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Difference Between Data Science vs Business Analytics

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

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

Key Differences Between Data Science 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 the 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 is a step ahead of Business Analytics, which 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 the day-to-day decision-making of the company, whereas Business Analytics is vital in management making 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 but not 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 much dirty data, unlike Business Analysts.

Data Science depends to 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 on LinkedIn and Facebook, 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 The interdisciplinary field of data inference, algorithm building, and systems to gain insights from 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

Business decision-makers do not use Data Science results.

Inability to apply findings to the organization’s 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 the organization’s 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 data analysis. 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 to make confident decisions.

In addition to the data and general trends, an important factor is skill learning. Both offer employees a lot of scope to learn and improve themselves. This learning is, in fact, a must 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 hot openings. The opportunities that lay ahead are plenty.