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In a world that is only getting increasingly connected and more and more reliant on technology, big data has become one of the biggest players in the world. Today, companies of all sizes and in every industry these days are constantly collecting large amounts of data that they can turn into actionable insights and useful information to make data-driven decisions and ultimately get better results for the business. But with companies around the world now generating massive data sets at a faster rate than ever seen before, this is driving the demand for professionals with the right education, skills, and knowledge to analyze this data and gather the right information from it so that it can be put to the best use.
For businesses to get a better understanding and be able to use the data that they collect, the fields of data science and data analytics have substantially grown over the past few years from being mostly academic and research focused fields to something that is becoming more and more essential as a business department. Both of these fields can be an excellent choice if you are looking for a high-demand career where you can enjoy great job security and growing average salaries, however, understanding the difference between these two fields isn’t always easy at first, as they are very interconnected and quite similar, despite their differences.
Data analytics and data science are both fields that businesses need to produce various results and often with different approaches. When companies need to study and put the data that they collect to use, it’s important to understand what each field offers and how they are different from each other.
What is Data Analysis?
Data analysis focuses on mainly processing and analyzing existing datasets. There are various different fields within data analysis, such as predictive analysis, which is focused on using data to predict more accurate future outcomes and drive better business decisions. You can learn more about getting into this role with a predictive analytics degree from Saint Bonaventure University.
A data analyst works in a role where they are mainly involved in determining different methods to capture, process, and organize data. They do this with the main aim of uncovering actionable insights and solutions to current business problems. Data analytics in its various forms tends to be used mainly to find answers to questions and produce results that can lead to immediate business improvement. For example, data analytics may be used by the marketing department to help the company determine how a specific marketing campaign is currently performing based on data collected from it, and gain insights that can be applied to both current and future marketing campaigns to achieve improved results.
How and Where is Data Analysis Used?
Data analysis is a process that can be used at any point within a business where there are improvements to be made, problems to be solved or answers to be found. With businesses today collecting data on every aspect of the company, there is always data available that the data analyst can study to determine the best course of action for the future or figure out what can be done differently compared to in the past to get better future results. For example, data analysis is often used in the process of getting to know customers better and determining customer needs. Businesses may analyze data collected from their website, for example, to learn more about what customers do when they visit the website, where they appear to be getting the most value from it and where they are more likely to bounce off the page or the site altogether.
This information can then be used to make data-driven improvements to the website based on which areas of the website have the desired effect on customer behavior and which do not. Reducing business costs is another goal that is often achieved through the use of data analytics, allowing businesses to more accurately and efficiently determine where they could be spending less in any area of the company. It can also be used to make important business decisions, by analyzing the data and determining which decisions are going to be more effective at improving future company results and success.
What is Data Science?
Data science is a similar field that is mainly focused on gaining actionable insights from large, raw or structured data sets. Data science is mainly focused on finding answers to things that we were not always aware that we did not know, unlike data analytics, which is more focused on finding the answers to questions that have actually been asked.
To achieve this, data scientists use a variety of different techniques to uncover answers such as computer science, machine learning, statistics, and predictive analysis. Data scientists often study huge data sets to establish problems that the business might not have even noticed it was dealing with. The main role of these professionals is to ask the right questions and uncover any potential areas that can be further explored. Instead of focusing on finding the right answers, they are more concerned that the right questions are being asked, by exploring and analyzing disparate and disconnected data sources, making future trend predictions, and finding better ways to analyze the data available.
How and Where is Data Science Used?
There is a wide range of ways that data science can be used within a business in order to achieve better results. For example, some companies may use data science to determine which questions they should be asking about their target audience and customers to make sure that they get a better understanding of their needs and requirements. Once you have determined the best questions to ask, it becomes easier for the business to find out more about their target audience and offer services or products that provide the best solution to problems and keep customers satisfied. Data science also plays a part in a wide range of business decision making, allowing companies to dig deeper into the whys of making a decision before using data to figure out which option is the best one to take.
What are the Biggest Differences Between These Fields?
While you will often hear the terms data analytics and data science being used interchangeably, these are two different fields. Data science can sometimes be used as an umbrella term for a wider group of fields that are all involved with mining large sets of data. On the other hand, data analysis is usually used to describe the more focused and specific field of analyzing data to find trends and answers to questions. Data science is used to predict outcomes and find the right questions to ask, to uncover problems that we may not have even realized we were facing. On the other hand, data analysis takes the problem that we know we are facing right now and uses data to find solutions to it.
Why Choose These Fields for Your Career?
Whether you are interested in uncovering the questions or the answers, both data analytics and data science are exciting career fields for the future with lots of opportunities. As the world gathers more and more data and we become increasingly connected with technology advancing at a rapid rate, job prospects for data science and data analytics professionals are only expected to improve. Some of the main reasons to consider a career in either of these fields include:
High Demand Worldwide
Around the world, technology is growing, and an increasing number of businesses are using this to their advantage to gather more and more data. This has led to a rapidly increasing demand for data science and data analytics professionals who are trained to make sense of this data and gather actionable insights from it. If you choose to work in either of these fields, there is little worry about finding work since both data analysis and data science are increasingly used by companies of all sizes in a wide range of industries around the world.
Generous Starting Salaries
Compared to other fields, the entry-level salaries for both data science and data analytics are very generous; you could get started in your data science career on a salary of $80k annually. Both fields are experiencing a shortage of professionals, which in turn has led to a competitive landscape where professionals who are set to fill this gap are rewarded generously.
More Career Freedom
Compared to a lot of roles where you may be limited to one industry or select industries, data is growing and being used in so many different ways around the world. As a result of this, a qualification in big data allows you to enjoy more career freedom with roles on offer in every industry imaginable no matter where you go. There’s no need to worry about switching careers or working in a different industry when you have these skills that are easy to transfer. Today, big data is an ever-growing field with an increasing demand for good professionals.
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