagnostic analytics. Why did this happen?
• Predictive analytics. Based on past data, what could happen?
• Prescriptive analytics. Taking the other three analytics together as an aggregate, what can we do about it?

For a more fleshed-out definition, we define descriptive analytics as the most common, fundamental form of business analytics used to monitor trends and keep track of operational performance  — by summarizing and highlighting patterns in past and existing data.

The practice of descriptive analytics produces business metrics, reports, and KPIs (Key Performance Indicators) to help businesses track their performance and different trends. As a result, companies understand what's happened thus far and, when combined with the other types of business analytics, get an idea of why things happened, what things may occur, and how to prepare for future events.

Here’s a descriptive analytics example — a very timely one in today’s digital world — social media engagement. Descriptive analytics provides metrics that help businesses figure out the return rate on different social media initiatives. These initiatives include engagement rates, numbers of followers, whether they’re growing or declining, and revenue generated via social media platforms.

Marketing professionals can use the descriptive analytics with social media engagement to decide which promotions work and which should be dropped. Social media metrics can also help businesses prioritize their social media outreach campaigns.

Other descriptive analytics examples include financial metrics that assess a business's health. This includes reports that show expenses and revenue, inventory and production logs, accounts receivable and payable records, cash flow, movement in the supply chain, internal and external surveys, and more. Yes, it's complex—hence—data analytics, in a descriptive way.

What Does Descriptive Analytics Tell Us?

Alright, so descriptive analytics gives businesses essential information about how it’s doing, where it’s going, and how it’s stacking up against the competition. But there’s much more to the story. So what does this tell companies and aspiring professionals in the field?

• The company’s current performance: Descriptive analytics helps businesses keep track of critical metrics involving individuals, groups and teams, and the company as a whole. For instance, descriptive analytics can show how a specific sales rep is doing this quarter or which of the rep’s products sells the most.
• The business’s historical trends: Descriptive analytics gathers information over long periods, and that accumulated information can be used to track the company's progress by comparing the metrics for different periods. For example, the corporate bean counters can track sales or expenses by comparing the results of various quarters, calculating revenue growth by percentages, and rendering the results on easy-to-read charts.
• The company’s strong and weak points: Descriptive analytics gives professionals the tools to compare the performances of various business groups using metrics like employee-generated revenue or expenses as a percentage of revenue. It will also compare these results with known industry averages or published results from other businesses. These comparisons help companies see where they’re doing well and where they need to improve. 

How Does Descriptive Analytics Work?

Descriptive analytics breaks down into five steps, including: 

1. State the Business Metrics

For starters, the business must identify the metrics that it wants to generate based on the essential business goals of each group within the company or the company's overall goals. For instance, a company emphasizing growth may emphasize measuring quarterly revenue increases. At the same time, the company's accounts receivable department might monitor great days' sales and other metrics that show how much time it takes to collect money from their customers.

2. Identify the Data Required

Next, the company must find the data needed to generate the desired metrics. This task is a potential challenge since the relevant data may be scattered across many files and applications. However, companies that employ an Enterprise Resource Planning (ERP) system may have an easier time because they will already have most or all the needed data in their systems' databases. Furthermore, some metrics may also need data from external sources, like e-commerce websites, industry benchmarking databases, or social media platforms.

3. Extract and Prepare the Data

Extracting, combining, and preparing the relevant data for analysis is potentially time-consuming if the needed analysis data originates from multiple sources. However, this is a crucial step to ensure accuracy. Furthermore, this may involve data cleansing to eliminate inconsistencies and mistakes in the data, a reasonable effort considering the information coming from an eclectic group of sources and rendering data into a suitable format for analysis tools. Advanced data analytics types use a process known as data modeling, a framework residing within information systems to help prepare, arrange, and organize the company's information. Data modeling defines and formats complex data, turning it into a usable, actionable resource.

4. Analyze the Data

Companies have various tools at their disposal to apply descriptive analytics, ranging from business intelligence (BI) software to spreadsheets such as ones found in Excel. Descriptive analytics usually involves using fundamental mathematical operations to one or more of the variables. For instance, a sales manager might like to monitor the average sales revenue or the monthly revenue from either established or recently acquired customers.

5. Present the Data

Once business analysts have gone through the necessary steps, all that's left is presenting the data. First, however, the information must be presented so that everyone can understand it, from stakeholders to finance specialists. Stakeholders usually appreciate seeing the report in compelling visual forms, like bar charts, pie charts, or line graphs. Visible data is easier to grasp. Finance specialists on the other hand, may want the information presented through numbers and tables.

What Are the Advantages of Descriptive Analytics?

Now, let’s look at the stand-out benefits of descriptive analytics.

• It’s easy to do: Descriptive analysis doesn’t require great expertise or experience in statistical methods or analytics.
• There are a lot of tools available: There is a cornucopia of analytics tools available to choose from, products that do most of the heavy lifting. Come to think of it, that helps explain why it’s easy to perform descriptive analytics!
• It answers the most common business performance questions: Most stakeholders and salespeople want to know things like "How are we doing?" or "What should we be doing differently?" Descriptive analytics provides the data needed to answer those questions efficiently, no matter when or how often they're asked.

But, like any other tool, descriptive analysis isn’t perfect. Here are the two chief drawbacks:

• It’s limited to simple analysis: Descriptive analysis examines the relationship between a handful of variables, and that’s all.
• It tells you what, but not why: Descriptive analysis reports events as they happened, not why they happened or what could possibly happen next.

Descriptive vs. Predictive vs. Prescriptive Analytics

We mentioned at the beginning of our story that there are several distinct types of business analytics. The following chart highlights the differences between descriptive, predictive, and prescriptive analytics.

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