Without exaggeration, nearly every industry in existence can make use of data analysis. A political party can better understand the interests of its constituents through analytics; a healthcare company can track their use of certain medical supplies more closely; an academic organization can better look at what areas of interest their students most prefer to focus on. The possibilities are endless, and the future growth in analytics is similarly expansive. 

But while data analytics is pervasive in our everyday lives, it’s a commonly misunderstood field. To add to the confusion, it’s often conflated with data science, which is a semi-related but distinct field. 

If you’re looking for clarification about what data analytics is, then you’re in the right place. Keep reading to learn more about how data analytics is used, what tools data analysts employ, and how to launch a data analytics career today.

Why Use Data Analysis?

To understand what data analysis is and what it can do, it’s helpful to understand why data analysis is used. Treasure troves of data are created by a nearly endless stream of applications, websites, programs, and more. From tracking the kinds of entertainment that someone consumes to counting the steps they take when exercising, everything that a person does while digitally connected can produce meaningful data. 

Here’s what data analytics can do for a business:

Higher Sales

Data analytics allows businesses to better target customers through tailored advertisements, which leads to higher sales. This is a matter of both reaching the right customer and reaching them with the right message—data analytics can help on both fronts. 

Better Customer Recommendations

Data analytics helps demystify users, and thanks to data analytics, users can enjoy increasingly custom recommendations. Take streaming services, for example. A lot of streaming is driven by being able to recommend items for users to enjoy, maintaining their engagement and happiness.

More Efficiency 

Improving production is one of the largest advantages of data analytics, especially in the realm of manufacturing. While the initial buy-in and development of analytics can feel expensive and time-consuming, the actionable insights provided by data analytics can drastically improve how a business functions. 

Better Best Practices

Data analytics allow a business to establish what works, ultimately helping to design the best business models available. They don’t need to struggle trying to determine what their consumers are looking for, or to find ways to improve efficiency within their workflow. The data says it all, and processes can therefore be streamlined to fit that.

Data Analytics vs. Data Science

Data analytics and data science are often confused for each other, but understanding their differences is key to answering the question of what is data analytics. 

The key difference between data science and data analysis is how data is handled and the ultimate results of that data. A data analyst is tasked with hunting for the best way to fix a specific need of a business. They might be asked, “How do we get the user to engage more?” or “What is the best product to sell to this kind of customer?” They take in relevant data and seek out any patterns within that data, translating that information into information that stakeholders can readily understand, and ultimately offer that data up to the business they work with.

A data scientist, on the other hand, is rarely tasked with such a specific question. Instead, they’re more likely trying to determine questions the company hasn’t asked but still needs an answer to. They build out algorithms and data models that seek out these questions, trying to find the problems that they need to solve. Essentially, a data analyst works with specific, requested data, while a data scientist helps define what those questions are.

Common Data Analytics Tools

The following are among the most regularly used tools in the industry:


SAS is a type of statistics software that helps visualize data, write queries, and analyze subsets of information. It ultimately helps craft machine learning mechanisms that help with future queries.


Python is arguably the most regularly used programming language in data analytics, as it is an open-source language that helps with data organization and modeling.


There are many data visualization tools that a data analyst can use, and Tableau is by far the most popular. When it comes to designing reports for stakeholders, Tableau is an excellent choice.

Apache Spark

If you need to process data as quickly as it comes in, Apache Spark is exceptionally efficient. This program uses some of the best in machine learning technologies to analyze data, helping to make analytical tasks a bit easier.


QlikView is a program that specializes in taking in truly massive amounts of data and providing key information from that data in real-time. Its guided analytics can help with important business decisions.

Power BI

This user-friendly tool combines data-driven information with key visualization elements, making your data approachable and that much easier to turn into information for stakeholders.

Data Analysis Skills

Data analysts need to cultivate a mix of hard and soft skills in order to succeed in the industry. 

Data Analysis Hard Skills

While there are no hard and fast rules altogether, the following are highly recommended hard skills for data analysts:

Programming Languages

Python, as mentioned above, is a highly recommended programming language for data analysts, though just about any programming language can be useful. SQL, or Structured Query Language, is also regularly employed and thoroughly recommended.

Machine Learning

While a machine learning engineer is also far removed from a data analyst, understanding artificial intelligence and the kinds of algorithms employed in machine learning can go a long way to success.


Having an understanding of statistics is useful when it comes to analyzing data, especially larger fragments of data. Similarly, knowing how to approach complicated statistics can make providing information to stakeholders much easier.

Data Organization

Understanding the best methods to acquire, organize, and ultimately store data is key. This also includes tracking trends and models within that data where possible.

Data Analyst Soft Skills

While the technical skills above are key to success in data analysis, these soft skills can be of equal or greater importance:

Communication Skills

Data analysts may end up working with other team members on their tasks. Even if their work ends up being largely solo, they ultimately end up reporting to assorted stakeholders and higher-ups. Being able to effectively communicate and present findings is a large part of the work, so being able to communicate effectively is a much-needed skill.

An Analytical Approach

As the job title implies, a data analyst typically should already have a natural interest in, and taste for, analytics. If you already enjoy approaching things from an analytical perspective, then a career in data analytics could be right for you.

Problem-Solving Mentality

With a glut of tools available to a data analyst, knowing when and how to use all those tools is key. Thus, data analysts must approach their work with a problem-solving mindset, ready to approach potentially complex problems.

Industries That Employ Data Analytics

While data analytics are employed in essentially every business sector, there are a few industries where it is of particular importance. These industries are always growing and often hire data analysts at multiple levels:


Retail is increasingly driven by data, identifying sales trends and pushing customers towards productive trends.


Being able to analyze diagnoses, medical supplies, and drug distribution are all important for success in the medical sphere. Data analytics can also help track viral infections and other factors that require tracing.


Optimizing routes and understanding the best ways to transfer and maintain product distribution is best done through data analytics.


The supply chain is increasingly complex, but manufacturers can employ data analytics to streamline their processes.


Many different financial organizations can use analytics to identify regular concerns, especially in instances of fraud. This can help protect both the bank and those that invest in it, resulting in a win-win for all involved.