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Business Analytics vs Data Science.
Business Analytics and Data Science, both are two different terms which are always debatable by a layman. However a professional should use this correct as the impact of this on a business is definitely very large.
In this article we will go thru the difference between the two in detail.
In simple words:
"Business Analytics is analysis of Data to make Business decisions for an organization."
"Data Science is study using Data using statistics which provides key insights but not busniess changing decisions."
Business Analytics Data Science
Analysis of Data Data study using statistics.
Uses Structured Data Structured/Unstructured Data
No Coding is involved Coding is involved to derive from Algorithm
Future is Coginitive Analysis Future is AI and Machine Learning
Useful for Business decisions. Not useful to take Business decisions.
Both the above streams are nische skills and have very competitive markets with very handsome packages to the professionals.
Business Analytics:
Statistical study of Data, mostly on structured data is known as Business Analytics. It provides solutions to many business problems in an organization. Business Anaytics is vital in our digitally-driven world as it essentially gives you an additional sense: a commercial vision that can help you see and process far more than the information that presents itself on the surface. And there are business examples and insights out there that demonstrate that every notion.
Benefits of Business Analytics:
Improves Business decision making
Enhances Productivity
Unleash the Business Insights
Boosts Finances
Increase in Sales
Streamline business processes
Motivates Employee Performance
Better Inventory Control
Insights on Marketing Strategies
Customer Focus.
Data Science:
The term science is usually synonymous with the scientific method, and some of you may have noticed that the process is very similar to the process characterized by the expression, scientific method.
Data Science Life Cycle:
Make Observations
Think of Interesting Questions
Formulate Hypotheses
Develop Predictions
Gather Data for Predictions
Develop Theories.
And refine the process from step 1 again.
For statistics, mathematics, algorithms, modeling, and data visualization, data scientists usually use pre-existing packages and libraries where possible. Some of the more popular Python-based ones include Scikit-learn, TensorFlow, PyTorch, Pandas, Numpy, and Matplotlib.
Data scientists can have a major positive impact on a business’ success, and sometimes inadvertently cause financial loss, which is one of the many reasons why hiring a top notch data scientist is critical.
Benefits of Data Science:
Identify and predict problem areas
Customized recommendations
Optimize business process
Automate predictions
Helps in Mitigation of problem areas.
By now you should have got a fair amount of data to differentiate the above, if you still have any specific question I am more than happy to take some questions from the comments section.
ciao,
Rajeev Jagatap,
Business Analytics Consultant
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