Back in 2011, Marc Andreessen, the famous entrepreneur and venture capitalist wrote an article for the Wall Street Journal making the bold claim that “Software is Eating the World.”
Looking backwards to connect the dots and seeing the rise of companies like Ola, OYO, Swiggy and not to mention the continued success of Amazon and Flipkart (as they dominate their industries and out compete their former brick-and-mortar competitors), it seems that Andreessen was absolutely correct. Software has been coded into the genetic makeup of the most successful companies of our time. Software is critical to the success of all types of companies those who are disrupting old industries and those who are creating new ones.
At this point you would be thinking don’t all companies use software? And you are absolutely right. Using various types of software is an necessity for almost all businesses today. Businesses use software for Customer Relationship Management (CRM), Accounting, Logistics Management etc. But what makes the software at companies like Amazon, Google and Netflix so successful is Machine Learning. Successful software driven companies go one step ahead to use the data to learn more about their customers. They take decision on actionable data not intuition or gut feeling. This increases the chances of success. In other words, they don’t leave success to chance. Machine Learning is part of the strategy that drives their success.
Like all businesses, what drives revenue at Amazon is very simple: increased customers, increased order sizes, and repeat buys. How they do that? They know a lot about your shopping preference, perhaps more than you know about yourself. At every step of the process, Amazon uses machine learning to optimize. For example, to increase order sizes, Amazon uses machine learning to recommend “similar items” as you browse and make your purchase. It also uses similar techniques to target past customers with new “recommended” offers. If we look closely, Amazon is really just a data-driven marketing machine that sells you everything from books to phones to furniture. Data is the fuel, and machine learning is the “engine”. And software connects both.
So software is eating the world, but machine learning is eating software.
But as a student or professional where do you fit in? First, we would advice you to master the essential tools of data science (i.e., data visualization and data manipulation), and then we strongly encourage you to dive into machine learning. To master Data Science, we suggest to non-engineers to learn R and SAS. These languages are easy to pick-up and operate. While R is open source and free, and SAS is paid (but available to students through University Edition). Both offer a very robust platform for Data Science and Machine Learning. Engineers too can learn R and SAS. They can pick up these languages in no time. An alternate they have is to learn Python, which is an open source language like R. Python is a general purpose language with strong libraries for Mathematics and Statistics making it great for Data Science and especially Machine Learning. There are a pitfalls though. First, python does not have impressive packages for data visualisations like R. Second, R has a larger and more mature online community which makes solving problems easier. So we suggest you to choose your path carefully.
To learn more about R, SAS & Machine Learning visit:
Article on Data Analytics: https://www.wkvedu.com/2018/12/30/3-reasons-employees-use-data-analytics/