Gathering data is the easy part, knowing how to obtain actionable insights from that data is where it starts to get complicated. Businesses more often than not end up with far more data than is comprehensible, increasing the likeliness of being counterproductive and leading to inaction.
Organizations from around the globe have leveraged two primary methods to translate datasets: data mining and machine learning, although these are considered two different areas in artificial intelligence and big data, they have a lot in common and complement each other when adapting to business intelligence protocols that help data scientists and data engineers in optimizing business operations. For example, both machine learning and data mining support the efficient and effective management of inventory protocols, quality controls, maintained management, and operational performance, with little to no human intervention.
Businesses across varying verticals such as logistics, finance, e-commerce, manufacturing, retail, and healthcare employ these techniques to effectively turn raw data into valuable insights that lead to operational, strategic, or statistical data-based decision-making that ultimately impacts the bottom line. Data mining and machine learning techniques have helped in optimizing marketing campaigns, improving operational efficiency, forecasting sales, and more.
Fascinated? In spite of its similarities, you will need different technology stacks, processes, and architectures when leveraging these technologies, depending on your goals and resources.
What is Data Mining?
Data Mining is a manual process of examining complex data warehouses to extract actionable information that can be used to discover new trends, predict data patterns, solve challenges, and mitigate risks. Data mining enables data analysts to spot trends and patterns on a case-by-case basis, it is typically slower than machine learning and requires specialized professionals who are qualified in applying various algorithms to gain intelligence. These meaningful models can bring to light insights and breakthroughs that simplify complex and lengthy processing for business intelligence, typically changing the way in which individuals perform.
What is Machine Learning?
Machine learning is an automated process of discovering new algorithms from data and past experiences, helping computers learn patterns and make predictions. The machine learning method permits machines to learn without human intervention via the design, study, and development of algorithms that enable them to learn and generate their own programming.
Analyzing data with machine learning allows for a more streamlined process of deriving insights on enormous datasets. Businesses commonly automate mundane processes and predict outcomes with the use of machine learning. The perfect example of machine learning is Netflix predicting what movie you may want to watch next based on the viewing preferences of users with similar profiles.
Now that you have a sufficient understanding of Data Mining and Machine Learning concepts, let’s look at their unique traits below.
What do they have in common?
Both data mining and machine learning fall under data science and are used to solve complex problems. More often than not, machine learning is also used to conduct data mining activities, and the data gathered and relationships mapped from data mining can be used to teach machines – once again blurring the lines between each solution.
Additionally, both machine learning and data mining use the same algorithms when discovering data patterns, although their results ultimately differ. With so much in common, it is not surprising that many people use the two terms interchangeably.
Key differences between data mining and machine learning?
How they operate
Data mining relies on big data—or an enormous dataset—to operate, data analysts work to make predictions that optimize workflows and benefit the businesses. Machine learning however works with algorithms rather than raw data and is programmed using languages such as Python for processing data.
Accuracy
The level of human intervention significantly differs between machine learning and data mining. Machine learning is based on artificial intelligence, it uses predictive models, statistical algorithms, and neural networks to teach itself ad replace humans to a certain degree when completing a task. Data mining on the other hand demands human intervention to complete a task, data analysts use data warehouses and pattern evaluation techniques to extract insights from data, leaving room for the possibility of human error and the inability to identify multiple connections or relationships between various data.
Potential for Growth
Data mining is a static process that is unchanging, it incorporates two elements: the database and machine learning, while the database provides data management techniques, it is machine learning that supplies data analysis techniques, therefore it’s likely that one can work without the other. Data mining is also only limited to the knowledge and experience of the users who enter the parameters.
Consequently, machine learning is built for change and growth, it’s designed to adapt and evolve instantly as new information becomes available, adjusting algorithms as the right circumstances manifest themselves, essentially predicting patterns and trends for future planning. Information gathered and processed via data mining can help a machine learn, it’s not a dependency but more of a convenience.
Which one is better for Data Science?
Almost every day more and more of our world turns to technology and digital solutions to solve complex problems. The continued dominance of Big Data ensures the need for data mining, whilst the continued drive and demand for smart machines means that machine learning is far from a dying trend.
Data mining helps businesses analyze and understand trends from large datasets to gain insight and make better business decisions. However, simply analyzing historical data may not be the answer for some companies, machine learning allows data scientists to teach computers to learn, evolve, and adapt through algorithms, to continuously analyze large amounts of data and resolve the issues automatically. Essentially distilling vital, accurate insights on an ongoing basis, rather than taking massive sets of data and retroactively spotting trends and patterns.
Which offers the most potential, you may wonder? There is no clean-cut answer, ideally, organizations would need to implement both the techniques rationally, data mining for problem identification and machine learning for obtaining an accurate solution. In the nutshell, both technologies succeed in achieving change that enables automation in every task whilst minimizing human involvement.