Descriptive analytics: What is it, benefits, and examples

Most businesses collect mountains of data across sales, marketing, finance, and product systems, but effective business decisions require more than just raw information. Enter descriptive analytics.

As the foundation of data-driven decision-making, descriptive data analytics gives businesses a clear view of what’s already happened within their organization by summarizing findings into metrics, reports, dashboards, and more.

In this guide, we show you how important descriptive analytics is in today’s business landscape, alongside a few examples.

What is descriptive analytics?

Descriptive analytics is the practice of analyzing historical data to understand what happened within your business and how performance has changed over time. The process turns raw activity into organized summaries that help you visualize patterns, trends, and the outcomes of recent events.

There are four main types of analytics, each designed to answer a different question. These are:

• Descriptive analytics: deciphers what happened
• Diagnostic analytics: analyzes why something happened
• Predictive analytics: predicts what is likely to happen in the future
• Prescriptive analytics: suggests what to do about it

Descriptive analytics is the essential first step of any larger analysis. Without the foundation of knowing what is happening within your business, all other forms of analysis lose their footing.

How descriptive analytics works

The descriptive analytics process pulls data from various systems, like CRM platforms, ad tools, finance software, and product databases. Many teams automate the flow of information to ensure their website data is stored in one place, ready to be analyzed.

Next, analysts standardize the data so that metrics mean the same thing everywhere. They summarize it into counts, totals, averages, percentages, percentiles, and trends that reflect what happened. 

From here, the focus shifts to presentation. Analysts translate those summaries into tools, like charts, tables, scorecards, and dashboards that help you spot spikes, dips, seasonal differences, and outliers. 

Finally, analysts and marketing operations leaders review the reports to get a better understanding of performance. If the efforts are part of a wider analysis, this is the point at which the descriptive step stops and other analytical forms take over.

The 6 steps of descriptive analytics

While each company and analyst has their own descriptive analytics techniques, here are the six general steps of the process. 

Step 1: Data collection

Before pulling data, analysts identify the metrics that matter most, whether that’s revenue by channel, campaign conversion rates, customer retention, operational volume, or something else. 

Analysts then confirm that the existing data is accurate and complete. This means gathering information from systems that reliably capture the activity you want to analyze and verifying everything matches up across different tools. If the wrong metrics or unreliable sources enter the pipeline (perhaps via an insufficient data pipeline architecture), then every summary that follows will reflect those flaws. 

Step 2: Data cleaning

Analysts must clean and standardize data so that findings are as accurate as possible and everything is consistent in the final report. That means fixing irregular formatting, removing duplicates, handling missing values, and ensuring fields line up across sources. 

Step 3: Analysis, compilation, and summarization

Once the data is clean, analysts review everything to decipher what it actually shows. They typically use simple techniques like calculating averages, comparing totals, and reviewing charts and graphs to help reveal patterns, trends, and surprises. 

Next, analysts group the data and summarize it into easy-to-understand metrics. For example, they might combine daily transactions into weekly or monthly totals, or they could summarize individual customer actions into overall behavior trends. 

Step 4: Visualization

Data is more useful and understandable when presented in charts, graphics, comparisons, statistical plots, and dashboards. Visualization is particularly useful when dealing with stakeholders across a host of business functions, as it helps as many people as possible understand the data.

Step 5: Interpretation and contextualization

At this stage, analysts examine the data and craft a story that helps stakeholders see the business context of the findings. For instance, they might note which quarterly sales dips were seasonal and which were sudden.

Step 6: Reporting and repetitive analysis

Finally, analysts put everything together in a report or presentation. Regardless of whether stakeholders decide to carry out other forms of analysis, the descriptive process will usually repeat on a set cycle, often monthly or quarterly, so that decision-makers have up-to-date information whenever they need it.

Descriptive analytics benefits and drawbacks

The insights you can gather from descriptive analytics are powerful, but the process can still fall short. Here are some of the most notable benefits and drawbacks.

Descriptive analytics advantages

• Clarity: Descriptive analytics turns raw data into clear summaries and visuals that illuminate past performance, offering unbiased insight.
• Simplicity: Since this analysis method doesn’t require advanced statistical modeling or machine learning, almost anyone can grasp the results and act on them.
• Future insight potential: By identifying patterns and anomalies and highlighting areas that need further investigation, descriptive analytics lays the foundation for further analysis.
• Decision-making support: Having a reliable view of what already happened lets stakeholders make informed decisions based on real evidence, not guesswork. 
• Benchmarking capabilities: By consistently measuring key metrics, businesses can compare performance over time and against industry standards.

Descriptive analytics disadvantages

• Lack of predictive capabilities: While it offers a good starting point for predictive analysis, descriptive analytics only shows what happened, not what’s likely to happen in the future.
• Historical data dependence: Your findings are only as good as the data behind them. If records are incomplete, inaccurate, or biased, you’ll be making decisions based on skewed information.
• Reactive approach: Since it only focuses on past events, descriptive analytics makes it difficult to put in place any proactive strategies. 
• Inability to handle unstructured data: Descriptive analytics works best with structured, well-organized datasets that fit neatly into rows and columns. When you're dealing with unstructured data like emails, images, videos, or social-media posts, it can fall short.

Descriptive analytics examples across industries

From finance to healthcare, descriptive analytics is used by a host of different industries. Here are several real-world examples of the process in action. 

Marketing campaign performance

Marketers use descriptive analytics to track how campaigns performed across channels. Metrics like click-through rates, conversions, and engagement over time show which messages resonated and which fell flat.

Customer behavior analysis

To better understand customer habits and fine-tune product targeting and advertising efforts, retailers often analyze behavior like historical purchases and interactions. For example, buying pattern trends and loyalty program engagement offer insight into what drives repeat sales. 

Financial reporting

Finance professionals regularly turn to descriptive analytics to summarize expenses, revenue, cash flow, and more. They use this data to craft regularly timed reports that provide a clear picture of the company’s financial health.

Healthcare outcome tracking

To highlight potential areas for improvement, hospitals and clinics use descriptive analytics to monitor patient outcomes, treatment success rates, and operational efficiency. 

Website and product performance

Tech and ecommerce teams often analyze past site activity, product usage, and traffic trends. This guides user experience (UX) and product decisions by revealing which features are popular, what drives engagement, and whether any webpage features need improvement.

How Fivetran supports descriptive analytics

Descriptive analytics depends on clean, reliable, and centralized data, and that’s exactly what Fivetran delivers. 

By automating data pipelines from multiple sources, including CRM systems, finance tools, and marketing platforms, Fivetran removes the manual work of collecting, exporting, and combining information. This means your historical data is always up to date and ready to provide insights.

Fivetran also keeps your data clean and consistent, enabling comparisons across all sources. Once the data is organized and centralized, it flows smoothly into dashboards and business-intelligence tools, giving you instant access to intelligence that guides smarter decisions.

Learn how Fivetran transforms raw data into analytics-ready tables and your historical data into clear, actionable insights by requesting a demo today.

FAQs

What’s the difference between descriptive analytics and predictive analytics?

Descriptive analytics summarizes historical data to reveal what happened in the past. Predictive analytics uses those findings alongside additional context to predict what might happen in the future.

Why is descriptive analytics important?

By laying out trends, patterns, and results, descriptive analytics provides a clear summary of past events. It creates a solid foundation for informed decision-making and accurate planning. The process is also the first step in any more comprehensive analysis cycle.

How do I know if I need data analytics or business intelligence? 

Data analytics and business intelligence go hand-in-hand. Descriptive data analytics is an integral part of business intelligence, providing a view of what’s happened within your organization. 

However, it doesn’t usually encompass more complex forms of analytics, such as descriptive, predictive, and prescriptive analytics. These processes explain why things are happening, what you can do about them, and how you can improve.

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