Types of Descriptive Analysis: Examples & Steps

The study of statistics involves gathering, categorizing, analyzing, interpreting, and presenting quantitative facts and numbers. Statistics is a branch of math. It is beneficial when working with populations that are too big and diverse for precise, in-depth measurements. Statistics are essential when extrapolating broad inferences about a database from a test dataset.

 

Two different categories of analytics are descriptive and inferential. Today, we'll take a closer look at the descriptive analysis, including their definition, many types, and how they vary from inferential statistics. Let’s get started.

 

 

What is Descriptive Analysis?

 

The practice of utilizing analytical techniques to characterize or summarize a data collection is known as descriptive analysis, sometimes referred to as descriptive analytics or descriptive statistics. 

 

Descriptive analysis, one of the main aspects of data analysis, is well-liked for its capacity to produce understandable insights from unrecognizable data.

 

The descriptive analysis somehow doesn't seek to make predictions about the future, in contrast to other methods of data analysis. Instead, it only uses historical data that has been altered to make sense of it in order to draw conclusions.

 

Also Read | Descriptive Statistics in R

 

How Does Descriptive Analysis Work?

 

Data analysis requires businesses to first gather and consolidate raw data from multiple sources, then transform it into a standard format for analysis. They may now begin analyzing the data. 

 

Many businesses employ data intelligence, which is a collection of techniques and instruments for gathering and analyzing data, then drawing conclusions and formulating plans of action based on the results. Others add simple descriptive analytics to the combined data using spreadsheet formulae, producing KPIs and other statistics which are subsequently included in presentations.

 

Integrated ERP systems may hold all of the company's business information in a centralized database, which greatly simplifies descriptive analytics. Professional suites also come with integrated analysis tools to aid with data narrative, which is the process of creating a narrative around data using visuals to communicate the significance of the data in an engaging manner. 

 

Common KPIs can be provided with real-time data combined into dashboards, charts, and reports using ERP-integrated business intelligence tools.

 

 

Difference Between Descriptive Analysis & Inferential Analysis

 

Descriptive statistics and inferential statistics, or what you're doing with the data, vary considerably. Let's take a look to learn more about the two terms.

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	Descriptive Analysis	Inferential Analysis
MEANING	The area of statistics is defined as descriptive statistics. It is focused on providing a description of the population being studied.	A kind of statistics known as inferential statistics concentrates on inferring information about the community from sample analysis and findings.
FUNCTION	To summarize the sample, it describes the data that is previously known.	It makes an effort to draw a sample from the population that goes beyond the evidence at hand.
USAGE	To explain a circumstance.	To describe the likelihood that an event will occur.
ACTION TAKEN	Logically arrange, examine, and present facts.	Data comparison, testing, and prediction.
FORM OF FINAL RESULT	Tables, graphs, and Charts	Probability

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1. In contrast to how the final result is presented in probabilities in descriptive statistics, the final outcome has a graphical representation of a tabulated format.

 

2. Although inferential statistics describes the likelihood of the risk occurring, descriptive statistics depicts a condition.

 

3. To synthesize the selection, descriptive statistics describe the data that is previously known. In contrast, inferential statistics seek to draw inferences about populations that are outside the scope of the data at hand.

 

Also Read | Frequency Distribution in Data Statistics

 

Types of Descriptive Analysis

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Types of Descriptive Analysis

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There are several types, traits, or metrics of descriptive statistics. According to some experts, there are two sorts. Some others claim three or even four. We shall stick with four kinds for the sake of dealing with statistics.

 

1. Measures of Frequency

 

The frequency distribution in statistics indicates the responses or frequency of the possible possibilities in a data collection or sample, which are used for covering quantitative and qualitative research. Typically, the frequency distribution is shown in a graphical format. The counts or frequency of the values' repetitions within an interval, range, or particular group are provided alongside each item in the table or graph.

 

A representation or overview of categorical variables that have been divided into mutually exclusive groups and the number of instances in each class is called a frequency distribution. It enables the presentation of raw data in a more organized and orderly manner.

 

Bar charts, histograms, pie charts, and line graphs are examples of common charts and graphs used in frequency distribution presentation and visualization.

 

2. Central Tendency

 

A dataset's descriptive overview utilizing a single number that represents the center of the distribution of the data is referred to as having a central tendency. Statistical measures placement are another name for measures of central tendency. The measurements of central tendency are indeed the mean, median, and mode.

 

The standard or most frequent number in data collection is known as the mean, which is regarded as the most widely used measure of central tendency. The average score for data collection in ascending order is referred to as the median. The score or value that appears most frequently in a data collection is referred to as the mode.

 

3. Measures of Dispersion

 

Understanding how facts are dispersed throughout a range could be useful at times. Take the average stature of a sample of two persons to demonstrate this. The average size is six feet if both people are six feet tall. 

 

The average height is still six feet even if one person is five feet in height another is seven feet tall. Measures of variability like ranging or measure of dispersion can be used to quantify this type of distribution.

 

4. Variability

 

A summary statistic that reflects the level of sample variation is known as a measure of variability. How far away the measured values obviously fall from the center is determined by the variability measurements.

 

The length between the greatest and lowest frequencies within a data collection is idealized as the range, which shows the degree of dispersion. The standard deviation is used to calculate the average variance in a collection of data and gives information about how far a value from a data set is from the average value with the same given dataset. The variance, which is just an average of the squared deviations, represents the extent of the dispersion.

 

 

Example of Descriptive Analysis

 

Every part of the business, from finance to manufacturing and sales, has examples of descriptive analytics, some of which are included below.

 

• Accounting entries receivable and payable, working capital, inventory, and output are all included in business reports.

 

• Measures of the economy and other business Examples of descriptive analytics include KPIs. These include figures like the profitability ratio, current ratio, and return on capital employed that measure the strength and worth of a company.

 

• Involvement in social media: Descriptive analytics offers measures like an increase in followers, audience engagement, and income related to certain social media platforms that assist in assessing the return on social networking operations.

 

• Surveys: Descriptive analytics creates representations of the outcomes of internal and external surveys, such as customer satisfaction scores.

 

 

Importance of Descriptive Analytics in Business

 

Everyone in the organization benefits from using descriptive analytics to make better decisions that steer the company's operations in the correct direction. Managers can quickly assess how well the company is doing and where adjustments might be needed since it shows trends that would otherwise be concealed in raw data.

 

Additionally, descriptive analytics enables firms to share information internally and externally. Before they decide to participate in a company, potential lenders and investors, for instance, would want to carefully examine figures for sales, profit, cash flow, and debt.

 

 

Benefits & Drawbacks of Descriptive Analysis

 

There are several benefits to descriptive analytics. It may be carried out using commonly available instruments and doesn't need a thorough grasp of analytical or statistical approaches. 

 

It can respond to a lot of the often asked inquiries regarding how well a company is doing, such as whether or not sales last quarter met targets. This assists the company in identifying areas that require improvement.

 

Descriptive analytics' main flaw is that it only recounts what has occurred without seeking to understand its causes or foresee what will happen later. Additionally, it is typically restricted to relatively straightforward studies that focus on the interactions between two or three variables.

 

Also Read | Advantages of Business Intelligence in Finance

 

 

5 Steps of Descriptive Analysis

 

Determining the statistics you want to output is typically the first step in applying advanced statistics, and delivering them in the proper manner is the final step. The procedures to produce your own descriptive analytics are listed below.

 

1. State Business Metrics

 

Finding the metrics you want to produce is the first step. These need to represent the main corporate objectives of each segment or the organization as a whole. 

 

For instance, a business that is focused on growth may measure quarterly revenue growth, while its accounts receivable department might keep track of indicators like days sales outstanding and others that show how long it takes to recover money from consumers.

 

2. Identity the Necessary Data

 

Find the information you require to generate the necessary stats. The data may be dispersed over several programs and files at certain businesses. Businesses that utilize ERP systems can already have the majority of all of the data they require in the databases of their programs. 

 

Some indicators could also need information from other sources, such as social networking sites, e-commerce websites, and datasets used for industry evaluation.

 

3. Data Extraction and Preparation

 

Extracting, integrating, and preparing the data for analysis when it originates from numerous sources is a time-consuming but essential process to guarantee accuracy. This process could entail translating data into a format compatible with analytical tools as well as data cleansing to remove discrepancies and inaccuracies in data from various sources. 

 

Data modeling is a technique used by advanced data analytics to assist prepare, shaping, and arranging corporate data. A framework for defining and formatting data inside information systems is called data modeling.

 

4. Examining Data

 

Businesses may use descriptive analytics using a range of technologies, including spreadsheets and business intelligence (BI) software. Applying elementary mathematical computations to one or more parameters is a common step in descriptive analytics. 

 

Sales representatives could, for instance, keep tabs on the typical income per sale or the monthly revenue from new clients. Financial measures like profitability ratio, or the ratio of gross profit to sales, may be monitored by executives and financial experts.

 

5. Current Data

 

Stakeholders are frequently more likely to grasp data when it is presented in visually appealing ways like pie charts, bar charts, and line graphs. However, certain individuals, such as financial experts, might prefer information to be presented in the form of statistics and tables.

 

 

Conclusion

 

Popular data analysis techniques include descriptive analysis. Given that its sole purpose is to describe and summarize historical data, it is frequently carried out before diagnostics or predictive analysis.

 

A range of statistical methods, including measurements of incidence, internal consistency, distribution, and location, are used in descriptive analysis to achieve this. Descriptive analysis may be done in a variety of ways depending on your objectives, but the process often includes gathering, cleaning, and then evaluating data.