With the emergence of digitization, every organizations is looking for ways to improve some of their business processes and also reimagine some of them. There is a big desire within organizations to tap into big data sources (internally and externally) and process them to come out with predictions which were not possible few years ago. With multiple technologies in the market, people are sometimes confused with the differences between Machine Learning, Predictive Analytics and Robotics Process Automation(RPA) and use these terms interchangeably. In this blog, I am going to try my best to explain what these terms are and where you could best apply them.
Artificial Intelligence is broadly referred to as the capability of Machines being able to imitate humans. Machine Learning(ML) is a current application of AI where machines are able to learn from data without being explicitly programmed. Even though ML has been around for so many years, its gained lot of attention in recent days. The reason is a combination of the below three things:
It’s now become more easy to apply these ML algorithms on top of big data sets and leverage the hardware to process the data and provide a quick output. ML is widely used for classification models (to filter emails as spams or not spam), recommendation engines, image/facial recognition, chatbots etc.
Machine Learning models learn and adapt with changes in training data making them more reliable in real-time predictions.
Predictive Analytics is a subfield of Machine Learning. It’s all about making predictions for future event. Most common technique is the Linear Regression where we have two variables and see how one can impact the other. For example, an organization might be interested to know the relationship between advertising and sales. Its important to also understand that there are few overlaps with Machine Learning as you could do Linear Regression in ML too. However, when you want to use advanced algorithms like decision tree, they are only possible with ML.
Predictive Analytics is very much focused on the goal of computing the value of a particular variable at a point in the future. Unlike Machine Learning, Predictive Analytics cannot adapt itself with the change in data as ML is smart enough to learn and find new patterns with the changing data. PA also requires a degree of expert involvement to validate and test associations.
Robotic Process Automation (RPA) is intended to automate repetitive tasks. It differs from AI in the sense that you have to always provide RPA with set of instructions to follow. Let’s say for example, there are a dozen emails which you receive with a PDF attachment. You could use a RPA software to extract the PDF contents from each email and post them as invoices to a web form in a SAP system. Another common example in banking sector is when creating a new customer account. This usually involves taking the customer data and punching it into several systems before actually onboarding the customer. RPA can significantly reduce the overall time to complete a particular business process E2E. It heavily relies on a rules framework. With the rise of Machine Learning, many RPA vendors have already started to incorporate Machine Learning into some of the tasks which the RPA software tries to accomplish, thereby creating a connection between doing and thinking in an automated environment.
Machine Learning
Artificial Intelligence is broadly referred to as the capability of Machines being able to imitate humans. Machine Learning(ML) is a current application of AI where machines are able to learn from data without being explicitly programmed. Even though ML has been around for so many years, its gained lot of attention in recent days. The reason is a combination of the below three things:
- Massive increase in computation power. We now have Graphical Processing Units(GPUs) which comes with thousands of cores to support parallel processing of data.
- New and advanced Deep learning algorithms
- Rise of Big data scenarios in organizations
It’s now become more easy to apply these ML algorithms on top of big data sets and leverage the hardware to process the data and provide a quick output. ML is widely used for classification models (to filter emails as spams or not spam), recommendation engines, image/facial recognition, chatbots etc.
Machine Learning models learn and adapt with changes in training data making them more reliable in real-time predictions.
Predictive Analytics
Predictive Analytics is a subfield of Machine Learning. It’s all about making predictions for future event. Most common technique is the Linear Regression where we have two variables and see how one can impact the other. For example, an organization might be interested to know the relationship between advertising and sales. Its important to also understand that there are few overlaps with Machine Learning as you could do Linear Regression in ML too. However, when you want to use advanced algorithms like decision tree, they are only possible with ML.
Predictive Analytics is very much focused on the goal of computing the value of a particular variable at a point in the future. Unlike Machine Learning, Predictive Analytics cannot adapt itself with the change in data as ML is smart enough to learn and find new patterns with the changing data. PA also requires a degree of expert involvement to validate and test associations.
Robotic Process Automation
Robotic Process Automation (RPA) is intended to automate repetitive tasks. It differs from AI in the sense that you have to always provide RPA with set of instructions to follow. Let’s say for example, there are a dozen emails which you receive with a PDF attachment. You could use a RPA software to extract the PDF contents from each email and post them as invoices to a web form in a SAP system. Another common example in banking sector is when creating a new customer account. This usually involves taking the customer data and punching it into several systems before actually onboarding the customer. RPA can significantly reduce the overall time to complete a particular business process E2E. It heavily relies on a rules framework. With the rise of Machine Learning, many RPA vendors have already started to incorporate Machine Learning into some of the tasks which the RPA software tries to accomplish, thereby creating a connection between doing and thinking in an automated environment.
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