Data Science and Machine Learning [#2] – Business Analytics, Data Analytics and Data Science

In this post, I will show you a diagram that explains how the most popular disciplines in the data science field intertwine with each other.

Let’s start with a list of terms that cover some aspects of the commercial sphere (business case studies, preliminary reports of qualitative analysis, data preliminary reports with visual aids, creating dashboards and predicting sales). They are all part of the business world.

How many of these terms involve working with data? Some of them, because some business activities are data-driven, while others are subjective or experience-driven.

You will need data to create a preliminary report, a visual representation of the performance of your company, to do the last year’s business dashboard and also a forecast for the future sales of your company. The blue rectangle place the activities for which it is essential to have data available.

Business case studies are real-world experiences of how business people and companies succeed or fail, you don’t need a data set to learn from business cases. Thus it will remain purely in the orange rectangle.

The same can be said for qualitative analytics because it’s all about using your intuition and the knowledge of the market to help in the future planning process.

Some of the mentioned terms refer to activities that aim to explain past behaviour while others refer to activities used for predicting future behaviour. The black line on the next figure represents the present, and all the terms that are on the right side of this line will regard analytics future planning and forecasting. And those that are on the left side of the line will be related to the analysis of past events or data.

While learning from business case studies is part of your business analysis, perfect business case studies examine events that have already happened. For instance, one can learn from them and attempt to prevent making a similar mistake in the future.

Qualitative analytics includes working with tools that help predict future behaviour. Therefore must be placed on the right side.

It is common practice to use the encompassing term business analytics to refer to both the analyses and analytics.

Preparing a report or dashboard is always a reflection of past data, so these terms will remain untouched. Forecasting though is a future-oriented activity so we can move it to the right of the black line but not too much because it must still belong in the sphere of business. So it must be in the area where business analytics and data intersect.

Like in the business case, it is common practice to use the encompassing term data analytics to refer to both the analyses and analytics.

Data science is a discipline reliant on data availability while business analytics does not completely rely on data. However, data science incorporates part of data analytics, mostly the part that uses complex mathematical-statistical and programming tools.

Consequently, the green rectangle representing data science on our diagram will not overlap with data analytics completely but it will reach a point beyond the area of business analytics.

Does this mean that the preliminary data reports, dashboards, and sales forecasting are of interest to a data scientist.

Now, let’s think of an example of a discipline that belongs to the field of data science and is considered data analytics but not business analytics. The injection machine parameters optimization. Because some of the parameters (injection time, retraction time, etc.) vary with external factors such as temperature, humidity, among others. This is a perfect fit for this sub-area because data science can be used to improve the accuracy of parameters based on data from previous years.

Finally, for example, digital signal processing involves data analysis, but it doesn’t involve data science or business analysis.

Digital signal is used to represent data in the form of discrete values which is an example of numeric data. Therefore data analytics can be applied to a digital signal in order to produce a higher quality signal and that’s what digital signal processing is all about.

The next post will continue with the remaining disciplines related to the world of business.


[1] Udemy – The Data Science Course 2020: Complete Data Science Bootcamp – 365 Careers

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