Descriptive Analytics – Definition, Types, Examples, and More

Table of Contents

Introduction
Definition and Meaning of Descriptive Analytics
Types of Descriptive Analytics
Use Cases and Importance of Descriptive Analytics
Metrics for Descriptive Analytics

Statistical Descriptive Analytics
Operational Descriptive Analytics

Examples of Descriptive Analytics
Application of Descriptive Analytics with Examples

Health
Business Analytics
Big Data
Real Life
Marketing
Descriptive Analytics in R
Cognitive Computing

Final Words
Topics in Data Analytics

Introduction

The full-fledged concept of business analytics has improved the overall performance of the business ecosystem. From research, examination, testing, detecting to deriving desired results, data analytics has emerged as the most simplified approach. Amongst all the inclusive elements descriptive analytics is the first and foremost step towards leveraging the potential of predictive data analytics. It is the widely used analytics type focused on summarizing the historical and current data patterns. Generally, it is used in combination with predictive and prescriptive models because larger the company, more analytics features are required to measure the performances.

Definition and Meaning of Descriptive Analytics

It is the process of interpreting historical data to understand changing trends and business occurrences during a set period. The wide range of historical data helps to provide better comparative study and facilitate holistic strategic development. As a result, descriptive analytics is potent to answer the question, “What happened and why it happened?” followed by a suitable SWOT analysis of the company. Thus, the theory of descriptive analytics helps to draw an accurate picture of a company’s affairs and establish profound management strategies that result in a better return on investments.

The descriptive piece of business intelligence is highly industry-specific. Let’s say seasonal variation during shipment completion uses descriptive analytics to broadly accept suitable measures across the financial industry. Also, Return on Invested Capital (ROIC) created using three-pointers –net income, dividends, and total capital are used to understand the percentage comparison of several companies. A study showed that ideally $1 million might sound impressive but it seems to lack context. In case a figure represents a 20% monthly decline, then it is a concern whereas a 40% annual increase represents healthy sales strategies with are a result of descriptive analytics.

Types of Descriptive Analytics

Descriptive analytics is a set of unique data-driven analytics which is seen as a whole without further bifurcation. Being the most fundamental part of analytics it is widely used to monitor measure and monitor operational trends. It largely uses various quantitative and qualitative KPIs to assess and predict relatable events. The impacts of these descriptive products and events appear in the dashboards, presentations, reports, financial statements, etc. because it serves as the first step of research while aiming a result-oriented.

Descriptive analytics find great relevance with the widely accepted types of descriptive analysis which are commonly inclined towards a statistical approach. As a result, oftentimes, descriptive analytics are studied into six broad categories:

Measure of Central Tendency
Measure of Frequency
Measures of Position
Measure of Dispersion
Contingency Tables
Scatter Plots

Thus, descriptive revolves around these key types which help to analyze and predict the next possible events. As a result, provides a blueprint of how and when shall organizations start executing policies and plan to grab healthy opportunities and ways t to mitigate unfavorable contingencies.

Use Cases and Importance of Descriptive Analytics

The analytics is completely focused on collecting and aggregating raw data from a variety of sources and converting them into suitable analysis. Such a concept is implemented differently for all business entities. Some use spreadsheet formulas to apply basic descriptive analytics principles, while others generate KPIs and statistics to create reports. Moreover, the ERP suites are emerging as the single storage platform for all the organizational business data. As a result, descriptive analytics performs several vital functions which serve as the structural process while implementing Descriptive Analytics.

The use cases and importance of descriptive analytics is presented below:

Use Case	Description
Identification of suitable business metrics and KPIs	Since efficient performance, measurement is backed by the identification of suitable metrics and indicators. As a result, descriptive analytics help to frame the right metric set resulting in précised decision-making in the organizational framework.
Data collection and aggregation	The processing of insightful information begins with relevant data collection from reliable sources. Since scattering of data is the most common practice seen not just in professional but personal life too. Thus, aggregating and cataloging them at suitable locations in suitable frameworks can be done with descriptive analytics.
Analyzing the data and related scenarios	The transformation and duplication of data into understandable information is the major function of descriptive analytics. Using the analytics data scientists automate the analysis process and link numbers with appropriate metrics to create value in outcomes.
Facilitating cost-effect relationship	Using the descriptive analytics research algorithm, users can easily establish a cost-effect relationship between the organizational variables. It helps stakeholders to prepare for future contingencies and ongoing concerns trends too.
Precision while data presentation	Besides, collecting and analyzing data descriptive analytics helps to present informative visuals using charts, graphs, and other suitable statistics. The more precise the presentation is, the better is the decision-making.
Reliable report generation	Every research, analysis, and presentation remains irrelevant if reports are not generated rightly. Thus, report generation is the focal point of all descriptive analytics types.

Metrics for Descriptive Analytics

Being the center of data aggregation and mining descriptive analytics metric set serves as a prominent performance measurement tool. The performance of every business aspect is measured using a variety of metrics ranging from statistics to servicing. Various metrics used in the two types of descriptive analytics are presented below:

Statistical Descriptive Analytics

The data statistics acts as the Key Performance Indicators (KPIs) for understanding the business patterns and processes.

Metric	Description
Frequency Measure	The method measures the frequent occurrence of certain events which are likely to occur in the future too. Such a study helps to understand the pattern and predict events so that suitable strategies can be framed on time.
Dispersion Measure	For facilitating measurement data is divided into ranges. Using the descriptive data distribution the precise and accurate deviation from the range is identified. It helps to identify the gaps and indicate the need for the placement of a better strategy.
Central Tendency	Using the average measurement tools – mean, mode, and median, a suitable descriptive study can be established. Largely, the mean average is observed as the widely-used descriptive metric for measuring the mid-values of the data.
Position Measure	It involves the identification of position using a numerical value and serves response for the related factors. The percentiles and quartiles are useful tools to identify the core areas of expertise and empower each business magnitude.

Operational Descriptive Analytics

The operational descriptive analytics help to identify the changing trends and streamline cost and effect relationship in respect to the sales and finance at large.

Metric	Description
Pricing changes	The thorough study of pricing trends and culture helps to create an effective base for decision making. A complete study of the pattern helps in determining the difference between the progressive and regressive price structure of the product and services.
Sales growth	The market study is the major source of analyzing the sales structure. For leveraging the right sales strategy and implementation of descriptive analytics in an organizational structure sales trends are considered.
User & Customer	Identifying the category and quantity of customers helps an organization to understand their tastes and preferences. It further helps the right customer-oriented campaign take a productive and progressive shape.
Revenue scale	Since revenue does not remain precisely constant for all financial years, therefore a comparative study is required to understand the minimum and maximum scale. The study helps to set desired revenue goals and ways to attain them.

Examples of Descriptive Analytics

The essential analysis technique is used in almost all business spheres where data science and analytics have paved their way. Descriptive analytics being driven by machine learning algorithms helps to replace complexity with convenience.

Descriptive analytics supports social media usage and engagement data in an effective manner. DeZyre, the online learning platform is a prominent example of descriptive social analytics that measures the number of followers, likes, comments, posts, average response time to under user attitudes. Other prominent platforms Facebook and Instagram use the descriptive algorithm for a similar purpose too.
The sales and marketing campaign also gains immense power and strength from descriptive analytics by summarizing past events and analyzing future events. As a result, various industries have accelerated their way towards the descriptive horizon.
All the survey reports and market studies are backed by descriptive analytics where data from all the major sources is cataloged into reliable information. As a result, from here the basic step of predictive analytics takes an active shape.
The website analysis is performed on several parameters where descriptive analytics plays a prominent role. One such example of descriptive analytics is identifying and modeling the suitable font size on the website.
Descriptive analytics helps in the testing process by identifying the defects, iterations and helps to streamline the complete process by reducing the controllable variables and mitigating uncontrollable ones with suitable preparations.

Application of Descriptive Analytics with Examples

Health

Descriptive analytics has proved its worth for the healthcare sector upto the greatest extends. A profound concept of accurately predicting future events with AI-driven tools and technologies makes it the important data analytics type. As a result, its application in the health care industry provides all solutions from the core to the crux which includes operational to diagnosing and surgical aspects. Therefore, descriptive analytics is used for improved patients’ prediction; manage their health risks, telemedicine, EHRs, developing new therapies, optimizing ER admissions, Medical Imaging, and overall strategic planning.

Business Analytics

Ideally, the applications of descriptive analytics for business entities are huge, but it precisely hit the right spot in the case of understanding online business engagement. Descriptive analytics serves as the answer to the common query of analysts who spend their immense time in understanding the sales outcomes. With the use of descriptive analytics, any website becomes potent to report accurate information based on the trend prediction. Moreover, it also plays an effective role in digging deeper and discovering the response of the visitors who are the prospects to the company. Additionally, it facilitates error detection which results in quality product and service delivery.

Big Data

Descriptive analytics is the key performer in using every niche and glitch of big data. Besides supporting the profound strategy formation, it helps to provide industry-specific solutions. In the case of shipping companies, descriptive analytics helps to predict seasonal variation to ensure seamless execution of required operations. On the other the big data in the financial sector can also be channelized into insight information using the broadly accepted descriptive analytics tools and measures. Additionally, it contributes immensely in observing higher Return on Invested Capital (ROIC) by predicting accurate details for organizational net income, dividends, and total capital. Evidently, descriptive analytics all major grounds of big data and take them to new heights.

Real Life

Every material humans see around in today’s competitive world is a result of a significant attempt of companies to add their product to the visibility list of the end-users. From the cosmetic to garments to shoes to watches and the food we eat, everything is a result of detailed results conducted by data analysts to understand the market taste and trends. The accurate prediction drawn using descriptive analytics facilitates building profound strategies to satisfy customers. As a result, it impacts the day-to-day business and non – business life of the people by advancing its potent advantages through its tools and technologies.

Marketing

Imagine if an automobile company wants to analyze the past few months of car sales, they would have to make a long list for the same and find out what was the most preferred object. But it does not sound realistic as it might take a huge time which no organization can afford in the highly dynamic world. Thus, descriptive analytics effectively takes the role up and automates the analysis process with its AI and ML approach. As a result, the process becomes real quick which further helps to strategize suitable marketing campaigns which bring better business to the company. In recent times descriptive analytics has shown its full potential in promoting the online existence of brands by making the use of prominent data-driven metrics.

Descriptive Analytics in R

Since descriptive analytics are largely focused on analyzing the historical data for predicting future events. Therefore, it blends well with the programming language R, which is inclined towards statistical computing. With the profound combination of Artificial Intelligence tools and coding king, analysts and miners are able to develop data mining and analysis software. Thus, descriptive analytics research and prediction features add better strength to the R ecosystem which is responsible for developing an interactive and customized online platform.

Cognitive Computing

The wholesome purpose of cognitive computing is to simulate human thoughts into computerized models. It becomes possible with the use of self–learning algorithms which are backed by proficient data mining, natural language processing, and pattern recognition which helps computers to understand the functioning of human brains. As are a result, cognitive computing gets a big boost from descriptive-analytic which is a comprehensive blend of machine learning, deep learning, and natural language processing. Products like Microsoft Cortana, IBM”s Watson, and Apple’s Siri are the few prominent examples of cognitive computing which is backed by the effective implementation of descriptive analytics.

Final Words

Descriptive analytics has been the widest choice of several companies across the world. Regardless of the business nature, investment size, inventory type, operating cycle descriptive analytics stand suitable for all entities. There exists no line of distinction while implementing machine learning algorithms through a full-proven descriptive study. Thus, the scope of predictive analytics and data science seems to grow with the effective features of descriptive analytics too.

Topics in Data Analytics