Data mining can be understood as the process of exploring data through cleaning, finding patterns, designing models, and creating tests. Data Mining includes the concepts of machine learning, statistics, and database management. As a result, it is often easy to confuse data mining with data analytics, data science, or other data processes.
Data mining has had a long and rich history. As a concept, it emerged with the emergence of the computing era in the 1960s. Historically, Data Mining was mostly an intensive coding process and required a lot of coding expertise. Even today, data mining involves the concepts of programming to clean, process, analyze, and interpret data. Data specialists need to have a working knowledge of statistics and at least one programming language to accurately perform data mining tasks. Thanks to intelligent AI and ML systems, some of the core data mining processes are now automated. If you are a beginner in python and data science, upGrad’s data science programs can definitely help you dive deeper into the world of data and analytics.
In this article, we’ll help you clarify all the confusions around data mining, by walking you through all the nuances, including what it is, key concepts to know, how it works, and the future of data mining!
To begin with – Data Mining isn’t precisely Data Analytics
It is natural to confuse data mining with other data projects, including data analytics. However, as a whole, data mining is a lot broader than data analytics. In fact, data analytics is merely one aspect of data analytics. Data mining experts are responsible for cleaning and preparing the data, creating evaluation models, and testing those models against hypotheses for business intelligence projects. In other words, tasks like data cleaning, data analysis, data exploration are parts of the entire data mining spectrum, but they are only the parts of a much bigger whole.
Key Data Mining Concepts
Successfully carrying out any data mining task requires several techniques, tools, and concepts. Some of the most important concepts around data mining are:
- Data cleaning/preparation: This is where all the raw data from disparate sources is converted into a standard format that can be easily processed and analyzed. This includes identifying and removing errors, finding missing values, removing duplicates, etc.
- Artificial Intelligence: AI systems perform analytical activities around human intelligence, such as planning, reasoning, problem-solving, and learning.
- Association rule learning: Also known as market basket analysis, this concept is essential for finding the relationship between different variables of a dataset. By extension, this is an extremely crucial component to determine which products are typically purchased together by customers.
- Clustering: Clustering is the process of dividing a large dataset into smaller, meaningful subsets called clusters. This helps in understanding the individual nature of the elements of the dataset, using which further clustering or grouping can be done more efficiently.
- Classification: The concept of classification is used for assigning items in a large dataset to target classes to improve the prediction accuracy of the target classes for each new data.
- Data analytics: Once all the data has been brought together and processed, data analytics is used to evaluate all the information, find patterns, and generate insights.
- Data warehousing: This is the process of storing an extensive collection of business data in ways that facilitate quick decision-making. Warehousing is the most crucial component of any large-scale data mining project.
- Regression: The regression technique is used to predict a range of numeric values, such as temperature, stock prices, sales, based on a particular data set.
Now that we have all the crucial terms in place let’s look at how a typical Data MIning project works.
How Does Data Mining Work?
Any data mining project typically starts with finding out the scope. It is essential to ask the right questions and collect the correct dataset to answer those questions. Then, the data is prepared for analysis, and the final success of the project depends highly on the quality of the data. Poor data leads to inaccurate and faulty results, making it even more important to diligently prepare the data and remove all the anomalies.
The Data Mining process typically works through the following six steps:
1. Understanding the Business
This stage involves developing a comprehensive understanding of the project at hand, including the current business situation, the business objectives, and the metrics for success.
2. Understanding the data
Once the project’s scope and business goals are clear, next comes the task of gathering all the relevant data that will be needed to solve the problem. This data is collected from all available sources, including databases, cloud storage, and silos.
3. Preparing the data
Once the data from all the sources is collected, it’s time to prepare the data. In this step, data cleaning, normalization, filling missing values, and such tasks are performed. This step aims to bring all the data in the most appropriate and standardized format to carry out further processes.
4. Developing the model
Now, after bringing all the data into a format fit for analysis, the next step is developing the models. For this, programming and algorithms are used to come up with a model that can identify trends and patterns from the data at hand.
5. Testing and evaluating the model
Modeling is done based on the data at hand. However, to test the models, you need to feed it with other data and see if it is throwing the relevant output or not. Determining how well the model is delivering new results will help in achieving business goals. This is generally an iterative process that repeats till the best algorithm has been found to solve the problem at hand.
6. Deployment
Once the model has been tested and iteratively improved, the last step is deploying the model and making the results of the data mining project available to all the stakeholders and decision-makers.
Throughout the entire Data Mining lifecycle, the data miners need to maintain a close collaboration between domain experts and other team members to keep everyone in the loop and ensure that nothing slips through the cracks.
Advantages of Data Mining for Businesses
Businesses now deal with heaps of data on a daily basis. This data is only increasing as time passes, and there’s no way that the volume of this data will ever decrease. As a result, companies don’t have any other choice than to be data-driven. In today’s world, the success of any business largely depends on how well they can understand their data, derive insights from it, and make actionable predictions. Data Mining truly empowers businesses to improve their future by analyzing their past data trends and making accurate predictions about what is likely to happen.
For instance, Data Mining can tell a business about their prospects that are likely to become profitable customers based on past data and are most likely to engage with a specific campaign or offer. With this knowledge, businesses can increase their ROI by offering only those prospects that are likely to respond and become valuable customers.
All in all, data mining offers the following benefits to any business:
- Understanding customer preferences and sentiments.
- Acquiring new customers and retaining existing ones.
- Improving up-selling and cross-selling.
- Increasing loyalty among customers.
- Improving ROI and increasing business revenue.
- Detecting fraudulent activities and identifying credit risks.
- Monitoring operational performance.
By using data mining techniques, businesses can base their decisions on real-time data and intelligence, rather than just instincts or gut, thereby ensuring that they keep delivering results and stay ahead of their competition.
The Future of Data Mining
Data mining, and even other fields of data sciences, has an extremely bright future, owing to the ever-increasing amount of data in the world. In the last year itself, our accumulated data grew from 4.4 zettabytes to 44 zettabytes.
If you are enthusiastic about data science or data mining, or anything to do with data, this is the best time to be alive. Since we’re witnessing a data revolution, it’s the ideal time to get onboard and sharpen your data expertise and skills. Companies all around the globe are almost always on the lookout for data experts with enough skills to help them make sense of their data. So, if you want to start your journey in the data world, now is a perfect time!
At upGrad, we have mentored students from all over the world, belonging to 85+ countries, and helped them start their journeys with all the confidence and skills they require. Our courses are designed to offer both theoretical knowledge as well as hands-on expertise to the students belonging from any background. We understand that data science is truly the need of the hour, and we encourage motivated students from various backgrounds to commence their journey with our 360-degree career assistance.
You could also opt for the integrated Master of Science in Data Science degree offered by upGrad in conjunction with IIT Bengaluru and Liverpool John Moore’s University. This course integrates the previously discussed executive PG program with features such as a Python programming Bootcamp. Upon completion, a student receives a valuable NASSCOM certification that helios in global access to job opportunities.
