Business Analytics vs Data Science- What's the Difference?
Data Science vs Business Analytics
Data science and business analytics are two terms that are oftentimes used interchangeably. However, data science, commonly abbreviated as DS, and business analytics, commonly abbreviated as BA, are notably different. Business analyst professionals and data scientists understand the difference between the two concepts. Other business professionals must also take the time to understand how data science and business analytics differ. Understanding the difference between data science and business analytics allows for more optimal utilization of both domains.
In order to understand the difference between data science and business analytics, it is necessary to define them. Data science is the study of data using statistics in order to produce key insights. Although data science supplies key insights, it does not directly serve business decisions. Alternatively, business analytics is the analysis of data for business decision making purposes.
Although data analyst professionals may debate it, business analytics is generally considered a business intelligence subset. Interestingly, many data analyst professionals classify business intelligence as a subset of data science. The confusion surrounding data analytics vs data science makes much more sense when considering how much all of these concepts overlap.
Similarities and Differences Between DS and BA
There are significant differences between data science and business analytics that all professionals must be aware of. One significant difference is that data science is multidisciplinary while business analytics is strictly utilized for business purposes. Data science uses artificial intelligence like machine learning in addition to statistics and algorithms. In fact, machine learning is often considered the link connecting artificial intelligence to data science.
Business analytics uses some of the same data analytics tools as data science. Although this may be obvious to a data scientist or business analyst it is crucial for companies to understand. One example is data mining utilized for more streamlined big data analysis. Data science combines traditional data analytics practices with computer science knowledge. An example of computer science knowledge necessary in data science is coding. Alternatively, business analytics is very more statistics oriented.
Business analytics data and data science data have crucial variations. Both business analytics and data science use structured data. However, business analytics uses structured data predominantly while data science uses both structured and unstructured data. Structured data is classified as data that is both defined and searchable. Alternatively, unstructured data is typically stored in its native format. Unstructured data is considered qualitative while structured data is considered quantitative.
Is Data Science or Business Analytics Better?
The data science vs business analytics conversation often results in professionals debating which domain is preferable. In actuality, neither business analytics nor data science is better than the other, they are simply better utilized differently. Although both business analytics data and data science data are important they are useful for different purposes.
Business analytics and data science applications both overlap and diverge. The top industries that utilize data science are technology and finance. Interestingly, business analytics is also applied heavily to these same industries. However, eCommerce and academic industries are top data science utilizers while marketing and retail industries are top business analytics utilizers.
The way that business analytics and data science are used within an organization differs. For instance, business analytics results are crucial to key decision makers in a company. On the other hand, data science can supply valuable insights but is not generally utilized for decision making purposes. Ultimately, data science studies patterns and trends while business analytics focus on specific business problems. While the insights that data science supplies may be relevant, business analytics is much more valuable for decision making personnel.
The future applications of business analytics and data science are also important to consider. Artificial intelligence and machine learning are data science future applications. Alternatively, cognitive analytics and tax analytics are business analytics future applications.
The different applications of data science and business analytics reveal a lot about the professionals that engage in both. In fact, both data science and business analytics have their own specialists. Data scientists are analytical data experts that have valuable technical skills. The technical skills that a data scientist possesses help solve very complex problems. Data scientists are frequently recognized for their insatiable curiosity towards problem solving.
Business analysts are accountable for connecting intellectual technology and an overall business. Business analysts use data analytics to evaluate business processes and for data driven decision making. Business analysts are in close contact with executives and shareholders to whom they deliver reports and recommendations. Although business analysts and data scientists are different, they are both interested in problem solving. Both business analysts and data scientists also interact with big data regularly.
Deciding whether business analytics or data science is more appropriate for a specific purpose depends on objectives. Sometimes a mixture of data science and business analytics is the most appropriate choice. Other times, either business analytics or data science may be prioritized. Thankfully, a great understanding of business analytics and data science makes it much easier to decide when to utilize each.
Key Takeaways of Business Analytics vs Data Science
- Although business analytics and data science are notably different in their application neither is better than the other.
- There are many differences and similarities between business analytics and data science that all business professionals should be aware of.
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