It is a well-placed fact that data has become the driving force of all the big and small organizations. Today, companies, independent of their shape or size, rely on data to increase their customer experience and sales. We can say that data and its decryption is touching new heights. A significant chunk of the fortune 500 companies rely on data to get the best of their services.
Ever wondered what the name of that specific course that teaches the decryption of the terabytes of data present over the internet is? If not, Data Science is the name. However, the mass generally gets confused between a similar aspect of Data Science that, when merged with Business to study statistical data, is known as Business Analytics.
Today, in this article, we look at the common misconception that people have over Data Science vs. Business Analytics, allowing a better understanding of these identical but different aspects of data.
Table of Contents
Introduction
Data science is the process used to unify and integrate several statistical data and related methods to allow scientists to understand and segregate different aspects of information with several tools and techniques. Data science study uses various techniques and theories paired up with computer science, mathematics, and statistics to understand user information and customer response.
On the other hand, Business analytics is the process that helps businesses study the segregated data and understand the top trends that will help them out in improving customer experiences and sales. To classify it broadly, we can say that Business Analytics is a part of a data management solution that comes under the Data Science umbrella and uses many methodologies such as predictive analytics and statistical analysis to allow businesses to analyze and transform data and anticipate the trends follow.
However, because these two terms exchange a close relation in their work, Data Science vs Business Analytics is often confused and interchanged. However, it should be known that they are very different and need to be understood correctly to use them correctly.
Simply put, Business Analytics vs. Data Science is a broader scope than we know. Both these sectors lay a significant impact and provide critical insights for business-changing decisions for the company.
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Data Science Vs Business Analytics
When it comes to the scope of comparison, Data Science vs. Business Analytics is two very unique fields that have a different range of qualifications. However, what sets them apart majorly is the scope of problems addressed by any given field of study.
In layman’s terms, Data Science is the study that puts the use of statistics, trends, algorithms, and technology to understand and segregate data into different aspects that make sense. This is known as Data Science. The main contribution of data science in business and management is to provide actionable insights over a wide range of data that are either segregated or needs to be mined, trying to bring facts around business operations, customer trends, and behavior in byte sized format.
On the other hand, Business Analytics is a statistical study of segregated/structured data. Business Analytics allows solutions to overcome hurdles and improve business performance.
Because these two terms are often used interchangeably, the chances are that a business analytics problem could be wrongly approached with Data Science’s solution. Using two different sets of tools to solve Business Analyst could be adverse and bring undesirable results.
Therefore, you must understand the implications of Business Analytics vs. Data Science to ensure that you don’t interchange one with the other and get the best possible results with the right tools in hand.
You may also like read: Types of Business Analytics – Types of Analytics With Examples
Business Analyst vs. Data Scientist
The job role, functionalities, and expectations from a business analyst and data scientist are two very unique categories. Let’s have a quick summary of their jobs before heading to an insight into both the job roles. Let’s have a look at another scope of Data Science vs. Business Analytics.
| Data Scientist | Business Analyst |
| Use both structured and unstructured data. | They majorly deal with structured data. |
| Key Skills: – Data Manipulation & Analytics – Visualization Reporting – Statistical Analysis & Modelling – Machine Learning – Text Mining & NLP – AI & Cloud Computing | Key Skills: – Data Manipulation & Analytics – Visualization Reporting – Statistical Analysis & Modelling |
| Most Demand in: – Banking & Finance – E-commerce – Insurance | Most Demand in: – Banking & Finance – Sales & Marketing – Retail |
The Career of a Business Analyst:
The Business Analysts have the job role that requires them to examine and extract information from gigabytes of data sets and organize them in well-structured manner.
Typically, the expectation from a Business Analyst job role is to provide the hiring firm with the data that would allow the decision-makers to understand the moving trends in their business and insight into the company’s past performance and pick up the best ways for improved future performance.
A Business Analyst’s job role also requires them to be adept at structuring the right analytical models to provide the mined information to the leaders, aiding them with an insight into the data that will help drive the company towards increased profits.
Related: What Does a Business Analyst Do? Responsibilities, Roles & Salary
The Career of Data Scientist:
A Data Scientist’s job role requires them to work heavily over the data collection’s front end and help businesses analyze the moving trends. A data scientist tends to develop enhanced technical skills and has more tools in his reach to help companies collect and analyze data.
Their primary job expectation lies in designing or leveraging the statistical and machine learning algorithms to make best use of structured, unstructured and text data. In addition deriving insights from data, they may also develop and deploy data science solutions them for improved productivity.
While a Business Analyst’s vital job role requires them to look for new data trends to leverage quality information, Data Scientists may also go a step further to look for the reasons behind those trends.
Therefore, we can say that a Business Analyst vs. Data Scientist is two different roles but with sometimes with overlapping responsibilities.
How is Business Intelligence Different From Data Science?
While it is evident that data science allows organizations to understand the reasons behind the changing trends by using the different tools in the analysis of data, it also helps companies get on a practical and predictive approach towards business solutions.
Similarly, on the other hand, Business Intelligence or BI helps organizations to analyze their current state of business data and further understand their historical performance in any given business.
To sum it up, we can say that BI helps businesses interpret past data so that Data Science can use such past trends to form a future prediction.
While Data Science is a term that is a whole lot bigger than the implications of BI, Business Intelligence is the much-required shift from and is a fundamental part that helps businesses get a data-driven organization.
Lastly, BI has its implications towards Descriptive Analytics, while Data Science focuses on Predictive Analytics or Prescriptive Analytics.
Related: Business Analytics vs. Business Intelligence- What’s the Difference?
How is Data Analytics different from Business Analytics?
Business Analytics vs. Data Analytics is a rather confusing notion as both of them are somewhat similar in their sense of approach.
Let’s have a look into the concept of Business Analytics vs. Data Analytics for further information on the same:
Data analytics in the field of study involves analyzing different sets of data to develop the new and popular datasets that help the businesses and analysts come over the industry’s original and rising trends. Further, these data and trends are used to make business decisions.
On the contrary, Business analytics is the field where these data are used to form statistical and strategic responses, helping businesses make the necessary decisions. The information processed by business analysts is often evaluated after considering the matrices like cost, the efficiency of operations, and other such metrics.
While data analytics and Communicating insights with business teams and critical stakeholders
Preparing strategic recommendations for process adjustments, procedures, and performance improvements.
Therefore, we can conclude that the job profile and responsibilities of a Data Analyst vs. a Business Analyst are similar but segregated at the same time.
You may also like to read in detail: Do You Know the Differences Between Business Analytics and Data Analytics?
Conclusion
Now that we have understood the different aspects of Data Science vs. Data Analytics, we can say that data science and data analytics are two sides of the same coin required equally for a business enterprise’s successful running.
Data helps businesses thrive the much rising need for segregation and understanding of trends to develop the right circumstances that would enable the businesses to make the right decisions at the right time.
Lastly, the job role of a Data Analyst vs. a Business Analyst might seem different, covering two other edges. However, both professions’ nature is similar and necessary to work similarly to bring excellent results in a given organization.
FAQs – Frequently Asked Questions
Q1. Which is better, Business Analytics or Data Science?
The topic of Data Science vs. Data Analytics is huge. However, both the streams have different areas that they cover and come up with different expertise around ‘data’ and its management.
We can never clearly draw a line between what is better as both the professions and area of study are responsible for running business organizations, one or the other. However, it should be understood that data science requires a more in-depth understanding of coding, ML algorithms, and business analytics requires basic knowledge of the same.
Q2. What is the difference between Business Analysts vs. Data scientists?
There are a lot of differences between Business Analysts vs. Data scientists. To begin with, business is a mark of opportunity where events take place, and people sell their products without much understanding of insights.
Business Analytics helps in successfully running a business by data mining to improve business performance by uncovering actionable insights management’s crucial aspects.
While Data Science also relies on understanding data patterns and trends to make out actionable analysis, but it is expected to deal with the complexities of structured and unstructured data, device a wider variety of solutions using advanced tools and machine learning algorithms. Data Science is also a way towards Artificial Intelligence.
Q3. Can you be a data scientist with a Data Analytics degree?
Yes, you can be a Data Scientist with a Data Analytics degree. However, you need to adapt to meet the role expectations and master various skills, like machine learning, working on unstructured data, and natural language processing.
You may also like to read:
1. Data Analyst vs Business Analyst – Which is for you?
2. Evolution of Business Analytics | Business Analytics Future
3. Data Science vs. Computer Science; Skills & Career Opportunities
1 Comment
The core of data science is mathematics. Data science is more about the evolution of mathematical fields, especially those related to probability and statistics, operations research, and system modeling and simulation. Before data science, there was a field called Decision Science. There is very little difference between their purpose. But the advent of “big data” has brought this area into a new and additional area. Mathematicians usually do not have the computer infrastructure and data management skills required for big data. In addition, mathematicians generally did not have good big data programming skills in terms of data storage and processing. Specifically, they had no experience with Hadoop, Hive, cloud services, Internet data streams, wireless technology data streams, etc. Thus, a new field emerged in which mathematical skills merged with the fields of computer science and information technology. to shape Data Science. There is very little new mathematics in Data Science. Most of them are implementations of past formulas and algorithms that have been used for the past 40+ years. But it is this fusion of mathematics, computer science, and information
that is at the very core of data science.