The terms and job titles in the data field are extraordinarily large, such as business analysis, data analysis, data science, etc.
They often stun everyone, so this article we will talk about all of them, especially the differences.
Business Analysis VS Data Analysis
Generally, data analysis means that using data to analyse a XXX problem. The pipeline includes data collecting, data warehousing, data cleaning, data visualisation, etc.
As we can see, there is a blank in the sentence.
Actually, data analysis can be applied to different areas, and that is also the meaning of ‘XXX’ .
It can be academic or commercial.
If ‘XXX’ is commercial, then data analysis will equal to business analysis. In other words, the business analysis is an application of data analysis.
There is a main difference between data analysis and business analysis, which is the data sources.
The data source of data analysis positions is often based on the company websites, Apps, ERP system, etc.
And those job descriptions will require candidates master SQL, Python or R.
However, the data collected from those platforms often exists several problems, mainly the data quality, such as some missing data or data noise.
Those are caused by weak IT infrastructures and ‘econnoisseurs’ who want to register more user accounts.
However, the data sources of business analysis positions not just include those internal data collected by companies but also include a large amount of external sources. For example:
- Industry studies
- Qualitative interviews
- Quantitative interviews
- Internal data
Business Analysis VS Data Science
Because of Alpha Go, Artificial Intelligence is well-known to everyone.
However, the successful area of AI is not related to data analysis.
They focus on the computer vision and natural language processing, and their industrial applications are mainly in security and supply chain.
However, the usage of algorithms in commercial industry is limited because some departments in the company are hard to be represented by data and algorithm models.
This causes that algorithm can only solve the specific problems.
- Firstly, algorithm is related to users directly. The famous example is risk control. Because the factors contributing to the user credits are easy to be built by an algorithm model, commonly logistic regression.
- Secondly, forecasting algorithms. Business Intelligence is highly required to do forecasting.
- Thirdly, dimensionality reduction algorithms. It is often to reduce dimensions so it is easy to evaluate a problem, such as new products.
In summary, algorithms are useful in business intelligence, however, it cannot replace it.
Business Intelligence is an application in commercial problems and algorithm models are tools to solve specific problems.
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