- Business Analytics - Home
- Business Analytics Basics
- Business Analytics - What It Is?
- Business Analytics - History and Evolution
- Business Analytics - Key Concepts and Terminologies
- Business Analytics - Types of Data
- Business Analytics - Data Collection Methods
- Different Tools used for Data Cleaning
- Business Analytics - Data Cleaning Process
- Different Sources of Data for Data Analysis
- Business Analytics - Data Cleaning
- Business Analytics - Data Quality
- Descriptive Analytics
- Descriptive Analytics - Introduction
- How Does Descriptive Analytics Work?
- Descriptive Analytics - Challenges and Future in Data Analysis
- Descriptive Analytics Process
- Descriptive Analytics - Advantages and Disadvantages
- Descriptive Analytics - Applications
- Descriptive Analytics - Tools
- Descriptive Analytics - Data Visualization
- Descriptive Analytics - Importance of Data Visualization
- Descriptive Analytics - Data Visualization Techniques
- Descriptive Analytics - Data Visualization Tools
- Predictive Analytics
- Predictive Analytics - Introduction
- Statistical Methods & Machine Learning Techniques
- Prescriptive Analytics
- Prescriptive Analytics - Introduction
- Prescriptive Analytics - Optimization Techniques
Descriptive Analytics - Introduction
What is Descriptive analytics?
Descriptive analytics is a type of data analysis which analyses the historical data of an organisation or business; it summarizes the data by identifying patterns, trends, and insights. Its result describes "What has happened” in the past using data aggregation, data mining, and data visualization techniques. Descriptive Analytics is a base of business intelligence; it performs statistical analysis on historical business data to find relevant results.
The main goal of descriptive analytics is to provide data insights on past events which help organizations to understand their performance and make strategic decisions for their organisation or business.
Techniques Used in Descriptive Analytics
Some of the most common techniques used in descriptive analytics are as follows −
- Data Summarization − Aggregating data to provide summaries, such as averages, totals, or percentages.
- Data Visualization − Using charts, graphs, and dashboards to represent data visually.
- Reporting − Creating static or dynamic reports to communicate insights.
Descriptive analytics is one of the most basic forms of analytics and is the foundation for advanced analytics methods like predictive and prescriptive analytics. Its analytical results are more inclined toward data trends, patterns, and relationships which are most important for organizations to make informed decisions.
Descriptive Analytics experts identify the data to query and analyze; they transform data queries into mathematical models that they apply to their chosen data. Descriptive analytics using data visualization is significantly easy and helps users in decision-making.
Descriptive analytics is a fast-growing field with a promising future. It allows you to make better business decisions and learn how people engage with companies and products. General descriptive analytics approaches include data collection, data preparation, exploratory data analysis, data visualization, statistical analysis, and predictive modelling. Descriptive analytics skills help users design business intelligence models and support organizations in harnessing the power of big data.
Components of Descriptive Analytics
The main key components of descriptive analytics are as follows −
- Measures of Frequency − These include count, percentage, and frequency.
- Measures of Central Tendency − measures of central tendency include mean, median, and mode.
- Measures of Dispersion − it measures the range, variance, and the standard deviation.
- Measures of Position − it measures the percentile ranks and quartile ranks
Features of Descriptive Analytics
Some of the most common features of descriptive analytics are as follows −
1. Data Summarization
This feature condenses large amounts of data into understandable form. It uses techniques like Aggregation, statistical measures (mean, median, mode), and data visualization.
2. Data Visualization
This feature presents data in graphical and colourful forms to make it easier to interpret and identify patterns. It includes Charts, histograms, heat maps, and dashboards. For Example, Some examples are visualizing sales trends over time, customer segmentation, or heat maps of website traffic.
3. Trend Analysis
This feature identifies data trends over time to provide context and inform future decisions. It uses Time series analysis, moving averages, and seasonal decomposition. For Example, Some examples are analysing trends in sales growth, website visits, or stock market prices.
4. Data Mining
The data mining feature explores large datasets to uncover patterns, correlations, and insights. It uses Clustering, association rule mining, and anomaly detection techniques. For Example, Some examples are identifying customer purchasing patterns, finding correlations between different variables, or detecting outliers.
5. Statistical Analysis
Statistical analysis features apply statistical methods to summarize the data and interpret it. Some most commonly used statistical analysis methods are Descriptive statistics, correlation analysis, and hypothesis testing. For Example: Finding the average, standard deviation, or correlation coefficient to understand data distribution.
6. Reporting
Reporting features provide stakeholders with regular updates on key metrics and performance indicators. It includes reports, dashboards, scorecards, and automated reporting systems. For Example Monthly financial reports, daily sales dashboards, or quarterly performance reviews.
Importance of Descriptive Analytics
Descriptive analytics parses historical data to find business trends and patterns. Using historical data and benchmarking, decision-makers can have a comprehensive understanding of performance and patterns to set business strategy. It provides businesses with critical information about how they are performing, where they are heading, and how they should stand in a competitive world.
Descriptive analytics analyses collected data over time, and accumulated data can be utilized to track the company's success by comparing measures from different periods. For example - measure sales or expenses by comparing quarterly statistics, computing revenue growth percentages, and displaying the results using charts and graphs to understand easily. Descriptive analytical results identify the areas of strength and weakness of an organization. For Example - Descriptive analytics data include year-over-year price variations, month-over-month sales growth, user count, and total revenue per subscriber. Descriptive analytics is used in conjunction with more recent analytics, such as predictive and prescriptive analytics.
Descriptive analytics allows professionals to compare a business group's performance using some set of tools like employee-generated revenue or expenses as revenue a percentage. It also compares analytical results with known industry averages or published results from other businesses. This comparative analysis gives a direction to the organisation that where they stand and where they need to improve.
How Does Descriptive Analytics Work in Business Analytics?
Descriptive Analytics is a powerful technique of data analytics; it summarizes data and produces analytical results in an informative way. It uses statistical methods like data distribution, central tendency, and dispersion to describe data and perform analytics.
Descriptive statistics in Business Analytics or Business Intelligence (BI) can be used to find relationships among variables and examine data groups. Descriptive statistics is used to frame strategic decisions about how to best analyse a dataset. Analytical results of Descriptive Analytics are produced using reports, tables, and charts and graphs like histograms, line graphs, pie charts, and box and whisker plots.
The process of Data analysis starts with data collection, consolidating collected data from multiple sources, and then transforming it into a standard format which can be further used for analysis and future reference.
Many companies use data intelligence, a set of strategies and tools for acquiring and analysing data, drawing conclusions, and developing action plans based on the findings.
Some organisations also use spreadsheets to perform simple descriptive analytics on gathered data, resulting in KPIs and other statistics that are then included in reports. Integrated ERP systems can hold an organization's business data in a single database which makes descriptive analytics easier. Integrated analysis tools are also used to make data storytelling, which involves constructing a narrative around data and communicating its relevance using data visualizations. ERP-embedded business intelligence can also be used with Real-time data and produces results using dashboards, charts, and reports to measure key performance indicators.
Process of Descriptive Analytics
The process of Descriptive analytics can be broken down into five steps which are as follows −
1. Determine Business Metrics
The organization must determine the metrics that it wants to generate based on the critical business goals of each group within the company or the company's general goals.
2. Identify Required Data
The organization must identify relevant sources to collect the data required to generate the appropriate metrics. This process could be difficult because the essential data is spread across multiple files and apps. Companies that use an Enterprise Resource Planning (ERP) system may have an easier time because their systems' databases already have most or all of the necessary data.
3. Extract and Prepare the Data
Extracting, integrating, and preparing pertinent data for analysis can be time-consuming if the data comes from multiple sources. It may entail data cleansing to eliminate inconsistencies and errors in the data, as well as keeping it in a standard format suitable for analytical tools.
4. Data Analysis
Companies can use a variety of tools to apply descriptive analytics, including business intelligence (BI) software and spreadsheets like Excel.
5. Present Data Results
Processed data are presented using graphical methods like dashboards, bar charts, pie charts, or line graphs. Visible data is easier to understand and interpret.
Advantages of Descriptive Analytics
Now, lets look at the stand-out benefits of descriptive analytics.
- Easy to learn − Descriptive analysis does not require expertise or experience with statistical methodologies or analytics.
- Availability of tools − Simple tools are available to do descriptive analytics.
- Answers business questions − Descriptive analytics can answer some common business questions like "What happened"
- Improved Decision-Making − Understanding what has happened in the past allows businesses to make more informed decisions regarding the future.
- Performance Monitoring − Descriptive analytics enables companies to monitor key performance indicators (KPIs) and determine whether they are accomplishing their objectives.
- Enhanced Understanding of Business Operations − Organizations can acquire a better understanding of their processes, customer behaviour, and market situations.
- Data-Driven Insights − Establishes a framework for data-driven decision-making, ensuring that company plans are grounded in concrete data rather than intuition.
- Risk Identification − Companies can identify possible dangers and possibilities for improvement by reviewing previous data.