Data Science vs Business Analytics

Difference Between Data Science vs Business Analytics

In the context of answering business problems, we discuss Data Science and Business Analytics. Both Data Science and Business Analytics involve data gathering, modeling and insight gathering. The difference between the two is that Business Analytics is specific to business-related problems like cost, profit, etc. whereas Data Science answers questions like the influence of geography, seasonal factors and customer preferences on the business. In short, Data Science is larger or superset of the two. Data Science combines data with algorithm building and technology to answer a range of questions. Recently Machine Learning and Artificial Intelligence have been doing their rounds and are set to take Data Science to the next level. Business Analytics, on the other hand, is the analysis of company data with statistical concepts to get solutions and insights.

Head to Head Comparison Between Data Science and Business Analytics ( Infographics)

Below is the Top 9 Comparisons Between Data Science and Business Analytics:

Key Differences Between Data Science and Business Analytics

Some key differences are explained below between Data Scientist and Business Analytics:

• Data Science is the science of data study using statistics, algorithms, and technology whereas Business Analytics is the Statistical study of business data.
• Data Science is a relatively recent development in the field of analytics whereas Business Analytics has been in place ever since a late 19th century.
• Data Science involves a lot of coding skills whereas Business Analytics does not involve much coding.
• Data Science is a superset of Business Analytics. So, a person with Data Science skills can do Business Analytics but not vice versa.
• Data Science being a step ahead of Business Analytics is a luxury. However, Business Analytics is mandatory for a business to understand the working and gain insights.
• Data Science analysis results cannot be used in day to day decision making of the company whereas Business Analytics is vital in management taking key decisions.
• Data Science does not answer a clear-cut question. The questions are mostly general. Business Analytics, however, answers very specific business-related questions mostly financial.
• Data Science can answer questions that Business Analytics can whereas not the vice versa.
• Data Science uses both structured and unstructured data whereas Business Analytics uses mostly structured data.
• Data Science has the potential to take leaps and bounds especially with the coming up of Machine Learning and Artificial Intelligence whereas Business Analytics is still taking slow steps.
• Data Scientists do not come across many dirty data whereas Business Analysts do.
• Data Science depends on a large extent on the availability of data whereas Business Analytics is not.
• The cost of investing in Data Science is high whereas that of Business Analytics is low.
• Data Science can keep pace with the Data of today. Data has grown and branched into a variety of data. Data Scientists are equipped with the right skills to deal with this. Business Analysts, however, do not possess this.

Data Science and Business Analytics Comparison Table

Below is the comparison table between Data Scientist and Business Analytics.

Basis For Comparison	Data Science	Business Analytics
Coining of Term	DJ Patil and Jeff Hammerbacher who were working in LinkedIn and Facebook respectively, first coined the term Data Scientist in 2008.	Business Analytics has been used since the late 19th Century when it was put in place by Frederick Winslow Taylor.
Concept	Interdisciplinary field of data inference, algorithm building, and systems to gain insights from data.	Use of statistical concepts to extract insights from business data.
Application-Top 5 Industries	Technology Financial Mix of fields Internet-based Academic	Financial Technology Mix of fields CRM/Marketing Retail
Coding	Coding is used widely. The field is a combination of traditional analytics practices with sound knowledge of computer science.	Does not involve much coding. More statistics oriented.
Languages Recommendations	C/C++/C#, Haskell, Java, Julia, Matlab, Python, R, SAS, Scala, SQL, Stata	C/C++/C#, Java, Matlab, Python, R SAS, Scala, SQL
Statistics	Statistics is used at the end of the analysis following algorithm building and coding.	The whole analysis is based on statistical concepts.
Work Challenges	Data Science results are not used by business decision makers. Inability to apply findings to organizations decision-making process. Lack of clarity on the questions that need to be answered with the given data set. Unavailability of/difficult access to data. Need to coordinate with IT.	Lack of significant domain expert input. Dirty data Unavailability of/difficult access to data. Privacy issues Lack of funds to buy useful data sets from external sources. Inability to apply findings to organizations decision-making process. Lack of clarity on the questions that need to be answered with the given data set. Limitations of tools. Need to coordinate with IT.
Data Needed	Both structured and unstructured data.	Predominantly structured data.
Future Trends	Machine Learning and Artificial Intelligence	Cognitive Analytics, Tax Analytics

Conclusion

Given the recent developments, both can expect a major shift in the way data is analyzed. With the rapidly growing data or Big Data, businesses will have the opportunity to explore different varieties of data and help the management make key decisions. This is just not financial analysis but also the analysis of the role customer preferences, geography etc. play in contributing to the growth of a company. Also forecasting data seems to be the order of the day. The management wants to know where they will stand a couple of years in the future so that they can make confident decisions.

In addition to the data and general trends, an important factor is skill learning.  Both offer employees a lot of scopes to learn and improve themselves. This learning is, in fact, a must in order to keep up with the recent developments. Gone are the days when analysis just involved statistics and survey data. Students and employees need to be versatile and constantly aim at learning new skills.  With changing data and learning trends, Data Science and Business Analytics opportunities can be considered as hot openings. The opportunities that lay ahead are plenty.

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