The Need for Machine Learning

Why is it needed?

1. Too much data, and too many things that can be done with the help of this data which is not being done currently. A great opportunity in each and every field.
2. Machine Learning is not a subject in itself, it is always associated with some domain (inface most of the domains which have data can leverage this). Its actually 'Machine Learning for Retail' or 'Machine Learning for Media' or 'Machine Learning for Bioinformatics' or 'Machine Learning for Astronomy'. The possibilities are endless.
3. Imagine you have an e-commerce store and 10 customers buy from you (just saying). You know what each customer is like and what they prefer to buy in your head. What if you become hugely successful and the number of customers go up to 10 Million? Also the number of products you are selling go up to 100,000. There is no way you can make use of this data without using some advanced technologies.
4. Its not Rocket Science : Well, you are not developing a system like the Skynet or the Matrix (in that case, it is much more than Rocket Science). With dedicated effort, you can master the algorithms and use them with your own data in your own field. Most of the algorithms are not dependent on the domain (but their interpretation is). 
5. Availability of Technology : 10 years back, if you wanted to analyze 100GB of data, you had to make a lot of investments in the tools like SAS or buying up huge amount of server space just to store the data. With the advent of OpenSource Technologies like Hadoop, Hive, Pig etc, OpenSource and free programming languages like R, Python, Octave etc and utilizing cloud technologies, you probably just need a good computer to run your Machine Learning Algorithms.
6. Huge Scope: Data Scientist is the sexiest job of the 21st century. If you want to remain relevant in the job market, you need to learn Machine Learning to become a data scientist.

Having said all this, its not easy. A lot of effort, dedication and practice is required to master this. It seems difficult at first, then a bit easy, then insanely difficult and once you understand everything it becomes insanely easy.