Amazing possibilities are available in data science with artificial intelligence (AI) and machine learning (ML). Large sets of data, inexpensive storage options, and cloud processing capabilities are enabling computers to make human-like decisions. Across industries, businesses are leveraging these algorithm-based models to save time, reduce costs, enable users, and grow profits.
What’s the difference between Data Science, Artificial Intelligence, and Machine Learning
Data science, AI, and ML can get lumped together, but there are some distinctions to understand. Simply put, AI is a computer doing things that typically would require human scrutiny or reasoning. ML is the application of statistical learning techniques to automatically learn patterns in data. These patterns are used to develop a model to make more accurate predictions about the world. And both terms utilize data science to accomplish outcomes.
With these central terms defined, we recommend using ‘machine learning’ or ‘ML’ to describe data science projects internally because there is sometimes an aura of fear around AI that “the robots are going to take my job.” Although joking (a bit), buy-in from executives is critical to a successful data project, so ML is recommended over AI.
Utilizing Machine Learning for Profit Growth
A recent study showed 78% of companies have already deployed ML, and 90% of them have made more money as a result. Manufacturing and supply-chain management are experiencing the largest average cost decrease, and marketing, sales, product and service development are reaching the highest average revenue gains. Additionally, a McKinsey survey revealed that organizations with a high diffusion of ML had 4-5% higher profit margins than their peers with no ML. Not only can ML reduce your overall costs, but it also enables you to grow your bottom line. If your organization is not utilizing ML, now is the time to start.
From Data to Model
Machine learning is already a staple in many of the functions we utilize daily. Predictive search in Google and within catalogues, fraud detection on suspicious credit card purchases, near-instant credit approval, social network suggestions via mutual connections, and voice recognition are all common today. Behind these intelligent decisions is a model that acts as a function or program. The model is trained on sample data using a machine learning algorithm to learn patterns. Based on the information learned about the sample data, the model is applied to inputs it may or may not have seen before and predicts an outcome.
Traditional programming depends on the written program and the input data it’s fed. The computer runs the program against the data, and you get an output directly tied to the logic or function of the program. Only the data that can be processed by the program gets analyzed, and outliers are removed.
In ML, the computer is still given input data. For example, what you know about your customer – time stamps, demographics, spend, etc. – but it doesn’t have a written program. Instead, it’s given the output you desire. For example, you might want to know which customers churn. Then you build a model by training programmed algorithms to analyze input data and predict an output. Essentially, the model recognizes the correlation between the output results and the input data. Here, the model utilizes algorithms to identify patterns in data that that heavily influence the customer churn score.
In this example, an organization might discover that most customers stop doing business with them after a certain promo ends, or a high percentage of customers who come in through a specific lead gen pipeline don’t stay for long. Using this information, the organization can make informed and specific decisions about how to reduce churn based on known patterns.
All relevant data is taken into account in ML to deliver a more comprehensive story about why things are happening in your organization. Machine learning can quickly affirm or discredit intuition and allow organizations to fail faster, and in the right direction, to meet overall goals more efficiently.
A best practices approach is necessary to streamline the process of introducing, completing, and repeating a data science project. With 2nd Watch Data and Analytics Services, you realize the power of machine learning with the right algorithm selection and model deployment. Contact Us to see how machine learning can positively impact your organization or download our eBook, “Artificial Intelligence and Machine Learning: 3 Steps to Set the Table for Data Science in 2021” to learn about the 3 steps necessary to producing valid and applicable results from your data science project.
– Rob Whelan, Practice Director, Data & Analytics