Beyond the Buzz: The Executive’s Straight-Talk Guide to Machine Learning
In an increasingly digitized business landscape, organizations are constantly seeking ways to derive more value from their data. You’ve heard it said before that data is the new “oil” driving value throughout organizations. While you may have heard the buzz around machine learning (ML), you might still be wondering how it fits into your business strategy. How can you use ML to change your valuation and how do you calculate/measure its success?
In this post, we’ll introduce machine learning at a high level and provide a basic understanding of its potential for strategic decision-making at the executive level.
What is Machine Learning?
Machine learning is a subset of artificial intelligence (AI), where computer algorithms are designed to autonomously learn from data and information. In essence, it's the process of teaching a machine how to make predictions or decisions without being explicitly programmed to do so.
Let's consider a simple analogy. Think of machine learning like training a dog. You don't explicitly program the dog's mind; instead, you provide it with rewards when it performs correctly, and over time, the dog learns to execute the behavior that leads to rewards.
Data: The Fuel of Machine Learning
If machine learning is the engine that drives intelligent decision-making, then data is undoubtedly the fuel that powers this engine. Without data, machine learning is akin to a car without gas—it simply won't go anywhere. And you want to go somewhere!
Data: Building the Foundation
Machine learning algorithms learn from data. They identify patterns, learn from experiences, and make predictions based on the information available to them. The quality, quantity, and diversity of this data directly impact the performance of the machine learning model.
Quality: High-quality data is clean, consistent, and accurate. Poor data quality can result in inaccurate models, leading to incorrect predictions or decisions. It’s like teaching a child with incorrect information; their understanding and subsequent application of the knowledge will be flawed.
Quantity: Machine learning algorithms improve with experience, and in the context of machine learning, experience translates to more data. More data provides a larger foundation for the algorithm to learn from, often resulting in more reliable and accurate predictions.
Diversity: To make well-rounded decisions, algorithms need a diverse dataset that represents different scenarios, variables, and outcomes. Without diversity, models can become biased or perform poorly when encountering unfamiliar data.
Getting the data just right is a significant amount of time and effort—sometimes, the majority of it!—in machine learning.
The Role of Data in Different Learning Approaches
Data's importance is highlighted further when considering different types of machine learning.
In supervised learning, the algorithm learns from a labeled dataset. This dataset serves as the teacher, providing the correct answers (or labels) that the model uses to train itself. The quality of this training data directly influences the model's ability to make accurate predictions.
In unsupervised learning, the algorithm is given unlabeled data and must find patterns or structure within it. Here, the diversity and quantity of data are particularly important as they influence the model's ability to identify meaningful relationships.
In reinforcement learning, the algorithm learns by interacting with its environment and receiving rewards or penalties. The data in this case comes from the environment itself, and the quality of learning depends on the richness and complexity of the environmental data. You can learn more about reinforcement learning in this blog post.
Get the Data Now
The best time to plant a tree is ten years ago. The next best time is today. Getting the data as early as possible is a huge benefit to your company. Even if you aren’t planning to implement ML any time soon, the larger and better your corpus is now, the better prepared you’ll be in the future.
Start collecting data now by adding fields to the user signup/registration process today so you’ll have data to analyze in the future. You can use tools like Pendo and Google Analytics to track and report on user interactions within your product.
Pendo Analytics
Not only will you benefit from collecting this data upfront, but your users will also enjoy a better and more tailored experience. The insights you garner will help you optimize your product for current and future users.
Supervised vs. Unsupervised Learning
In the realm of ML, algorithms are often categorized into two main types: supervised learning and unsupervised learning. These terms may sound intimidating, but their concepts are quite straightforward. Below we’ll break down these two machine learning methods and how they differ.
Supervised Learning: Learning with a Mentor
Supervised learning can be likened to a student learning under the guidance of a teacher. In this context, the “teacher” is the dataset you provide the machine learning model, complete with inputs and the correct outputs. The “student” is the machine learning algorithm, striving to learn from this dataset.
The goal of supervised learning is for the algorithm to be able to apply this learned relationship to new, unseen data. The “supervision” comes in the form of labeled data, where both the input (the features of the data) and the output (the label or what we're predicting) are provided. This is used in problems where the aim is prediction, such as predicting house prices based on various features or determining whether an email is spam or not.
Most of what you hear in the news with the huge breakthroughs and success in ML is supervised learning. If you need help with your data pipeline, whether that’s using best practices to collect it or ensuring it’s labeled correctly, Crafted is here to help. From data augmentation, to employing mechanical turks, to identifying existing open source data sets and fine tuning models to them, we’ve done it all.
Unsupervised Learning: Learning on its Own
Contrary to supervised learning, unsupervised learning is like a student learning on their own without explicit guidance. Here, the machine is given a set of data and must find patterns and relationships within this data on its own.
The key differentiator from supervised learning is that the data provided to the algorithm is unlabeled; we only provide the input and ask the algorithm to find patterns or structure within this input. This can be used for clustering, or grouping similar instances/examples together (things that look alike); anomaly detection, or spotting unusual instances (instances with data that doesn’t fit the others); or dimensionality reduction (simplifying the input data without losing too much information).
The Art of Fine-Tuning: Enhancing Existing Machine Learning Models
In the world of machine learning, creating a model from scratch can be a time-consuming and resource-intensive process (read: it can cost millions of dollars!) It involves defining the architecture of the model, initializing parameters, and then training the model on a large dataset over multiple iterations. But what if there was a more efficient way to create a robust machine learning model?
This is where the concept of fine-tuning comes in. Fine-tuning is a process that involves taking a pre-trained machine learning model—one that has already been trained on a large-scale dataset—and tweaking or "fine-tuning" it for a specific task. Here are a few reasons why you might want to fine-tune an existing model rather than training one from scratch:
Improve the efficiency and effectiveness of the model
Leverage the data used in the pretrained model (data you might not have access to or data that took significant effort in cleaning)
Significantly reduce the training time and cost (sometimes on the order of months and millions of dollars); you’re only changing a few layers instead of potentially hundreds
Leverage the features and knowledge of the pretrained model
It's akin to buying a pre-furnished house and then adjusting the decor to your tastes, rather than building a house from scratch. Understanding these benefits to fine-tuning can guide strategic decisions about resource allocation in machine learning initiatives, helping your organization achieve better results with less investment.
Practical Applications of Machine Learning
The applications of machine learning are vast and span across industries. It's an incredibly versatile technology that can deliver significant benefits in a business context. The key is knowing how to implement it effectively.
Enhanced Customer Experience
Personalized Recommendations: Companies like Netflix and Amazon have used machine learning to provide personalized recommendations to their customers. By analyzing a user's behavior and preferences, machine learning algorithms can predict what products or content a customer may like, thereby enhancing the user experience and potentially increasing revenue.
Chatbots and Virtual Assistants: Machine learning powers intelligent virtual assistants that can handle customer queries around the clock. They can learn from each interaction, improving their understanding and ability to provide relevant information over time.
Optimized Operations and Increased Efficiency
Predictive Maintenance: In manufacturing, machine learning can predict equipment failures before they occur. Algorithms analyze data from machines to detect patterns indicating potential faults, allowing maintenance to be scheduled proactively and prevent unexpected downtime.
Supply Chain Optimization: Machine learning can analyze multiple variables in the supply chain, including demand, delivery times, and supplier performance. This can lead to more accurate forecasting, better decision-making, and ultimately, an optimized supply chain.
Risk Mitigation and Security
Fraud Detection: In banking and finance, machine learning can detect patterns that might indicate fraudulent transactions. These algorithms can analyze vast quantities of transaction data far quicker than humans and can therefore respond to potential fraud in real time.
Cybersecurity: Machine learning can analyze network traffic and identify unusual patterns that may indicate a cyber threat. It can also learn from each attack, constantly improving its ability to detect and respond to threats.
Making Data-Driven Decisions
Market Analysis and Prediction: Machine learning algorithms can analyze complex and vast amounts of market data to identify trends and make predictions. This can help executives make data-driven decisions about market entry, product development, and more.
Customer Churn Prediction: By analyzing customer behavior, machine learning can predict which customers are likely to churn. This allows businesses to proactively address issues and implement retention strategies.
Sales Forecasting: Machine learning can help businesses predict future sales trends based on historical sales data and other influencing factors. This can help in planning production, managing inventory, and setting realistic revenue targets.
Conclusion
While these examples provide a snapshot of machine learning's potential applications, the possibilities are nearly limitless. It is important to remember that successful implementation of machine learning relies heavily on having clear goals, quality data, and skilled teams to manage and interpret the outcomes.
As an executive, understanding how machine learning can be applied within your business context will be key in leveraging this powerful technology to drive strategic growth.
In short, steps for successfully leveraging machine learning to increase your success, company’s valuation and the valuation multiple:
Start collecting, cleaning, organizing and labeling your data
Build the right team - Crafted is here to help!
Identify a clear, measurable, business goal or target and execute against that
Start small and deliver iteratively
Profit!
