What is Data Mining and How to Make a Good Career in it

What is Data Mining and How to Make a Good Career in it | Data Science | Emeritus

Data mining is a valuable process that helps businesses pave the path to success. It involves a unique set of practices that can collectively streamline multiple aspects of a business. This includes improved operations management, better customer experience, efficient marketing strategies, and predictive analysis, among others. It plays an instrumental role in understanding what customers are interested in and how businesses can build strategies around this information. Thanks to data mining techniques, raw data can be processed, interpreted, and turned into actionable intelligence. But what is data mining and how does it actually benefit businesses? Let’s find out in detail.

What is Data Mining?

what-is-kafkaIt is a process of analyzing large volumes of unstructured data to help identify patterns and draw out actionable insights. The main objective of this process is to find correlations between large sets of data and aid companies in making informed choices. It generally provides key insights regarding sales, marketing, finance, and the operational aspects of a business. 

E-commerce websites, for instance, use this technique and predictive analysis to store customer preferences and cross-sell products based on their search or purchase history. Another real-life application of data mining would be mobile service providers. They use data mining techniques to predict if customers are looking to change vendors and accordingly target incentives to them. 

Now that we know ‘what is data mining’ all about, let us take a look at some key benefits of this and how it can help transform business practices. 

Benefits of Data Mining

It offers a wide range of business benefits that can be used to identify market opportunities, enhance customer experience, improve strategic planning, and drive cost-cutting. 

What is data mining’s importance? Let’s explore this briefly:

  • Improves the quality of business-related decisions 
  • Optimizes operations and supply chain management 
  • Helps build customer-centric marketing strategies 
  • Drives cost saving and increase revenue 
  • Boosts risk management 
  • Helps make accurate predictions on future market trends 
  • Enhances competitive advantage 
  • Promotes business safety and security 
  • Refines customer service and experience 
  • Improves cross-selling and increases Return on Investment (ROI) 

How Does Data Mining Work?

What is data mining as a process like? It typically involves interpreting and organizing data. This is usually done by data scientists or skilled Business Intelligence (BI) professionals. Predictive analysis, statistical analysis, and Machine Learning (ML) form the core elements of this process. 

The Cross-Industry Standard Process for Data Mining (CRISP-DM) is the most reliable approach to this process. This comprises six primary steps. 

Step 1: Business Understanding

Begin by defining the main business objective, project parameters, and central criteria for success. 

Step 2: Data Understanding

Analysts extract relevant data required to address identified business challenges. 

Step 3: Data Preparation

On several occasions, the extracted data needs to be prepared for analysis. This involves changing the data to an appropriate format and fixing quality issues like rectifying corrupt, irrelevant, or duplicate data. 

Step 4: Modeling

Develop algorithms to identify data patterns and apply those to a predictive model.

Step 5: Evaluation

The iterative phase is where analysts assess results and fine-tune the algorithm to ensure it accurately addresses the business objective. 

Step 6: Deployment

The final phase is where data miners run the analysis and provide the derived results to decision-makers or business leaders. 

Difference Between Data Mining and Machine Learning

What is List Comprehension in PythonAt their core, both these technologies are about deriving conclusions from data to enhance business decision-making. However, the two concepts operate on different principles. 

This process works on existing data sets to look for functional patterns, whereas machine learning looks beyond pre-existing data to make accurate predictions about future outcomes. Machine learning works with algorithms and follows a more automated process. It essentially trains a computer or system to be more human-like in its behavior by interpreting data and making predictions. Meanwhile, data mining is a more manual process that determines outcomes based on gathered data. 

Data Mining Software and Tools

Once you have a good understanding of what is data mining, it is important to learn about different software and how they perform. Data mining tools are essential in order to interpret and visualize data with ease. 

Most Popular Data Mining Software Tools

  • Orange Data Mining
  • Statistical Analysis System (SAS) Mining
  • DMelt Data Mining
  • Rattle
  • Rapid Miner

There is a wide variety of different types of software that companies work with based on their requirements. A thorough understanding of programming languages, such as Python, SQL, R, Apache Stark, Java, and NoSQL is also necessary to work with large databases. 

ALSO READ: Why Data Engineer Salary Levels are on the Growth Path Globally?

Types of Data Mining

Once you get a clear understanding of the business objective, you will need to choose the type of data mining that yields the best results. There are broadly two types. 

1. Predictive Data Mining

As the name suggests, it uses business intelligence to forecast or predict trends based on past analysis. This can be divided into four types:

  • Regression Analysis
  • Classification Analysis
  • Time Series Analysis 
  • Prediction Analysis

2. Descriptive Data Mining

This type of the technology is used to identify trends from the past by analyzing stored data. It is broadly classified into four types:

  • Summarization Analysis
  • Clustering Analysis
  • Association Rules Analysis 
  • Sequence Discovery Analysis

Data Mining Techniques

There are multiple techniques that can be used to segregate datasets based on their applications.

Key Techniques Used Across Projects

  • Classification
    It helps classify data based on specific criteria.
  • Clustering
    It organizes similar data into buckets.
  • Data Warehouse
    It is the system used to store all the data.
  • Data Cleansing and Preparation
    It enhances the quality of information stored in the data warehouse.
  • Regression
    It identifies and analyzes the numeric value of a variable.
  • Association
    It finds hidden patterns and links within a data set.

Career Outlook and Salary for Data Mining

According to a McKinsey report, there is a shortage of talent for businesses to take advantage of big data. Careers with a core focus on big data and information security are all using these practices today. A few common job profiles that use skills are a business data analyst, database administrator, information security analyst, and data scientist. 

According to the U.S. Bureau of Labor and Statistics, jobs in Information Technology (IT) and computer science are projected to grow by 15 percent between 2021 to 2031. The average salary for jobs with data mining skills is $177,084, based on the U.S. National Average, and can range anywhere between $55,706 and $298,462. 

To conclude, this is a dynamic industry that empowers organizations to make better business decisions that give them an edge over competitors. It integrates multiple big data tools to streamline database management and tackle complex business challenges. 

To enhance your career prospects and develop a deeper insight into the world of data mining, check out data science and analytics courses on Emeritus, your one-stop solution to the best academic courses offered by globally renowned universities! 

Write to us at content@emeritus.org

About the Author

Senior Content Contributor, Emeritus Blog
Iha is the grammar guru turned content wizard who's mastered the delicate dance of correcting bad grammar and teaching people how to correctly pronounce her name. With a filmmaker's flair for marketing and digital media, she's the project ninja, flawlessly coordinating remote and in-person teams for 6+ years. When not conjuring captivating copy, she's delightfully torn between diving into 5 books or diving into endless series—decisions, decisions. Beware of her mischievous dog, who is always ready for a great escape!
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