Data Science vs Business Analytics : A Beginner’s Guide in 10 Easy Points
Introduction
Business is a huge process that involves different activities like planning, organizing, understanding, implementing, fruitful results, failures, etc. Being a part of these activities, here we are going to discuss two important elements that have a wide scope in the organization. Both Data Science and Business analytics are very familiar terms in the business environment. Each time has its significance and contributes its part to the growth of the organization. So let’s see what is data science and what is business analytics? Also, let us explore the Differences Between Data Science vs Business Analytics in detail.
Data science
Data science is a concept or discipline of analyzing the data using several algorithms, functions for both the structured and unstructured data which is useful to perform various activities in the organization. It is a traditional method and the initial step also.
Business Analytics
Business analytics is an iterative concept. It is a methodological exploration of data to analyze and produce statistical reviews and records. It uses only structured data and no algorithms were allowed. Certain companies like data-driven decision-making organizations were used widely. It is like a continuous process or simply we can say it as a loop.
Differences Between Data Science and Business Analytics
Besides understanding the individual concepts of data science and business analytics it is very appreciable to understand the differences between data analytics and business analytics and every point of concern.
- Evolution
- Concept and meaning
- Examples
- Coding
- Languages
- Requirement of Data
- Future Applications
- Frequency
- Processing Time
- Challenges in the work environment
1. Evolution
The data science concept is a new concept when compared to business analytics it came into existence in the year 2008 by DJ Patil and Jeff Hammerbacher. On the other hand, business analytics is a traditional approach which is in practice since the 19th century itself. Frederick Winslow Taylor had brought this concept.
2. Concept and meaning
The major difference between data science and business analytics is the meaning of these terms. Data science is an interdisciplinary concept that utilizes the algorithms for both the structured and unstructured data whereas the concept of business analytics involves the analysis of structured data and applies statistical tools.
3. Examples
The concept of data science is utilized in various industries like-technology academic, financial, the mix of fields, internet-based organizations, etc. In a contrast, the concept of business analytics is used in technology, Financial industries, CRM, retail marketing industries, etc. Several organizations use both the concepts of data science & business analytics. For those organizations, if we take a whole process, the first three steps were undertaken by data sciences and the last step of data science will act as the first step of business Analytics. From this, we can understand, how is business analytics different from data science?
4. Coding
Another important difference between the
Data science & business analytics is the usage of coding in the process of analyzing data. As Data science is a traditional approach, it involves a lot of coding, and sound computer knowledge is also required. On the other hand, business analytics doesn’t need coding at all. It only uses statistical tools and some mathematical operations to analyze the data.
5. Languages
Along with the differences between Data Science and Business Analytics, they have few similarities. Certain languages like see, c, C h, met lab, python, SQL, R SAS, Java, etc were similar in both data science and business analytics. But in data science, Haskell, Julia, Stata, etc were also in practice.
6. Requirement of Data
While understanding the meaning of what is the difference between data science and business analytics? , It is important to understand the major difference. Even it seems to be a small aspect, but it counts well. The importance of data science might be either structured data or unstructured data but business analytics doesn’t process or analyze the unstructured data. It only accepts the structured data as input. So to get any kind of solution, it is important to consider the input that we are giving. So to analyze and get the output from these two concepts, the input which is either structured data or unstructured data should be highly prioritized.
7. Future Applications
From various research studies, it is forecasted that the future applications of data science are artificial intelligence and machine learning. But if we observe the business analytics, the future applications were noticed as cognitive learning and tax Analytics.
8. Frequency
The utilization of the results of data science and business analytics where differ from one another. The output of data science may not be used in day-to-day decisions but the business Analytics results were used frequently and had much importance in key decision making. This is what we can see how is business analytics different from data science?
9. Processing Time
Also, another important issue to discuss while understanding the differences between data science and business analytics, data science can able to analyze and process the leaps and bounds of data within a short span. On the other hand, business analytics can process only structured data two in a small amount. It is a bit slower process when compared to the data science concept.
10. Challenges in the work environment
Both data science and business Analytics were giving productive results, few limitations are faced by both concepts. So let’s have a glance at the challenges facing data science and Business Analytics at the work location.
A) Challenges of data science:-
- It cannot work individually. they need to coordinate with the IT department.
- The accessibility of data is quite difficult.
- While understanding the output of data science, several questions are arising and no clear cut answers were provided.
- The results of data science were not fit for the decision-making process and these were not used by the decision-makers.
B) Challenges of Business Analytics:-
- It utilizes only structured data.
- Business analytics also need to coordinate with IT.
- The output of business Analytics was also not prioritized intention making the process.
- The accessibility of data is a bit difficult.
- They may have some dirty data at times.
These are the various challenges that appeared at the workspace while using the concepts of data science and business analytics.
Conclusion
Hence we can conclude that data science is a traditional approach which is a combination of both algorithms and technological tools to analyze both unstructured and structured data. And business analytics is another concept to analyze structured data using statistical tools and simple techniques of mathematics. After exploring more about the differences between data sciences and business Analytics, we can come to a clear idea about both the concepts and their differences. By considering the available input, forecasting the output the organization needs to choose any of the concepts or both for its growth.
It is also important to find the right place to learn and become proficient in all these skills and languages. Jigsaw Academy, recognized as one of the Top 10 Data Science Institutes in India, is the right place for you. Jigsaw Academy offers an Integrated Program In Business Analytics for enthusiasts in this field. The course runs for 10 months and is conducted live online. Learners are offered a joint certificate by the Indian Institute of Management, Indore, and Jigsaw Academy.
