What Is Descriptive Analytics?
Descriptive analytics is a type of data analytics process that helps businesses to understand what has happened in the past by looking at historical data trends. It involves comparing data from different time periods, like quarterly or yearly, or comparing with other companies in the same industry to see and analyze if their company is doing well or not.
For example, it is used to report financial details such as changes in prices over a year, sales growth each month, the number of customers gained or loose, and much more. These reports show what has happened in a business during a specific time period.
Descriptive Analytics – Key Takeaways
- Descriptive analytics examines past data to understand how a business has changed over time.
- This analysis highlights areas of strength and identifies weaknesses within an organization.
- Examples of this include tracking changes in prices over the years, monthly sales growth, the number of users, or total revenue per customer.
- Descriptive analytics is often combined with other types, such as predictive and prescriptive analytics, to offer a more comprehensive analysis.
How Does Descriptive Analytics Work?
Descriptive analytics basically involves looking at past data to understand how things have performed. It helps managers, investors, and other stakeholders to see what has happened before and allows them to compare it with different time periods.
Let us understand this more clearly with the help of an example- For example if a company reports $2 million in sales, this number alone does not tell the whole story. If this amount is a 10% drop from last month, it could be a matter of concern. But if it is a 20% increase compared to last month, it indicates good progress in sales or marketing.
Not only this but Descriptive analytics also helps you to compare your performance with other companies in the same industry. It can highlight your company’s strengths and weaknesses which will help managers for making better decisions.
Overall, descriptive analytics is a basic but important tool in business. It provides useful insights by looking at past data.
How Is Descriptive Analytics Used?
Descriptive analytics is a valuable tool that businesses use to understand their performance and spot any inefficiencies. It helps companies to see where they are doing well and where they need to improve. This information is useful for management, as it guides them in making changes to boost success.
There are two main ways to collect data for descriptive analytics, these two ways include: data aggregation and data mining. First, data is gathered and organized into manageable pieces. Then, management can use this information to see how the business is performing.
Steps Involved In Descriptive Analytics Process
To successfully use descriptive analytics in a business, companies can follow a few simple steps. Here is a step-by-step process written below which will help you to understand it in a better way:
- Choose the Right Metrics: The first step is to decide what information you want to analyze, like quarterly sales or yearly profits, and set a time frame for each.
- Find the Data: In the next step, gather all the data you need for specific information. This may involves looking through internal databases and external sources to find everything required.
- Organize the Data: After gathering the data from different sources, your next task is to make sure that it is accurate. For this, you have to go through it once and put it all into one format. This step ensures that the information is ready for analysis.
- Analyze the Data: After organizing the data into a meaningful order, your next task is to use various tools to examine the data and figures thoroughly to find insights.
- Share the Results: Finally, present the data to key people, like managers or investors, using simple visuals like charts or graphs. This will help everyone to understand the company’s progress and direction.
Advantages And Disadvantages Of Descriptive Analytics
Let us move further and understand the basic advantages and disadvantages of descriptive analytics. Going through the table below will help you to understand it better.
| Advantages | Disadvantages |
| It makes it easy to share information with everyone by using simple visuals like charts and graphs. | It only shows what happened in the past and does not predict future outcomes. |
| It helps major stakeholders to easily understand complex ideas and compare the company’s past and present performance. | It doesn’t help companies to see how future market changes, like supply change and demand shifts. |
| Companies can compare themselves with competitors in the same industry by looking at similar factors like costs and revenue. | One can influence which metrics are chosen. This can lead to a skewed view of the company’s success. |
| It highlights areas where a company might need improvement based on comparisons with others. | Stakeholders might ignore important data, creating a false sense of security about the company’s performance. |
Descriptive Analytics vs Predictive Analytics, Prescriptive Analytics, and Diagnostic Analytics
Descriptive analytics helps you to present important information in a way that is easy to understand. This type of analysis will always be needed to analyze past and present trends, but there is growing interest in newer types of analytics like predictive, prescriptive, and diagnostic analytics.
These newer forms of analytics are basically build on the base of descriptive analytics by just adding some extra features and data from different sources to predict likely future outcomes. They don’t just provide information but they also help in making decisions for the future. They can even suggest actions to improve results and avoid problems. Let us understand each of these types of analytics written above with the help of paragraphs written below.
Predictive Analytics Â
Predictive analytics basically aims to forecast future performance using statistics and modeling. By analyzing current and past data, it helps users to predict most certain outcomes that are likely to happen again. Companies using predictive analytics can spot inefficiencies and find better ways to use their resources, like supplies, labor, and equipment.
Prescriptive Analytics Â
Prescriptive analytics is generally used to help companies figure out what actions they need to take to achieve specific goals. It considers different situations, resources, and past performance to offer suggestions for the future. This type of analysis helps decision-makers to determine if they should invest more in research, continue a product, or enter a new market.
Diagnostic Analytics Â
Diagnostic analytics focuses on understanding the relationship between variables and why certain trends occur. It helps to answer the question, “Why did this happen?” Unlike other analytics, diagnostic analytics does not focus on past performance or future predictions. Instead of this, it is used to find the root cause of an event and make improvements for the future.
Descriptive Analytics Example
An example of descriptive analytics is when a company analyzes its sales data from the past year to understand its trends. For example, let us take an example of a retail store that wants to review its monthly sales figures to see which products sold the most during different seasons. By looking at this historical data, the store manager can identify patterns, such as higher sales of winter clothing in December and increased demand for swimsuits in June. This analysis will help the store owner to understand what happened in the past and can make decisions like inventory management and marketing strategies for the upcoming year.
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Descriptive Analytics FAQs
How does Descriptive Analytics differ from other types of analytics?
Descriptive Analytics focuses on summarizing past data, whereas Predictive Analytics forecasts future outcomes, and Prescriptive Analytics suggests actions that should be taken based on data.
What are some common examples of Descriptive Analytics?
Common examples of descriptive analytics include sales reports, customer satisfaction surveys, and website traffic analysis. These reports summarize past data to help businesses make informed decisions.
Why is Descriptive Analytics important?
Descriptive Analytics is important because it provides a clear view of past performance. This helps businesses to identify trends, measure success, and make data-driven decisions.
What tools are used in Descriptive Analytics?
Tools like Microsoft Excel, Tableau, and Google Analytics are commonly used for Descriptive Analytics. These tools help visualize and summarize data in an easy-to-understand format.
