According to a study by IDC titled Data Age 2025, the worldwide data generation will grow to 163 Zettabytes by the end of 2025 which is 10x the amount of data generated in 2017. Data is growing so fast and so is the tech jargon associated with it. When talking about data, words like Data analytics, data science, machine learning, data mining, and big data are tossed around in every boardroom discussion, meeting, big data conference and newsletters. This creates confusion amongst people on their real meaning. There is a distinction in various similar-sounding terms be it data science vs machine learning, data mining vs machine learning, data mining vs data science, or anything else. We will focus on two popular terms people often confuse with Data Mining vs Machine Learning.
Data Mining vs Machine Learning – What is the Difference?
Before we get started it is extremely important to answer these two questions “What is Data Mining?” and “What is Machine Learning?”. The meaning of mining and learning are poles apart and each is different in its own applications. However, data mining and machine learning form a close associative relationship as both are deeply rooted in data science and learn from data for better decision making. As in there are a few similarities between data mining and machine learning – both concepts are an integral part of the analytics process, both learn from data to improve decision making, both work perfectly with accuracy when there are large amounts of data and both are good at pattern recognition. These similarities often make people confuse between the two and think they are similar. So to all the confused people (even the not so confused souls can read it though) out there, this article on Data Mining vs Machine Learning will make it easy for you to understand the concept of data mining, machine learning, and the difference between the two. Let’s go further and explore what is the difference between data mining and machine learning. For beginners, first, let’s get an idea of what these two terms are:
What is Data Mining?
Data mining is at the heart of business strategies today be it banking, retail, communication, marketing, or any other industry. Data mining helps organizations drill down into transaction data and other web data to identify customer habits and preferences, determine the perfect place for product positioning, study the impact on customer satisfaction, sales, and revenue generation. The Economic Times defines data mining as “the process used to extract usable data from a larger set of any raw data”.
Data mining also referred to as Knowledge Discovery in Data is a technique to identify any anomalies, correlations, trends or patterns among millions of records (particularly structured data) to glean insights that could be helpful for business decision making and might have been missed during traditional analysis. The main goal of data mining is to find facts or information that was previously ignored or not known using complicated mathematical algorithms. Just like any other analysis technique it just increases the accuracy of analysis but there is never 100% certainty of the outcome. Data mining imbibes its techniques from statistics, artificial intelligence, machine learning, and database systems. Data mining leverages the power of different pattern recognition techniques from machine learning to extract knowledge and unknown interesting patterns from large data sets. Data mining finds great applications in the research field.
A good application of data mining is its extensive use in the retail industry to identify trends and patterns. It helps with better market segmentation by predicting which customers are most likely to unsubscribe from a product or service or what kind of products interest a specific customer based on their search patterns to direct personalized marketing campaigns to specific customer segments. Not just this if the retailers have enough data on customer churn, a data mining algorithm can help identify new associations or relationships to predict future customer churn.
What is Machine Learning?
Machine learning is a subset of artificial intelligence that gives computers the ability to learn on its own without being programmed explicitly and improve with experience. Machine learning can be best related to math geeks who work with ‘n’ number of practice problems to find methods for solving them by identifying patterns between the information given in the problems and their associated solution. What matters in a machine learning algorithm is to identify the most effective data i.e. the practice problem that can be given as input to the most effective machine learning algorithms (learning styles) to generate the best performance. Machine learning is all about eliminating the human element from learning to make machines intelligent and smarter. Machine learning is one of the exciting technologies today that finds applications in day-to-day life be it traffic predictions, product recommendations, fraud detection, or your very own personal assistants Alexa and Siri.
The three integral components of machine learning that make a machine self-learn are –
- Data – Having a good dataset is extremely important for any machine learning algorithm to function with accuracy and efficiency. Collating qualitative data is difficult that most of the organizations are willing to reveal their machine learning algorithms but not the datasets. The more the data the better is the outcome of the algorithm.
- Features – The fundamental building blocks of datasets i.e. the characteristics of the object you are trying to analyse. Quality of the features in a dataset is directly proportional to the quality of insights gained when a dataset is used for machine learning. So if you have crappy data then even the best machine learning algorithm will not produce the right outcome.
- Algorithms – Each business problem can be solved differently in the machine learning world. The process or the set of rules applied to solve these problems is often referred to as a machine learning algorithm. There are tons of algorithms that can be applied to solve a given problem. There are several ML algorithms that fit a problem but choosing which one fits better is important. The machine learning algorithm you choose has a major impact on the accuracy, and performance of the final machine learning model.
Data Mining vs Machine Learning – Understanding the Differences
Though both data mining and machine learning involve learning from data for better business decision making but how they go about doing it is different.
1. Data Mining vs Machine Learning – The Goal
Originating in the 1930s, the goal of data mining is to identify the relationship and association between the attributes in a dataset to predict outcomes or actions. Originated in the 1950s, machine learning involves gaining knowledge from past data and making use of that knowledge to make future predictions, all this without being explicitly programmed. Data mining is a cross-disciplinary field (data mining uses machine learning along with other techniques) that emphasizes on discovering the properties of the dataset while machine learning is a subset or rather say an integral part of data science that emphasizes on designing algorithms that can learn from data and make predictions. So, data mining requires machine learning but the vice-versa is not true.
2. Data Mining vs Machine Learning – Manual vs Automatic
Data mining is more of a manual technique as the analysis needs to be initiated by humans. Moreover, data mining lacks self-learning ability and follows a predefined set of rules and conditions to solve a business problem. On the contrary, in machine learning, once the rules are given the process of learning and refining to extract knowledge is automatic. A machine becomes intelligent by itself with learning and does not require human intervention. Data mining cannot work without the same. This makes machine learning less error-prone and more accurate over data mining.
3. Data Mining vs Machine Learning – Existing Dataset vs Trained Dataset
Data mining discovers anomalies, patterns or relationships from existing data (like that of a data warehouse) while machine learning learns from the trained datasets to predict the outcomes. A machine learning algorithm is iteratively fed with the trained dataset to make predictions near to perfect.
What to expect with the future of data mining and machine learning?
As an increased number of businesses look to become more predictive and the amount of data increases, data mining and machine learning are here to stay as they have the power to impact business decisions through data patterns. The future is bright for professionals who can help organizations scale up their analytical abilities and decision making. For professionals looking to make a career transition, now is the time to upskill and land a job in the machine learning field. Take advantage and make the most of the data mining and machine learning opportunities that exist today. Explore a career in machine learning with Springboard’s 1:1 mentor-led project-based machine learning career track to prepare for a successful and rewarding career.