What is Data Analytics?

In today’s digital world, businesses and organizations are flooded with data. But data by itself doesn’t mean much unless you can understand it and use it wisely. That’s where data analytics comes in.

Whether you’re a student, job seeker, or business owner, learning about data analytics can give you a strong edge. In this beginner-friendly guide, we’ll explain what data analytics is, why it’s important, the types of data analytics, how it works, and how you can start a career in this exciting field.

What is Data Analytics?

Companies are constantly collecting a lot of data, but that data, most of the time, will not mean much just as it is raw. This is where data analytics comes into play. Data analytics is the process of evaluating raw data to get meaningful, actionable insights, which can then be investigated to drive smart business decision-making.

A data analyst pulls raw data, structures the data, and finally examines it specifically to turn data from nonsensical numbers to understandable information. Once the data has been interpreted, the data analyst passes their interpretations back to the company in the form of recommendations on what their next steps could be.

You can think of data analytics as an instance of business intelligence that is used to propose specific solutions to the constraints and challenges of their organization. It will essentially be extracting patterns from a data set that will tell you something meaningful and relevant about an area of a business; an example might be the behavior of certain customers, or how engaged a company’s staff could be to a specific technology.

In simple terms, data analytics helps turn numbers into knowledge.

For example:

A business might use data analytics to understand why sales dropped in a particular month.
A hospital may analyze patient data to improve care and reduce wait times.
A sports team might use data to improve player performance or game strategies.

Why is Data Analytics Important?

Today, almost everything we do generates data—shopping online, using social media, visiting a doctor, or watching videos. Organizations need a way to make sense of this data, and that’s where analytics becomes valuable.

Here are some key reasons why data analytics is important:

Better decision-making – It provides facts and figures that support smart choices.
Cost savings – It can identify areas where money is being wasted.
Improved efficiency – It shows how to optimize business operations.
Customer satisfaction – It helps businesses understand customer needs.
Competitive advantage – It gives companies an edge over their rivals.

Types of Data Analytics

Data analytics is divided into four main types, each with a different purpose:

Descriptive analytics

Descriptive analytics has traditionally been simple, surface-level analysis and answers the question of what has happened in the past. In most cases, descriptive analytics relies on two methods of analysis: data aggregation and data mining. First, the data analyst aggregates the data and presents it in some summarized form (aggregation) and then data mining allows the analyst to "mine" the data to find patterns within the data.

The data presented in the manner that is easiest for a large, widely-diverse audience to digest (not just data experts). Descriptive analytics does not describe the historical data, nor is it trying to establish causal relationships among data; at this point, the analyst is simply measuring and describing the "what". Descriptive analytics employs various techniques that fall under the umbrella of descriptive statistics.

Diagnostic analytics

Descriptive analytics identifies the "what," while diagnostic analytics tries to pinpoint the "why." In the process of diagnostic analytics, data analysts will typically start by identifying anomalies within the data (i.e., anything that cannot be explained through the accompanying data). For example, if there were a data sample that shows a decline in sales for March, the data analyst would have to determine why. To do this, they will have to go through the discovery phase, identifying any additional sources of data that may provide additional context and perhaps let them understand why these anomalies occurred in the first place. Lastly, they will try to find cause-and-effect relationships: for example, figure out whether any incidents correlated or coincided with the decline in sales. At this point in the process, data analysts can utilize probability, regression, filtering, and time-series analytics.

Predictive analytics

As the name indicates, predictive analytics is about predicting what will happen in the future. This is where data analysts begin to develop actionable, data-driven recommendations that the business can take based on the analysis.

Predictive analytics uses historical data and theory of probability to estimate what the probability of a future outcome is, and while it will never be 100% accurate, it will certainly lessen the amount of guesswork involved in important business decisions.

Predictive analytics can forecast all sorts of occurrences, from what products will likely be the most popular at certain times to how much revenue the business will expect to increase or decrease in a certain timeframe. All in all, predictive analytics is utilized to "hit the mark" for a business and do its best to take the most appropriate action.

Prescriptive analytics

Where predictive analytics is used to predict outcomes, prescriptive analytics provides advice regarding to actions and decisions to take.

In other words, prescriptive analytics shows you how you will utilize the predicted outcomes. When performing prescriptive analytical analysis, data analysts consider a range of scenarios and possible actions the company may take.

Prescriptive analytics is one of the more complex types of analysis and will likely include algorithms, machine learning, and computational modeling approaches. However, when used appropriately, prescriptive analytics can greatly enhance the decision-making process, and subsequently impact the company strategy and financial success.

The type of analysis you carry out will also depend on the kind of data you’re working with. If you’re not already familiar, it’s worth learning about the four levels of data measurement: nominal, ordinal, interval, and ratio.

How Does Data Analytics Work?

The data analytics process usually follows these steps:

1. Data Collection

This is the first step—gathering data from various sources like websites, surveys, databases, sensors, or transactions.

2. Data Cleaning

Raw data often contains errors, duplicates, or missing values. Cleaning ensures the data is accurate and usable.

3. Data Analysis

This step involves using software tools and methods (like statistics or programming) to explore the data and uncover patterns.

4. Data Visualization

Charts, graphs, and dashboards are used to present the findings clearly and understandably.

5. Decision-Making

Based on the insights, companies or individuals take informed actions.

Common Tools Used in Data Analytics

Here are some popular tools and programming languages used in data analytics:

Microsoft Excel – Great for small datasets and basic analysis.
SQL – Used to manage and query data in databases.
Python & R – Programming languages used for advanced analysis and visualization.
Power BI & Tableau – Tools for creating interactive dashboards and reports.
Google Analytics – Helps track and analyze website traffic.

Don’t worry if you’re not tech-savvy—many tools are beginner-friendly and free to learn.

Real-Life Examples of Data Analytics

To see how data analytics works in the real world, here are a few examples:

Retail – Amazon uses data to recommend products based on your shopping habits.
Healthcare – Hospitals use analytics to reduce patient wait times and predict disease outbreaks.
Finance – Banks analyze customer behavior to detect fraud and assess loan risks.
Sports – Teams like those in the NBA or IPL use data to make player selections and game strategies.

Who Can Learn Data Analytics?

Anyone can learn data analytics, regardless of their background.

You don’t need a computer science degree or a strong math background to get started. Many successful data analysts started as teachers, marketers, or sales professionals.

If you enjoy solving problems, asking questions, or finding patterns, you already have the mindset of a data analyst.

How to Start Learning Data Analytics

Here’s a simple roadmap to get started:

Understand the basics – Learn what data analytics is and how it’s used.
Learn Excel and basic statistics – Great starting points for beginners.
Take a beginner-friendly course – Many platforms like Coursera, Udemy, and edX offer affordable courses.
Practice with real data – Use free datasets from Kaggle or Google Dataset Search.
Learn a programming language – Python is highly recommended for beginners.
Build a portfolio – Show your projects and skills on GitHub or LinkedIn.
Apply for internships or entry-level jobs – Start gaining real-world experience.

Careers in Data Analytics

With the rise of data-driven decision-making, data analytics jobs are in high demand. Some common roles include:

Data Analyst
Business Intelligence Analyst
Data Scientist (advanced role)
Marketing Analyst
Financial Analyst
Operations Analyst

Salaries are competitive and grow with experience and skills.

Conclusion

Data analytics is changing the world. Whether it is in the areas of business, healthcare, or entertainment, it's impacting how we make decisions and find success.

If you are inquisitive, enjoy solving problems, and want to create a career with a good career path ahead of it, then learning data analytics is a great option. There have never been so many free resources, online courses, and beginner-friendly tools available to make learning easy and fun!

What is Data Analytics? [Complete Guide for Beginners]