There are over 5.40 billion internet users in the world as of September 2022 who spend an average of 6 hours and 42 minutes on the internet every day.
These users make up 70% of user-generated data of the 2.5 quintillion bytes of data generated each passing day.
This data is valuable across all industries for monitoring trends, discovering insights, and making critical decisions.
The field of data is as broad as there are approaches to manipulating data for various purposes. Two such fields making use of data are business analytics and data science.
In both, data is collected, processed, and manipulated to arrive at desired results.
However, these two fields are different in many ways. Professionals who undertake business analytics courses and are ushered into the career world use data to identify and solve specific business problems.
Thus, business analytics uses already-established methodologies that are applied to data, the raw material, to solve a problem.
On the other hand, professionals who pursue data science courses aspiring to launch a career in data science use data to devise techniques/algorithms that they can apply to datasets to extract information that is useful for decision-making.
As John Owen rightly puts it, “Data is what you need to do analytics. Information is what you need to do business.”
Let’s delve deeper into the differences between business analytics and data science. Business analytics or data science? Find out which is better below.
What is business analytics?
Business analytics is a field whose goal is to make use of data to find ways of improving business. This can be through identifying and solving problems, improving service delivery, planning and strategy, and more.
Over the years, business analytics has been seen as the missing link between IT and the business, where analytics is employed to drive decision-making and profitability.
For this reason, business analytics professionals, in addition to possessing IT, statistics, and data analytics skills, also require business acumen.
Core business analytics processes include:
- Data mining
- Statistical analysis
- Predictive analysis
Top business analytics skills
- Data mining (cleaning and interpretation)
- Data visualization and storytelling
- Analytical reasoning
- Statistical analysis and mathematical skills
- Communication (oral and written)
What is data science?
Rather than solving business problems, data science is an interdisciplinary field that aims at solving the world’s problems across industries through the study and extraction of knowledge and insights from data.
Data science techniques are drawn from various fields, including math and statistical analysis, programming, AI, and machine learning.
Insights drawn from data science processes inform strategic planning and decision-making. The goal of data science is to establish the systems and processes that will facilitate data analysis.
The data science process involves the following steps:
- Gathering data from multiple sources
- Data cleaning and exploration
- Data modeling
- Data analysis and interpretation
- Communication of results
The main difference between data business analytics (BA) and data science is that while BA uses present and historical data to discover hidden trends and insights, data science uses data to predict future trends.
Secondly, data science skills are useful across a wide range of industries, including manufacturing, financial services, healthcare, retail, IT, academia, and more.
Top data science skills are:
- Statistical analysis
- Programming
- Machine learning
- Multivariable calculus and linear algebra (mathematics)
- Data visualization and storytelling
What are the differences between business analytics and data science?
Whilst both BA and data science involve the process of gathering, analyzing, and extracting insights from data, the former is limited to the business and finance industries, while data science applications span a wide range of industries.
Data science is way broader. A data scientist can assume the role of a business analyst but not vice versa.
Here are the main differences between business analytics and data science
| Business Analytics | Data Science | |
| Definition | The statistical study of business historical and present data to discover trends and insights that drive decision-making for business planning and strategy | The science of studying data using statistical analysis, algorithms, and other technologies to draw insights for the purpose of predicting future outcomes |
| Orientation | Is more oriented to statistical analysis | Interdisciplinary involving coding, math, and statistical analysis and other fields. |
| Process | The entire business analysis process is based on statistical analysis | The data science process is based on coding first, and the statistical analysis towards the end |
| Goal | Uncover trends and insights in data that are specific to addressing business needs and solving business problems | Uncover general trends and insights in data |
| Core skills | Business modeling Statistical analysisData analysisData modelingWorkflow modeling Predictive analysis | Mathematics and statistical analysis Data analysis Visualization AI and machine learning Database management Data mining Data warehousing and engineering Programming |
| Data | Uses mostly structured data | Uses both structured and unstructured data |
| Prerequisites | Business acumen (strategy, planning, data analysis, predictive modeling, storytelling, and optimization techniques) | Computer science and programming, algorithms, math, and statistical analysis. Also, AI, machine learning, and other technologies related to data |
| Tools | Tableau, Excel, SQL, Power BI, SAS, Oracle Analytics Cloud, JIRA | Python, R, Keras, PyTorch, Pandas, Matlab, Tensorflow, Numpy |
| Roles | Business analyst, operations manager, solutions architect, project supervisor, senior consultant | Data scientist, machine learning engineer, data engineer, AI specialist, data analyst |
Business Analytics or Data Science– which is better?
Both business analytics and data science play crucial roles in the growth and strategic planning of business as they influence decision-making.
However, data science is a broader field and is applicable across all industries. Are you more passionate about coding, technology, and data? Are you intent on the development of systems, architectures, and infrastructure used for data wrangling, modeling, analysis, and visualization? Are you curious about data development? Consider pursuing a career in data science.
Business analytics is limited to the fields in the business domain like the retail and finance industries.
This field is important for the management and daily operations of the business and facilitates decision-making and strategy, which directly affects business growth and profitability.
Are you passionate about business management and operations, planning and strategy, decision-making, and profitability? Do you love manipulating numbers to discover trends in business that affect business operations? Consider pursuing a career in the field of business analytics.