Data mining isn’t a new invention that came with the digital age. The concept has been around for over a century, but came into greater public focus in the 1930s.
According to Hacker Bits, one of the first modern moments of data mining occurred in 1936, when Alan Turing introduced the idea of a universal machine that could perform computations similar to those of modern-day computers.
Forbes also reported on Turing’s development of the “Turing Test” in 1950 to determine if a computer has real intelligence or not. To pass his test, a computer needed to fool a human into believing it was also human. Just two years later, Arthur Samuel created The Samuel Checkers-playing Program that appears to be the world’s first self-learning program. It miraculously learned as it played and got better at winning by studying the best moves.
We’ve come a long way since then. Businesses are now harnessing data mining and machine learning to improve everything from their sales processes to interpreting financials for investment purposes.
Both data mining and machine learning are rooted in data science. They often intersect or are confused with each other, but there are a few key distinctions between the two. Here’s a look at some of the differences between data mining and machine learning and how they can be used.
One of the key differences between data mining and machine learning is how they are used and applied in our everyday lives. For example, data mining is often used by machine learning to see the connections between relationships. Uber uses machine learning to calculate ETAs for rides or meal delivery times for UberEATS.
Data mining can be used for a variety of purposes, including financial research. Investors might use data mining and web scraping to look at a start-up’s financials and help determine if they want to offer funding. A company may also use data mining to help collect data on sales trends to better inform everything from marketing to inventory needs, as well as to secure new leads. Data mining can be used to comb through social media profiles, websites, and digital assets to compile information on a company’s ideal leads to start an outreach campaign. Using data mining can lead to 10,000 leads in 10 minutes.
Machine learning embodies the principles of data mining, but can also make automatic correlations and learn from them to apply to new algorithms. It’s the technology behind self-driving cars that can quickly adjust to new conditions while driving. Machine learning also provides instant recommendations when a buyer purchases a product from Amazon.
Banks are already using and investing in machine learning to help look for fraud when credit cards are swiped by a vendor. CitiBank invested in global data science enterprise Feedzai to identify and eradicate financial fraud in real-time across online and in-person banking transactions. The technology helps to rapidly identify fraud and and can help retailers protect their financial activity.
Foundations for Learning
Both data mining and machine learning draw from the same foundation, but in different ways. Data mining pulls from existing information to look for emerging patterns that can help shape our decision-making processes. The clothing brand Free People uses data mining to comb through millions of customer records to shape their look for the season. The data explores best-selling items, what was returned the most, and customer feedback to help sell more clothes and enhance product recommendations.
Machine learning, on the other hand, can actually learn from the existing data and provide the foundation necessary for a machine to teach itself. Zebra Medical Vision developed a machine learning algorithm to predict cardiovascular conditions and events that lead to the death of over 500,000 Americans each year.
Machine learning can look at patterns and learn from them to adapt behavior for future incidents, while data mining is typically used as an information source for machine learning to pull from. Although data mining can be set up to automatically look for specific types of data and parameters, it doesn’t learn and apply knowledge on its own without human interaction. Data mining also can’t automatically see the relationship between existing pieces of data with the same depth that machine learning can.
The right software and tools are needed to be able to analyze and interpret huge amounts of data and find recognizable patterns. Otherwise, the data would largely be unusable unless people could devote their time to looking for these complex, often subtle and seemingly random patterns on their own.
Businesses could use data to shape their sales forecasting or determine what types of products their customers really want to buy. For example, Walmart collects point of sales from over 3,000 stores for its data warehouse. Vendors can see this information and use it to identify buying patterns and guide their inventory predictions and processes for the future.
It’s true that data mining can reveal some patterns through classifications and and sequence analysis. However, machine learning takes this concept a step further by using the same algorithms data mining uses to automatically learn from and adapt to the collected data. As malware becomes an increasingly pervasive problem, machine learning can look for patterns in how data in systems or the cloud is accessed. Machine learning also looks at patterns to help identify which files are actually malware, with a high level of accuracy.
Both data mining and machine learning can help improve the accuracy of data collected. However, data mining and how it’s analyzed generally pertains to how the data is organized and collected. Data mining may include using extracting and scraping software to pull from thousands of resources and sift through data that researchers, investors, and businesses use to look for patterns and relationships that help improve their bottom line.
One of the primary foundations of machine learning is data mining. Data mining can be used to extract more accurate data. This ultimately helps refine your machine learning to achieve better results. A person may miss the multiple connections and relationships between data, while machine learning technology can pinpoint all of these moving pieces to draw a highly accurate conclusion to help shape a machine’s behavior.
Machine learning can enhance relationship intelligence in CRM systems to help sales teams better understand their customers. Combined with machine learning, a company’s CRM can analyze past actions that lead to a conversion or customer satisfaction feedback. It can also be used to learn how to predict which products and services will sell the best and how to shape marketing messages to those customers.
The Future of Data Mining and Machine Learning
By 2020, our accumulated digital universe of data will grow from 4.4 zettabytes to 44 zettabytes, as reported by Forbes. We’ll also create 1.7 megabytes of new information every second for every human being on the planet.
As we amass more data, the demand for advanced data mining and machine learning techniques will force the industry to evolve in order to keep up. We’ll likely see more overlap between data mining and machine learning as the two intersect to enhance the collection and usability of large amounts of data.
According to reporting from Bio IT World, the future of data mining points to predictive analysis, as we’ll see advanced analytics across industries like medical research. Scientists will be able to use predictive analysis to look at factors associated with a disease and predict which treatment will work the best.
We’re just scratching the surface of what machine learning can do and how it will spread to help scale our analytical abilities and improve our technology. According to reporting from Geekwire, as our billions of machines become connected, everything from hospitals to factories to highways can be improved with IoT technology that can learn from other machines.
But some experts have a different idea about data mining and machine learning altogether. Instead of focusing on their differences, you could argue that they both concern themselves with the same question: “How we can learn from data?” At the end of the day, how we acquire and learn from data is really the foundation for emerging technology.