Data Analytics and its type
Last Updated :
11 Jul, 2025
Data analytics is an important field that involves the process of collecting, processing, and interpreting data to uncover insights and help in making decisions. Data analytics is the practice of examining raw data to identify trends, draw conclusions, and extract meaningful information. This involves various techniques and tools to process and transform data into valuable insights that can be used for decision-making.
In this article, we will learn about Data analytics, data which will help businesses and individuals that can help them to enhance and solve complex problems, Types of Data Analytics, Techniques , Tools , and the Importance of Data Analytics .
Data Analytics
What is Data Analytics?
In this new digital world, data is being generated in an enormous amount which opens new paradigms. As we have high computing power and a large amount of data we can use this data to help us make data-driven decision making. The main benefits of data-driven decisions are that they are made up by observing past trends which have resulted in beneficial results.
In short, we can say that data analytics is the process of manipulating data to extract useful trends and hidden patterns that can help us derive valuable insights to make business predictions.
Understanding Data Analytics
Data analytics encompasses a wide array of techniques for analyzing data to gain valuable insights that can enhance various aspects of operations. By scrutinizing information, businesses can uncover patterns and metrics that might otherwise go unnoticed, enabling them to optimize processes and improve overall efficiency.
For instance, in manufacturing, companies collect data on machine runtime, downtime, and work queues to analyze and improve workload planning, ensuring machines operate at optimal levels.
Beyond production optimization, data analytics is utilized in diverse sectors. Gaming firms utilize it to design reward systems that engage players effectively, while content providers leverage analytics to optimize content placement and presentation, ultimately driving user engagement.
Types of Data Analytics
There are four major types of data analytics:
- Predictive (forecasting)
- Descriptive (business intelligence and data mining)
- Prescriptive (optimization and simulation)
- Diagnostic analytics
Data Analytics and its Types Predictive Analytics
Predictive analytics turn the data into valuable, actionable information. predictive analytics uses data to determine the probable outcome of an event or a likelihood of a situation occurring. Predictive analytics holds a variety of statistical techniques from modeling, machine learning , data mining , and game theory that analyze current and historical facts to make predictions about a future event. Techniques that are used for predictive analytics are:
- Linear Regression
- Time Series Analysis and Forecasting
- Data Mining
Basic Cornerstones of Predictive Analytics
- Predictive modeling
- Decision Analysis and optimization
- Transaction profiling
Descriptive Analytics
Descriptive analytics looks at data and analyze past event for insight as to how to approach future events. It looks at past performance and understands the performance by mining historical data to understand the cause of success or failure in the past. Almost all management reporting such as sales, marketing, operations, and finance uses this type of analysis.
The descriptive model quantifies relationships in data in a way that is often used to classify customers or prospects into groups. Unlike a predictive model that focuses on predicting the behavior of a single customer, Descriptive analytics identifies many different relationships between customer and product.
Common examples of Descriptive analytics are company reports that provide historic reviews like:
- Data Queries
- Reports
- Descriptive Statistics
- Data dashboard
Prescriptive Analytics
Prescriptive Analytics automatically synthesize big data, mathematical science, business rule, and machine learning to make a prediction and then suggests a decision option to take advantage of the prediction.
Prescriptive analytics goes beyond predicting future outcomes by also suggesting action benefits from the predictions and showing the decision maker the implication of each decision option. Prescriptive Analytics not only anticipates what will happen and when to happen but also why it will happen. Further, Prescriptive Analytics can suggest decision options on how to take advantage of a future opportunity or mitigate a future risk and illustrate the implication of each decision option.
For example, Prescriptive Analytics can benefit healthcare strategic planning by using analytics to leverage operational and usage data combined with data of external factors such as economic data, population demography, etc.
Diagnostic Analytics
In this analysis, we generally use historical data over other data to answer any question or for the solution of any problem. We try to find any dependency and pattern in the historical data of the particular problem.
For example, companies go for this analysis because it gives a great insight into a problem, and they also keep detailed information about their disposal otherwise data collection may turn out individual for every problem and it will be very time-consuming. Common techniques used for Diagnostic Analytics are:
- Data discovery
- Data mining
- Correlations
The Role of Data Analytics
Data analytics plays a pivotal role in enhancing operations, efficiency, and performance across various industries by uncovering valuable patterns and insights. Implementing data analytics techniques can provide companies with a competitive advantage. The process typically involves four fundamental steps:
- Data Mining : This step involves gathering data and information from diverse sources and transforming them into a standardized format for subsequent analysis. Data mining can be a time-intensive process compared to other steps but is crucial for obtaining a comprehensive dataset.
- Data Management : Once collected, data needs to be stored, managed, and made accessible. Creating a database is essential for managing the vast amounts of information collected during the mining process. SQL (Structured Query Language) remains a widely used tool for database management, facilitating efficient querying and analysis of relational databases.
- Statistical Analysis : In this step, the gathered data is subjected to statistical analysis to identify trends and patterns. Statistical modeling is used to interpret the data and make predictions about future trends. Open-source programming languages like Python, as well as specialized tools like R, are commonly used for statistical analysis and graphical modeling.
- Data Presentation : The insights derived from data analytics need to be effectively communicated to stakeholders. This final step involves formatting the results in a manner that is accessible and understandable to various stakeholders, including decision-makers, analysts, and shareholders. Clear and concise data presentation is essential for driving informed decision-making and driving business growth.
Steps in Data Analysis
- Define Data Requirements : This involves determining how the data will be grouped or categorized. Data can be segmented based on various factors such as age, demographic, income, or gender, and can consist of numerical values or categorical data.
- Data Collection : Data is gathered from different sources, including computers, online platforms, cameras, environmental sensors, or through human personnel.
- Data Organization : Once collected, the data needs to be organized in a structured format to facilitate analysis. This could involve using spreadsheets or specialized software designed for managing and analyzing statistical data.
- Data Cleaning : Before analysis, the data undergoes a cleaning process to ensure accuracy and reliability. This involves identifying and removing any duplicate or erroneous entries, as well as addressing any missing or incomplete data. Cleaning the data helps to mitigate potential biases and errors that could affect the analysis results.
Usage of Data Analytics
There are some key domains and strategic planning techniques in which Data Analytics has played a vital role:
- Improved Decision-Making - If we have supporting data in favour of a decision, then we can implement them with even more success probability. For example, if a certain decision or plan has to lead to better outcomes then there will be no doubt in implementing them again.
- Better Customer Service - Churn modeling is the best example of this in which we try to predict or identify what leads to customer churn and change those things accordingly so, that the attrition of the customers is as low as possible which is a most important factor in any organization.
- Efficient Operations - Data Analytics can help us understand what is the demand of the situation and what should be done to get better results then we will be able to streamline our processes which in turn will lead to efficient operations.
- Effective Marketing - Market segmentation techniques have been implemented to target this important factor only in which we are supposed to find the marketing techniques which will help us increase our sales and leads to effective marketing strategies.
Future Scope of Data Analytics
- Retail : To study sales patterns, consumer behavior, and inventory management, data analytics can be applied in the retail sector. Data analytics can be used by retailers to make data-driven decisions regarding what products to stock, how to price them, and how to best organize their stores.
- Healthcare : Data analytics can be used to evaluate patient data, spot trends in patient health, and create individualized treatment regimens. Data analytics can be used by healthcare companies to enhance patient outcomes and lower healthcare expenditures.
- Finance : In the field of finance, data analytics can be used to evaluate investment data, spot trends in the financial markets, and make wise investment decisions. Data analytics can be used by financial institutions to lower risk and boost the performance of investment portfolios.
- Marketing : By analyzing customer data, spotting trends in consumer behavior, and creating customized marketing strategies, data analytics can be used in marketing. Data analytics can be used by marketers to boost the efficiency of their campaigns and their overall impact.
- Manufacturing : Data analytics can be used to examine production data, spot trends in production methods, and boost production efficiency in the manufacturing sector. Data analytics can be used by manufacturers to cut costs and enhance product quality.
- Transportation : To evaluate logistics data, spot trends in transportation routes, and improve transportation routes, the transportation sector can employ data analytics. Data analytics can help transportation businesses cut expenses and speed up delivery times.
Conclusion
Data Analytics act as tool that is used for both organizations and individuals that seems to use the power of data. As we progress in this data-driven age, data analytics will continue to play a pivotal role in shaping industries and influencing future.
Similar Reads
Data Science Tutorial Data Science is a field that combines statistics, machine learning and data visualization to extract meaningful insights from vast amounts of raw data and make informed decisions, helping businesses and industries to optimize their operations and predict future trends.This Data Science tutorial offe
3 min read
Introduction to Machine Learning
What is Data Science?Data science is the study of data that helps us derive useful insight for business decision making. Data Science is all about using tools, techniques, and creativity to uncover insights hidden within data. It combines math, computer science, and domain expertise to tackle real-world challenges in a
8 min read
Top 25 Python Libraries for Data Science in 2025Data Science continues to evolve with new challenges and innovations. In 2025, the role of Python has only grown stronger as it powers data science workflows. It will remain the dominant programming language in the field of data science. Its extensive ecosystem of libraries makes data manipulation,
10 min read
Difference between Structured, Semi-structured and Unstructured dataBig Data includes huge volume, high velocity, and extensible variety of data. There are 3 types: Structured data, Semi-structured data, and Unstructured data. Structured data - Structured data is data whose elements are addressable for effective analysis. It has been organized into a formatted repos
2 min read
Types of Machine LearningMachine learning is the branch of Artificial Intelligence that focuses on developing models and algorithms that let computers learn from data and improve from previous experience without being explicitly programmed for every task.In simple words, ML teaches the systems to think and understand like h
13 min read
What's Data Science Pipeline?Data Science is a field that focuses on extracting knowledge from data sets that are huge in amount. It includes preparing data, doing analysis and presenting findings to make informed decisions in an organization. A pipeline in data science is a set of actions which changes the raw data from variou
3 min read
Applications of Data ScienceData Science is the deep study of a large quantity of data, which involves extracting some meaning from the raw, structured, and unstructured data. Extracting meaningful data from large amounts usesalgorithms processing of data and this processing can be done using statistical techniques and algorit
6 min read
Python for Machine Learning
Learn Data Science Tutorial With PythonData Science has become one of the fastest-growing fields in recent years, helping organizations to make informed decisions, solve problems and understand human behavior. As the volume of data grows so does the demand for skilled data scientists. The most common languages used for data science are P
3 min read
Pandas TutorialPandas is an open-source software library designed for data manipulation and analysis. It provides data structures like series and DataFrames to easily clean, transform and analyze large datasets and integrates with other Python libraries, such as NumPy and Matplotlib. It offers functions for data t
6 min read
NumPy Tutorial - Python LibraryNumPy (short for Numerical Python ) is one of the most fundamental libraries in Python for scientific computing. It provides support for large, multi-dimensional arrays and matrices along with a collection of mathematical functions to operate on arrays.At its core it introduces the ndarray (n-dimens
3 min read
Scikit Learn TutorialScikit-learn (also known as sklearn) is a widely-used open-source Python library for machine learning. It builds on other scientific libraries like NumPy, SciPy and Matplotlib to provide efficient tools for predictive data analysis and data mining.It offers a consistent and simple interface for a ra
3 min read
ML | Data Preprocessing in PythonData preprocessing is a important step in the data science transforming raw data into a clean structured format for analysis. It involves tasks like handling missing values, normalizing data and encoding variables. Mastering preprocessing in Python ensures reliable insights for accurate predictions
6 min read
EDA - Exploratory Data Analysis in PythonExploratory Data Analysis (EDA) is a important step in data analysis which focuses on understanding patterns, trends and relationships through statistical tools and visualizations. Python offers various libraries like pandas, numPy, matplotlib, seaborn and plotly which enables effective exploration
6 min read
Introduction to Statistics
Statistics For Data ScienceStatistics is like a toolkit we use to understand and make sense of information. It helps us collect, organize, analyze and interpret data to find patterns, trends and relationships in the world around us.From analyzing scientific experiments to making informed business decisions, statistics plays a
12 min read
Descriptive StatisticStatistics is the foundation of data science. Descriptive statistics are simple tools that help us understand and summarize data. They show the basic features of a dataset, like the average, highest and lowest values and how spread out the numbers are. It's the first step in making sense of informat
5 min read
What is Inferential Statistics?Inferential statistics is an important tool that allows us to make predictions and conclusions about a population based on sample data. Unlike descriptive statistics, which only summarize data, inferential statistics let us test hypotheses, make estimates, and measure the uncertainty about our predi
7 min read
Bayes' TheoremBayes' Theorem is a mathematical formula used to determine the conditional probability of an event based on prior knowledge and new evidence. It adjusts probabilities when new information comes in and helps make better decisions in uncertain situations.Bayes' Theorem helps us update probabilities ba
13 min read
Probability Data Distributions in Data ScienceUnderstanding how data behaves is one of the first steps in data science. Before we dive into building models or running analysis, we need to understand how the values in our dataset are spread out and thatâs where probability distributions come in.Let us start with a simple example: If you roll a f
8 min read
Parametric Methods in StatisticsParametric statistical methods are those that make assumptions regarding the distribution of the population. These methods presume that the data have a known distribution (e.g., normal, binomial, Poisson) and rely on parameters (e.g., mean and variance) to define the data.Key AssumptionsParametric t
6 min read
Non-Parametric TestsNon-parametric tests are applied in hypothesis testing when the data does not satisfy the assumptions necessary for parametric tests, such as normality or equal variances. These tests are especially helpful for analyzing ordinal data, small sample sizes, or data with outliers.Common Non-Parametric T
5 min read
Hypothesis TestingHypothesis testing compares two opposite ideas about a group of people or things and uses data from a small part of that group (a sample) to decide which idea is more likely true. We collect and study the sample data to check if the claim is correct.Hypothesis TestingFor example, if a company says i
9 min read
ANOVA for Data Science and Data AnalyticsANOVA is useful when we need to compare more than two groups and determine whether their means are significantly different. Suppose you're trying to understand which ingredients in a recipe affect its taste. Some ingredients, like spices might have a strong influence while others like a pinch of sal
9 min read
Bayesian Statistics & ProbabilityBayesian statistics sees unknown values as things that can change and updates what we believe about them whenever we get new information. It uses Bayesâ Theorem to combine what we already know with new data to get better estimates. In simple words, it means changing our initial guesses based on the
6 min read
Feature Engineering
Model Evaluation and Tuning
Data Science Practice