Business Analytics vs. Data Science- How They Compare
Knowing how to harness and leverage big data is required by growth-oriented organizations that want to remain competitive. While there are many different business intelligence solutions and artificial intelligence available to collect and manage large quantities of information, many companies still don't understand exactly what to do with all of the new data they have.
While management may know exactly what needs to be fixed internally, understanding how to use data to pinpoint the exact cause of the problem can be complicated.
Other organizations have different needs. For example, educational institutions may want to use collected data to understand how the emotional well-being of students affects their learning capacities.
Thankfully, there are business analysts with machine learning experience available who know how to use data to address different requirements and answer complex questions. But first, every organization needs to understand the difference between data science and analytics to determine which field is more applicable to their particular set of circumstances.
While management may know exactly what needs to be fixed internally, understanding how to use data to pinpoint the exact cause of the problem can be complicated.
Other organizations have different needs. For example, educational institutions may want to use collected data to understand how the emotional well-being of students affects their learning capacities.
Thankfully, there are business analysts with machine learning experience available who know how to use data to address different requirements and answer complex questions. But first, every organization needs to understand the difference between data science and analytics to determine which field is more applicable to their particular set of circumstances.
Data Science vs. Business Analytics
Data science is a multidisciplinary field that integrates statistics and programming skills to extricate valuable insights from data. It employs complex algorithms and predictive modeling to analyze structured and unstructured information and generate intelligence unrelated to specific business decisions.
Primarily, a data scientist solves analytically complex issues from a broader perspective, such as the roots of customer behavior or patterns in market trends.
While business analytics and data science are often used interchangeably, they are two separate disciplines. Both use data to generate insights, but BA is focused on analyzing historical information in the context of a specific business problem.
Data science is an umbrella phrase for everything related to data mining, including analytics. In summary, BA is a subset of the data science field, just as it is a subset of business intelligence.
There are many other differences between data science and business analytics, including-
Primarily, a data scientist solves analytically complex issues from a broader perspective, such as the roots of customer behavior or patterns in market trends.
While business analytics and data science are often used interchangeably, they are two separate disciplines. Both use data to generate insights, but BA is focused on analyzing historical information in the context of a specific business problem.
Data science is an umbrella phrase for everything related to data mining, including analytics. In summary, BA is a subset of the data science field, just as it is a subset of business intelligence.
There are many other differences between data science and business analytics, including-
1. Algorithms and Unstructured Data
Data science drills down into unknown situations that have no previous algorithms employed to extract insights. Its purpose is to solve complex problems that nobody has ever addressed in the past by using both unstructured (data without a predefined model) and structured op data sets.
Because data science does not consider historical information, problems are solved by exploring data and finding the top method to generate a model that can deliver insights. This requires experienced data scientists who are skilled in predictive modeling and statistical algorithms.
Data analysts conduct BA by using historical information to create a predictive model. A data analyst only looks at structured data to pinpoint patterns and trends in real-time and past data to find the best path forward in the future. There are previous algorithms and formulas set in place before an analyst ever conducting business analytics.
Because data science does not consider historical information, problems are solved by exploring data and finding the top method to generate a model that can deliver insights. This requires experienced data scientists who are skilled in predictive modeling and statistical algorithms.
Data analysts conduct BA by using historical information to create a predictive model. A data analyst only looks at structured data to pinpoint patterns and trends in real-time and past data to find the best path forward in the future. There are previous algorithms and formulas set in place before an analyst ever conducting business analytics.
2. Coding and Computer Science Knowledge
Business analytics does not usually require the analyst to perform coding, or programming language to teach a computer how to behave. Instead, business analytics is more oriented towards understanding statistics and numerical values to pinpoint patterns.
Data science requires both quantitative analysis and a comprehensive understanding of computer science. This analyst must know how to code so he/she can navigate big data and develop models. Many coding tools allow the analyst to validate statistical models, focus on solutions, and build large enterprise online systems.
Data science requires both quantitative analysis and a comprehensive understanding of computer science. This analyst must know how to code so he/she can navigate big data and develop models. Many coding tools allow the analyst to validate statistical models, focus on solutions, and build large enterprise online systems.
3. Industry Use
Data science is typically employed in different fields than business analytics. Because data science is used to solve broad and complex problems, it is more often utilized in academia, financing, e-commerce, or technology companies.
For example, data science may be used by educational institutions to find new methods to innovate the curriculum, monitor pupil requirements, or use surveys to assess social-emotional skills. Companies like Amazon have used data science to generate recommendation systems or filtering systems that predict customer preferences.
While there is a crossover between industries that employ data science and business analytics, BA is better suited for retailers, marketers, and manufacturers. Because it uses historical information to generate predictive models, these industries often employ analytics to pinpoint inefficiencies and eliminate them in the future.
For example, a retailer may use BA to pinpoint inefficiencies in past inventory management, resulting in a more streamlined re-ordering process in the future.
A manufacturer might use business analysis to determine when equipment tends to break down in the past, ensuring preventative measures are in place in the future to conduct maintenance before an equipment breakdown.
For example, data science may be used by educational institutions to find new methods to innovate the curriculum, monitor pupil requirements, or use surveys to assess social-emotional skills. Companies like Amazon have used data science to generate recommendation systems or filtering systems that predict customer preferences.
While there is a crossover between industries that employ data science and business analytics, BA is better suited for retailers, marketers, and manufacturers. Because it uses historical information to generate predictive models, these industries often employ analytics to pinpoint inefficiencies and eliminate them in the future.
For example, a retailer may use BA to pinpoint inefficiencies in past inventory management, resulting in a more streamlined re-ordering process in the future.
A manufacturer might use business analysis to determine when equipment tends to break down in the past, ensuring preventative measures are in place in the future to conduct maintenance before an equipment breakdown.
4. Patterns vs. Business Problems
In short, data science is more concerned with analyzing trends and patterns that have not previously been noted. New algorithms and models are generated by observing these trends, helping to make future predictions or deliver a broad assessment of a complex issue.
Business analytics is employed to solve a specific problem and pinpoint inefficiencies to make better future decisions.
While industries may use a combination of both disciplines, analytics is better at improving day-to-day workflows and operational efficiency. It is also preferable for startups or smaller organizations seeking to find new customers and generate revenue streams. Data science is preferable for academia or larger organizations looking to assess broad issues.
Business analytics is employed to solve a specific problem and pinpoint inefficiencies to make better future decisions.
While industries may use a combination of both disciplines, analytics is better at improving day-to-day workflows and operational efficiency. It is also preferable for startups or smaller organizations seeking to find new customers and generate revenue streams. Data science is preferable for academia or larger organizations looking to assess broad issues.
Key Takeaways
In conclusion, here are the key points to remember about business analytics vs. data science-
- Data science is a broad field that requires programming and modeling skills to answer complex problems. Business analytics utilizes historical and current information to pinpoint inefficiencies and predict future events.
- Data science requires an analyst to drill down into structured and unstructured data sets to generate new models. Business data analytics does not typically look at unstructured data, nor does it require new programming.
- Data science necessitates coding and programming skills while business analytics does not.
- Data science is typically employed by technology companies, e-commerce, and academia. BA is more often used by manufacturers, retailers, and marketers.
- Data science is used from a broader perspective and does not pinpoint inefficiencies or solve daily business needs. BA is a better choice for small to mid-sized businesses seeking to optimize operational efficiency and streamline workflows.