Here's something practically every company out there is focusing on today: data. Organizations are collecting and processing data in unprecedented volume. They’re also using sophisticated data analytics solutions that unlock valuable business insights.
We have been supporting companies of different sizes on their digital transformation journey by providing them with tailored data science services. Our experience shows that "data science" and related terms such as "machine learning" or "data mining" can generate a fair amount of confusion.
Read on to find out the key similarities and differences between data mining, machine learning, and data science.
What is Machine Learning?
There are many machine learning definitions out there, but here's a simple one. Machine learning is a subfield of data science that deals with algorithms able to learn from data and make accurate predictions. Innovative approaches such as neural networks and deep learning. Machine learning uses supervised and unsupervised learning methods to train algorithms.
Machine Learning examples
To help you understand the capabilities of machine learning, here are three applications of this technology that power many real-world products.
This is one of the most common use cases of machine learning which is gaining traction across many different industries. The idea here is building software that can identify and classify objects found on digital images or videos. For example, developers can use machine learning to build a tool that recognizes human faces in images and then matches them to pictures stored in a database. Image recognition software has found many applications in sectors such as retail, healthcare, and entertainment. For instance, it enabled the healthcare industry to introduce new microsurgical procedures delivered by robots that use computer vision and image recognition techniques.
Another common use case of machine learning is speech recognition applications. Such solutions can recognize spoken language and convert it into a text file or take action if following voice commands. Virtual assistants such as Amazon’s Alexa or Google Now rely on speech recognition to bring their users a rich selection of possible actions they can take. Such voice user interfaces allow performing voice searches, dialing, appliance control, call routing, or even data entry.
The powerful capacities of machine learning are transforming healthcare. The technology currently finds many uses in disease diagnostics. Doctors use it for analyzing clinical parameters and their specific combinations for predicting disease progression, therapy planning, and patient monitoring. Healthcare providers also use AI-powered chatbots equipped with speech recognition capabilities to identify patterns in patient symptoms and help generate a potential diagnosis or even recommend an appropriate course of action.
Find more examples of machine learning uses in this article: Machine learning in enterprises - examples of use
What is data mining?
Data mining is an interdisciplinary field that concentrates on discovering the properties of data sets. To accomplish that goal, data mining can take on different approaches. Machine learning is one of them. Other approaches include topological data analysis or data visualization.
Data Mining examples
Here are three real-world use cases of data mining that demonstrate the versatility of this approach to analyzing data.
Enterprises use data mining to explore their increasingly large databases of customer data. By analyzing the relationships between customer demographic data and other parameters, such solutions can predict their behavior to direct personalized marketing campaigns. Such tools also come in handy for market segmentation that requires processing large volumes of data. Data mining helps companies to understand customer interests and preferences to deliver more effective marketing messages and products.
Stores and supermarkets use data mining to generate joint purchasing patterns that identify product associations. Such insights help stores decide how to place items in the aisles and on the shelves to generate maximum revenue. Data mining also helps to identify which offers customers value most and increase sales by delivering such offers to the right people at the right time – for example, at the checkout queue.
Banks use data mining tools for a broad range of purposes. Market risk is one prominent area of application. Financial services companies apply data mining to credit ratings. The technology also powers anti-fraud systems that analyze transactions, purchasing patterns, card transactions, and customer financial data to identify threats. Banks can learn more about their customers by gathering data about their online behaviors. Data mining helps to optimize the ROI of marketing campaigns, manage regulatory compliance obligations, and learn more about the performance of individual marketing channels.
Machine learning and data mining differences
Now that you know what machine learning and data mining are, you're probably still asking yourself this question: What's the difference between machine learning vs. data science? Is there anything the two have in common? And where does data mining enter the equation?
It's worth to take a moment and answer these questions. That's how you deepen your understanding of data science and analytics, together with the potential benefits they might bring to your company.
Let's start with this:
Data mining and machine learning are both rooted in data science.
But there are several key distinctions between these two areas. We list a few of them below.
While data mining and machine learning use the same foundation – data – they draw learning from it in different ways.
In data mining, data scientists analyze the existing information to find emerging patterns that contain insights which may influence the decision-making processes. For example, a fashion e-commerce company can look through millions of customer records to decide which looks to introduce in the new season. Data mining techniques will help the organization explore best-selling items, see which ones were returned most often, and what was the overall customer feedback. By incorporating this knowledge into the sales strategy, the company can boost its revenue, make more accurate product recommendations, and deliver better customer experience.
Machine learning, on the other hand, learns from the existing data and provides the foundation required for machines to teach themselves. A machine learning algorithm can analyze patterns and then learn from them to adapt its behavior for future tasks. That's why data mining is used by data scientists as a source for machine learning. The difference here is that data mining doesn't learn from data apply insights on its own without being set by a human.
It only makes sense to collect data if you're looking to derive meaningful conclusions from it. That's why companies invest in software that can analyze massive volumes of data, find patterns, and deliver valuable information.
Data mining algorithms allow data scientists to reveal patterns in their database. To do that, they use types of data mining such as sequence analysis and classification.
Machine learning, on the other hand, uses data mining to do that – and then it automatically adapts its actions to the collected data. That's why machine learning algorithms are so effective in areas such as security. For example, ML can look for patterns to identify how users access data in systems or the cloud and pinpoint potential malware with high accuracy without the need for monitoring by a human.
The last key difference between data mining and machine learning is that they're used to solve different problems.
For example, data scientists use data mining to discover connections between data and spot patterns. Investment funds use data mining and web scraping to understand whether a company is worth investing in. Data mining can be used to analyze sales trends, social media profiles, digital assets, website, and more to predict future trends in the constantly evolving customer requirements.
Machine learning finds different real-world use cases because it can generate correlation automatically and learn from them to apply knowledge to new algorithms. For instance, Uber uses machine learning to calculate the ETA for rides or meal delivery times for UberEATS. Self-driving cars use ML to adjust to new conditions while driving quickly. Machine learning is the technology behind the instant product recommendations users see when purchasing products from Amazon. The best thing about ML is that its algorithms are designed to improve over time and deliver increasingly accurate results.
|Data mining||Machine learning|
|Learns from the existing data||Learns from the existing data and helps the machine teach itself|
|Recognizes patterns||Recognizes patterns and adapts its analysis to the changing data sets|
|Require human monitoring for improvement||Improve accuracy automatically|
Data mining and machine learning are two fundamental areas of data science today. Companies looking to unlock insights from massive data sets need to closely revise their respective benefits and potential use cases before deciding to invest in a solution. The data mining vs. machine learning issue is just one of the many questions you need to ask when building the best solution for your business.
Do you need advice about building a potential data science solution for your business? Reach out to us; we help companies like yours make the most of these innovative technologies.