Business Analytics vs Data Science: Know The Exact Difference

Ever wondered if numbers really make a difference?

In the past few decades, the nature of business has evolved a lot. How businesses function, types of consumers, preferences and behaviour, profits and revenue, and everything else has changed the way businesses work. This evolution is seen due to newer marketing approaches, operational processes, and a focus on customer experience. Behind all these changes, there’s one root cause which made it all happen – numbers. We’re constantly surrounded by different types of data which create an impact on businesses and the world at large.

The process of leveraging data, conducting analysis, and developing insights to achieve business goals and growth is called business analytics. It makes use of different types of analytical tools and techniques to identify problems, analyse relevant data, develop insights, and transform data into actionable solutions. This process boosts business growth and helps organisations achieve their business objectives.

However, it is often confused with the field of other fields of data analytics, like data science. The subject of data science is an interdisciplinary field which uses scientific processes, algorithms, methods, and systems to extract insights from structured as well as non-structured data. It essentially helps solve complex problems with data-driven solutions that can fuel growth and efficiency.

The domains, business analytics and data science, are similar yet far apart. These fields meet at a single centre point, but one that makes all the difference – data. However, both of these fields have individual roles and responsibilities, each bringing something new to the table. In this blog, we’ll understand the specifics of both these fields, where they overlap and how they differ from each other.

Business Analytics: Solving Business Problems

The field of business analytics is rapidly evolving. With more data sources and analytical tools, the potential of business analytics is also growing. It involves identifying problems, collecting, analysing, interpreting, and translating data into solutions that can fuel business growth. The application of business analytics also includes drawing insights to improve operational efficiencies as well as customer experience.

Primary goal of business analytics

Business analytics stands strong on one and only one primary goal – to solve problems. Once a certain problem or challenge is identified, the efforts that follow all work to solve the defined problem. The insights developed are in best interests of the business, and aim to achieve business objectives in a smooth and efficient manner. Such strategic problem-solving requires proper understanding of the business as well as data analysis.

Specifications in business analytics

The wide domain of business analytics is defined by several specifications that represent the impact of business analytics. Firstly, it uses an intricate and carefully curated process to conduct thorough business and analysis. Secondly, it involves the use of different metrics to understand business goals as well as data insights. And together, the whole process improves decision making by encouraging more data-driven decisions.

Also Read – 10 Key Subjects Covered in Business Analytics Courses

Tools and techniques in business analytics

Since the field of business analytics runs on data, there are many tools and techniques developed to properly clean, prepare, and analyse the data. Business analytics makes use of advanced tools like Excel, programming languages like Python, Power BI, and SQL to prepare and organise the data, and Tableau to communicate the data visually. You can learn all these tools with IMS Proschool’s comprehensive business analytics program which covers all the skills and tools required to excel in business analytics.

Data Science: Focuses on Exploration and Modelling

Data science is a rich mix of statistical processes, methods, algorithms, and domain expertise to draw knowledge from structured and unstructured data. Because it majorly deals with data, data science involves powerful leveraging of data to discover underlying trends, patterns, and correlations which can together drive informed decision-making. The applications of data science serve across various organisations and industries as well.

Primary goal of data science

While the field of data science is quite wide and advanced, it thrives on a single primary goal – to extract meaningful insights. The typical overview of data science involves collection, cleaning, and analysis of data, model building and evaluation, and finally, deploying developed solutions and monitoring their performance. With the help of data science, organisations can gain a competitive advantage and make more data-driven decisions.

Specifications of data science

The process of data science is driven by a few significant tasks which make it thorough and fool-proof. There’s Exploratory Data Analysis (EDA) which helps understand data through visualisation tools like plot graphs, histograms, etc., feature engineering which creates new features to enhance existing models, and model building, which makes use of machine learning algorithms to build and train models.

Tools and techniques in data science

Data science involves major engagement with data which is treated with a variety of tools and techniques. It can use statistical methods like regression analysis, hypothesis testing, etc., machine learning algorithms, data visualisation tools, programming languages like Python and SQL, and cloud platforms like Azure. Each tool used in data science serves a different purpose and makes the process a lot smoother and quicker.

Also Read – How Much Do Data Scientists Earn in India, North America, and Europe?

Key Differences Between Business Analytics and Data Science

Now that we know both the fields, business analytics and data science, a little better, let’s dive deeper into how they’re different from each other. Certain factors set one apart from the other, and the distinction is necessary to understand which field serves what purpose and creates what kind of impact. Following are the key differences between business analytics and data science:

Business Analytics vs Data Science – What’s The Difference?

Business Analytics vs Data Science – What Are Similarities?

While there are many differences that set business analytics apart from data science, there’s also a few similarities that show where these two fields meet. These similarities are equally important to understand what both of these domains have in common, potential opportunities to work in tandem with each other, and what such collective use of these fields can mean for organisations.

• Both business analytics and data science are disciplines centered around leveraging data to make more informed decisions.
• The mutual goal is to extract valuable insights that can help improve business performance.
• The reliance on statistical techniques to analyse data is common in both the fields.
• Statistical analysis techniques like regression analysis, hypothesis testing, and correlation analysis are common for both the fields.
• Business analytics and data science both make use of data visualisation to present data in a more engaging and interactive manner.
• Both the fields require proficiency in programming languages to analyse data properly.
• The iterative process is common for both the domains, which broadly covers collection, analysis, interpretation, and refinement of data and insights.
• Finally, both the fields demand an analytical mindset which is capable of solving complex business problems.

Also Read – Want a job in Business Analytics? Read top 6 job profiles & salaries

Business Analytics vs Data Science – Collaboration Opportunities These 2 Fields

The key differences and similarities between business analytics and data science are clearly definitive of what each brings to the table. The real magic, however, lies in the synergy between business analysts and data scientists which come together to make a powerful force that boosts data-driven decision-making. Some key collaboration opportunities include:

• Problem definition: With their individual capabilities, business analysts and data scientists can come together to identify and clearly define problems and challenges.
• Data collection and preparation: While business analysts can identify relevant data sources, data scientists can leverage their technical skills to clean and prepare the data.
• Generating insights: With the domain knowledge of business analysts and statistical expertise of data scientists, together they can generate excellent insights based on patterns and trends.
• Interpreting and communicating data: Once the data is interpreted collectively, it can be translated into more visually appealing and informative visualisations for the ease of communicating insights with stakeholders.
• Monitoring solutions: Business analysts can focus more on business growth and impact of the implemented solutions, while data scientists can parallelly work to monitor performance and enhance the processes wherever needed.

Such a collaborative environment can result in fruitful solutions and much optimised processes for organisations. They can leverage the capabilities of both business analysts and data scientists, with effective communication, and a shared vision to work towards similar goals. When working together, both these critical roles can create a lasting impact on organisations and yield wonderful results.

Also Read – Top 10 Business Analytics Courses | Online & Offline

Business Analytics vs Data Science – Why Do People Confuse Between These 2 Fields & Courses

As we discussed above, there are many overlapping similarities between business analytics and data science. These similarities and the major role of data often lead to confusion between the two fields. The knowledge of both these fields is often misunderstood as the same. Here’s a list of factors which confuse the two fields as one and the same:

• The skill sets required for both the fields are quite overlapping in nature. Each field requires knowledge and familiarity with programming languages to indulge in data analysis.
• The common purpose of extracting insights to solve problems is similar between the two. However, business analytics focuses on solving specific business problems whereas data science generally focuses on solving certain complex problems.
• Since both the terms, business analytics and data science, are used interchangeably, the distinction between both the industries can often become blurred.

Also Read – Everything You Need to Know About Data Science Jobs

Proschool’s Role In Preparing You As A Business Analyst

If the engagement of business and data analytics sounds exciting to you, IMS Proschool’s Business Analytics Course is just the platform to begin your education in this field. Their end-to-end program covers all the aspects of business analytics which equip you with the right knowledge and skills to excel in the field.

Listed below are a few perks you can have while studying business analytics with IMS Proschool:

• Ranked among one of the top 10 analytics institutes in India, Proschool is one of the most reputed organisations to learn with.
• The 2.5 month program covers all the essential touchpoints of business analytics, including programming languages, foundational statistics, as well as theoretical knowledge of the domain.
• Learn from 15+ case studies and projects which equip you with more practical knowledge of the subject, and better clarity of the profession.
• With new classes almost every month, online and offline, you can begin pursuing the program at any convenient time that works best for you.
• Build a strong problem-solving mindset, a skill extremely crucial to business analytics.
• Explore several job opportunities in 30+ companies spanning across 800+ jobs at any given time.
• Gain knowledge from experienced professionals who solve each doubt and answer every question you may have.

Frequently Asked Questions

1. What is the difference between business analytics and data science?

The key difference between business analytics and data science is the primary goal of both these fields. While business analytics aims to solve specific business problems and encourage business growth, data science aims to solve generally complex problems that can enhance operational systems.

2. How to choose between business analytics vs data science?

To choose between business analytics and data science, try to evaluate your interests, skills, and goals, to identify which field would you fit best in. If you’re most interested in solving business problems, business analytics is the way to go. Whereas if your interests lie in technical research and development of complex models, data science might be a better fit.

3. Which course has more scope – business analytics or data science?

Business analytics and data science both offer significant scope and growth opportunities for businesses. The perception of which field is better differs based on individual interests and skills. Both of these fields overlap to a great extent, such as the central role that data plays. Since both the fields rely on data to extract insights, there is good scope for business analytics as well as data science.

4. Can I do business analytics after data science?

Absolutely, you can transition from data science to business analytics. In fact, your strong foundation of data science gives you a significant advantage in business analytics, and makes data analysis a lot more effective. While you may already have technical proficiency and problem-solving abilities, you can look forward to building business acumen and enhancing your communication skills for a smoother transition.

Conclusion

Both the fields of business analytics and data science have many similarities and differences. While the two fields are closely related, business analytics focuses on practical applications and enhanced business outcomes, whereas data science focuses on solving complex problems. Organisations can either harness the power of an individual field or bring them together to fully boost a collaborative environment, push organisational growth, and explore newer opportunities that create a promising way ahead for businesses!