Text Transcript

Big Data is not just about volume, extract business value by analyzing large volumes of data. What we need to remember is it is all about the data analytics.

Key Applications for Big Data

Data analytics isn’t just about volume

1. Resource Optimization

Utilize analytics to determine the amount of resources to be allocated for given different scenario.

Once you have enough truthful data collected, data that is not manipulated, you are able to optimize your resources.

2. Explore and Discover New Things

Using analytic results to identify the features of new products, measure the cost to expand into new markets.

Whenever I teach a full-length Big Data courses, I usually teach students to look at the data and feel it first. This means that you understand WHAT happens before you start asking WHY it happens. A lot of time, companies, and bosses have expectations of what the data can do, such as increasing or reducing something to bring benefits to the company.

However, before all that can happen, you need to explore the new territory or develop new features for the users. For example, if you are running a new website or a mobile phone company, without proper data to identify something relevant for you, the product manager often waste a lot of time cracking their heads to know what they should do. Many companies spend a lot of money on the Research and

Development department in order to carry out more research and experiments. But what is actually the most important key point for identifying what your users want, is by understanding your users.

Users often are not sure what they want. Data can become helpful in understanding how your users use your application, which parts or steps do they often get stuck for too long. These are the things that can be useful for you to understand and improve your features.

3. Fraud Detection and Prevention

Building analytical models for machine learning to improve the reliability of your decision-making process.

This is not restricted to the finance or insurance industry. Any industry can use this to detect fraud.

A very interesting chapter in the book “Freakonomics”, there was this guy that used data analytics to determine who will steal the bagels in order to understand who have a higher tendency to cheat.His business model was like this : every morning he brings a bag of bagels, he put the bagels with a money box in front of it with the price stated. He used data analytics to determine who are the people that tend to cheat more by eating without paying. He learned that those that tend to cheat are usually the white-collared individuals. So these are some interesting findings behind this.

His business model was like this: every morning he brings a bag of bagels, he put the bagels with a money box in front of it with the price stated. He used data analytics to determine who are the people that tend to cheat more by eating without paying. He learned that those that tend to cheat are usually the white-collared individuals. So these are some interesting findings behind this.

This book is highly recommended in relations to data science and economics. You can read it if you are interested in learning more in this field.