Descriptive analytics helps to identify important patterns and trends in large datasets. In comparison to all other methods of data analysis, descriptive is the most used one. The main task of descriptive analytics is to create metrics and key performance indicators for use in dashboards and business reports.
In this article, we will get to know What is Descriptive Analytics, How it works, Its advantages, disadvantages, and applications.
Table of Content
What is Descriptive Analytics?
Descriptive analytics uses statistical summaries and data visualization techniques to condense and describe historical data. It helps identify patterns, trends, and relationships within the data, clearly showing “what happened” and “what is currently happening.” Think of it as the foundation for further analysis – it sets the stage for understanding past performance and current trends.
Data analytics can be divided into four key types:
- Descriptive Analytics: What happened? (Summarizes past and current data)
- Diagnostic Analytics: Why did this happen? (Drills down to identify causes)
- Predictive Analytics: What might happen in the future? (Uses trends to forecast future events)
- Prescriptive Analytics: What should we do next? (Recommends actions based on predictions)
Let’s look at the Example of Descriptive Analytics:
Imagine a hospital analyzing patient records. They might calculate average wait times in the emergency room, categorize the most frequent diagnoses, or track year-over-year trends in admissions. By summarizing data and using visuals like bar charts or line graphs, they can identify patterns and understand what’s happening within the hospital. This descriptive analysis allows them to focus on areas for improvement, like reducing wait times or allocating resources based on patient needs.
What Information Do Descriptive Analytics Provide?
Descriptive analytics provides insights into what has happened and what is currently happening, summarizing data with statistics and visualizations.
How does Descriptive Analytics work?
Now we know what is Descriptive Analytics, let’s have a closer look on the working of Descriptive Analytics. A type of descriptive analytics involves analyzing and simplifying historical data to provide insights into previous events, trends, and patterns. It is much closer to reporting than to what most people think of as analytics.
Working of Descriptive analyis
Steps for Descriptive Analytics Work:
- Data Collection: Collecting useful information is the initial stage in the descriptive analytics process. By using multiple resources such as databases, spreadsheets, and other data repositories. All of these provide this data. Since they directly affect how accurate the descriptive analytics is, the accuracy and standard of the data are extremely important.
- Cleaning the Data and Preprocessing: The obtained data usually needs to be cleaned and preprocessed before analysis can start. This includes converting data into a uniform structure, standardizing formats, and handling missing or incorrect values. Clean and well-preprocessed data ensures that the subsequent analytics is reliable.
- Data analysis: It provides an understanding of the structure and features of the dataset. Here EDA (exploratory data analysis) methods helps to find the patterns, trends, and possible outliers in the data. These methods include making histograms, scatter plots, and summary statistics.
- Compilation and Summary: The goal of descriptive analytics is to offer an overview of the data at a high level. To get important metrics and statistics, such as mean, median, mode, range, and standard deviation, this frequently requires combining the data.
- Visualization: In descriptive analytics, visualizations are extremely useful tools. It helps us to communicate complex information with a variety of charts, graphs, and other visual representations are employed. Data patterns and trends can be highlighted with the use of visualization, which also makes it easier to convey insights to a wide range of audiences.
- Fiction Creation: Descriptive analytics can include the creation of descriptions that offer a logical and contextualized explanation of the data, in addition to visuals. When communicating findings to those in the audience who might not be familiar with the complexities of the data, this can be especially helpful.
- Interpretation: To obtain significant knowledge, analysts interpret the outcomes of descriptive analytics. This involves knowing the effects of the trends and patterns seen in the data. While interpretation provides the foundation for more in-depth analyses that investigate “why” and “what might happen in the future,” descriptive analytics concentrates on the “what happened” topic.
- Testing Actively: The process of descriptive analytics is not one-time. Organizations continually repeat the descriptive analytics when new data becomes available in order to keep informed about the latest developments and patterns. This way, people making decisions get the newest information.
Advantages of Descriptive Analytics
We have explored the Descriptive Analytics meaning, its working and example. Descriptive analytics helps organizational workflows by making difficult concepts easy for everyone and simplifying the distribution of information. Here are some advantages to descriptive analytics:
- Data-driven decision making: It provides well-informed decision-making based on facts rather than gut instincts by evaluating and simplifying data.
- Presents data clearly: Descriptive analytics simplifies complex data, making it easy to understand through reports and visualizations like charts and graphs.
- Convenient to Realize: Data that has been summarized and graphically represented is easier to clarify and evaluate for a larger audience.
- Identifies Relevant Data Points: It offers straightforward metrics that give an accurate estimation of important data points.
- Simple and cost-effective: Descriptive analytics is simple to use and just requires basic arithmetic knowledge for execution.
- Efficient with tools: With the aid of tools like Python or MS Excel, which make things fast and easy.
Disadvantages of Descriptive Analytics
Descriptive analysis has disadvantages as well as limitations, but it provides insightful information about historical data. Understanding these drawbacks is essential for organizations to make informed decisions about the appropriate use of descriptive analytics. Here are some of the key disadvantages:
- Inability of Cause Analysis: The main goal of descriptive analytics is to explain historical events. It doesn’t explore the root causes or reasons for the patterns that are seen.
- Analysis Simplicity: The reach of descriptive analytics is restricted to basic analyses that look at the relationships between a small number of variables.
- Doesn’t Explain Why: History offers lessons for future generations, by offering facts, but causes and predictions are not provided to the readers.
- Inappropriate for Making Decisions in Real Time: Normally, descriptive analytics involves getting summary information at intervals intervals and this might not be the best option for decision- making when the time matter. In many situations, fast responsiveness is vital, therefore, sometimes only relying on the descriptive analytics might drag you behind.
- Lack of ability to handle unstructured data: Structured and well-organized datasets are better suited for descriptive analytics. while analyzing semi-structured or unstructured data, such as text, photos, or multimedia, it could make challenging to offer insightful analysis.
Applications of Descriptive Analytics
The applications of descriptive analysis may be discovered in many different fields and sectors, and it offers insightful information about past data. These examples show how useful descriptive analysis is, showing how it can be used in a variety of contexts to make data-driven decisions, enhance workflows, and extract insightful information from historical data. The following are some important applications:
- Financial Performance Evaluation: For instance, in the past; descriptive analytics was often used to appraise and assess a specific firm’s previous performances. Lots of organizations can detect trends, patterns and possibilities for a change by tracking key performance indicators (KPI’s) at different periods of time. This awareness helps in the construction and building of business operations with all the required strategic planning.
- Marketing and Analysis of Customer Behavior: However, Companies should analyze and understand the customers’ behavior. Firms need descriptive analytics to weight historical data on consumer interactions, purchasing patterns, and preferences.
- Friction Analysis in Business Processes: Descriptive analytics is applied descriptive approaches in business learning and development, and to detect and reduce friction in business processes. All the blockades or imparing of efficiency restraining processes from moving will be called friction. Organizations can easily pinpoint the bottlenecks of their business processes by looking at historical data over workflow delays using of resources and process’s time.
- Social Networking Analytics: In order to analyze user involvement, content performance, and audience demographics, descriptive analytics is used in social media. It assists businesses in customizing their social media plans according on past performance.
- Crime and Fraud Detection: Pattern in previous crime data is investigated by law enforcement and security agencies in order to do descriptive analysis which is one of the types of analytics. It is applied by financial organizations to make discoveries of market fluctuations and anomalies that can prevent or can be used to fight them.
- Crypto Market Analysis: Cryptocurrency markets are a great source of information for investors, as historical price data, market volumes aggregates, and market trends can be used to analyze the behavior of Bitcoin traders. These algorithms, mood patterns in the market, and possible factors may affect the price fluctuation of Bitcoin can all been fancy with the help of a descriptive analytics.
- Human Resources Management: HR uses descriptive analytics to analyze their staff. It aids businesses in the analysis of previous information on worker performance, turnover rates, training effectiveness, and other HR indicators.
- Risk Assessment and Management: To identify and analyze historical risk factors, descriptive analytics is used in risk assessment. Organizations need to know this information. This information is really important for companies in areas like banking and insurance to create plans that help reduce and handle risks better.
Descriptive vs. Predictive vs. Prescriptive Analytics
Difference between Descriptive vs. Predictive vs. Prescriptive Analytics can be described as follows:
| Feature | Descriptive Analytics | Predictive Analytics | Prescriptive Analytics |
|---|---|---|---|
| Purpose | Understand what happened in the past. | Forecast what might happen in the future. | Recommend actions to achieve desired outcomes. |
| Focus | Historical data analysis. | Future trends and patterns. | Decision-making and optimization. |
| Time Frame | Past events and trends. | Future events and probabilities. | Future actions and recommendations. |
| Examples | Summarizing sales data from the previous month. | Predicting future sales based on market trends and historical data. | Recommending product pricing strategies to maximize profits. |
| Tools | Reporting tools, dashboards, data visualization. | Statistical models, machine learning algorithms. | Optimization algorithms, decision support systems. |
| Key Metrics | Descriptive statistics: mean, median, mode, etc. | Predictive accuracy metrics: RMSE, MAE, etc. | Prescriptive performance metrics: ROI, cost-benefit analysis, etc. |
| Decision Support | Provides insights for informed decision-making. | Guides future actions and strategies. | Offers actionable recommendations to achieve specific goals. |
| Example Application | Analyzing website traffic to understand user behavior. | Predicting customer churn to anticipate and prevent losses. | Suggesting personalized marketing campaigns based on customer segmentation. |
| Objective | Historical understanding and trend analysis. | Future prediction and risk assessment. | Optimal decision-making and performance improvement. |
| Impact | Historical insights for strategy refinement. | Anticipating future scenarios for proactive decision-making. | Maximizing outcomes and efficiency through informed actions. |
| Data Requirements | Historical data sets. | Historical data sets, future predictors. | Historical data sets, future predictors, decision variables. |
Conclusion
In conclusion, the core concept of data analytics is descriptive analytics, which gives businesses valuable information about their past data. Through a variety of techniques like data aggregation, visualization, and exploratory data analytics, organizations may identify patterns, trends, and abnormalities that provide valuable insights for decision-making in a range of sectors. Descriptive analytics influence will grow as technology develops and is combined with other advanced analytics approaches to give businesses an integrated approach for deriving meaningful insights from their data.
FAQs – Descriptive Analytics
What is an example of descriptive data analysis?
Imagine analyzing customer survey data. You might calculate the average rating for a product, the most frequent reasons for choosing your company, or the breakdown of customer demographics. This helps understand your customer base.
What is the difference between descriptive and predictive analytics?
Descriptive analytics focuses on summarizing what happened in the past (e.g., average sales). Predictive analytics uses historical data to forecast future trends (e.g., predict future sales based on past patterns).
What is the descriptive statistics analysis?
It’s a technique within descriptive analysis that uses statistics like mean, median, and mode to summarize a dataset. It helps understand the data’s central tendency and spread.
What is the purpose of a descriptive analysis?
Its purpose is to get a basic understanding of your data. It helps you see patterns, central tendencies, and outliers, providing a foundation for further analysis.
What are the advantages of descriptive analysis?
- Easy to understand: Summarizes data into clear metrics.
- Foundation for further analysis: Provides a springboard for more complex methods.
- Identifies trends and patterns: Helps uncover initial insights from your data.