Business Understanding

Business understanding is the main and first step for doing machine learning in any platform or languages. Not all business problem can be addressed by machine learning approaches. There are some proposed categories for machine learning such as “Supervised Learning” and “Un-Supervised Learning”.

Supervised Learning is about when we identify both input and output variable while unsupervised learning is when we just have input.

Another machine learning classification has three major groups as

·        Descriptive analysis

·        Predictive analysis

·        Prescriptive analysis.

The descriptive analysis uses mainly unsupervised learning approaches for summarizing, classifying, extracting rules to answer what happens was happened in the past. While Predictive analysis is about machine learning approaches for the aim forecasting future data based on past data. And finally, prescriptive analysis use optimization and recommendation algorithms to answer possible outcomes.

However, there is other analytics like diagnosis analytics that also able to help people to get a better understanding of their business. According to the Gartner [1], these four analytics approaches help decision makers in the organization to make a better decision.

Each of this analysis (above figure) provides some values for the company and implementing them needs some skills level. Descriptive analysis is the easiest one that does not need a high level of skills and can be done with different tools. While diagnosis and discovery analysis able to provide more insight on data and needs more level of skills.

 Descriptive analysis

Descriptive analysis is the main approach to understand the data and existing patterns. In the Descriptive analysis can be used for understating the structure of the data, existing rules or patterns in data and so forth. Descriptive analysis can be categories into four main groups.
• Operational Reports
• Statistical Analysis
• Data mining approaches
Operation reports are mainly about the traditional reports that are about the analysis of the past data. These reports mainly use to judge the performance of companies.

Below figure shows a report that illustrates the average total hourly rate of labor based on different industries and income groups.

This report provides a brief description of the hourly rate of people in different industries.

The other reports related to some Statistical reports that show some statistical facts about data.

Statistical analysis is another way of doing descriptive analysis which able to help us a better understanding of the statistical behavior of data. There is various statistical analysis such as Univariate Analysis that we consider each column separately, such as a summary of data and data distribution. This report provides a brief description of the hourly rate of people in different industries. The other reports related to some Statistical reports that show some statistical facts about data. Statistical analysis is another way of doing descriptive analysis which able to help us a better understanding of the statistical behavior of data. There is various statistical analysis such as Univariate Analysis that we consider each column separately, such as a summary of data and data distribution. As you can see in the below picture we able to see the average hourly rate of the employee as a histogram to see how is the distribution, also we able to see the minimum, maximum, average. Median of this data in box plot or in a table format. The boxplot chart shows the data has been distributed smoothly as the median (the middle point) in the same as data average. 

The other statistical analysis is about the Bivariate correlation and regression between two different data column.

In the next Post, I will talk about the predictive and prescriptive analysis.

Leila Etaati

Trainer, Consultant, Mentor

Leila is the first Microsoft AI MVP in New Zealand and Australia, She has Ph.D. in Information System from the University Of Auckland. She is the Co-director and data scientist in RADACAD Company with more than 100 clients in around the world. She is the co-organizer of Microsoft Business Intelligence and Power BI Use group (meetup) in Auckland with more than 1200 members, She is the co-organizer of three main conferences in Auckland: SQL Saturday Auckland (2015 till now) with more than 400 registrations, Difinity (2017 till now) with more than 200 registrations and Global AI Bootcamp 2018. She is a Data Scientist, BI Consultant, Trainer, and Speaker. She is a well-known International Speakers to many conferences such as Microsoft ignite, SQL pass, Data Platform Summit, SQL Saturday, Power BI world Tour and so forth in Europe, USA, Asia, Australia, and New Zealand. She has over ten years’ experience working with databases and software systems. She was involved in many large-scale projects for big-sized companies. She also AI and Data Platform Microsoft MVP. Leila is an active Technical Microsoft AI blogger for RADACAD.