- Blog Categories
- Software Development Projects and Ideas
- 12 Computer Science Project Ideas
- 28 Beginner Software Projects
- Top 10 Engineering Project Ideas
- Top 10 Easy Final Year Projects
- Top 10 Mini Projects for Engineers
- 25 Best Django Project Ideas
- Top 20 MERN Stack Project Ideas
- Top 12 Real Time Projects
- Top 6 Major CSE Projects
- 12 Robotics Projects for All Levels
- Java Programming Concepts
- Abstract Class in Java and Methods
- Constructor Overloading in Java
- StringBuffer vs StringBuilder
- Java Identifiers: Syntax & Examples
- Types of Variables in Java Explained
- Composition in Java: Examples
- Append in Java: Implementation
- Loose Coupling vs Tight Coupling
- Integrity Constraints in DBMS
- Different Types of Operators Explained
- Career and Interview Preparation in IT
- Top 14 IT Courses for Jobs
- Top 20 Highest Paying Languages
- 23 Top CS Interview Q&A
- Best IT Jobs without Coding
- Software Engineer Salary in India
- 44 Agile Methodology Interview Q&A
- 10 Software Engineering Challenges
- Top 15 Tech's Daily Life Impact
- 10 Best Backends for React
- Cloud Computing Reference Models
- Web Development and Security
- Find Installed NPM Version
- Install Specific NPM Package Version
- Make API Calls in Angular
- Install Bootstrap in Angular
- Use Axios in React: Guide
- StrictMode in React: Usage
- 75 Cyber Security Research Topics
- Top 7 Languages for Ethical Hacking
- Top 20 Docker Commands
- Advantages of OOP
- Data Science Projects and Applications
- 42 Python Project Ideas for Beginners
- 13 Data Science Project Ideas
- 13 Data Structure Project Ideas
- 12 Real-World Python Applications
- Python Banking Project
- Data Science Course Eligibility
- Association Rule Mining Overview
- Cluster Analysis in Data Mining
- Classification in Data Mining
- KDD Process in Data Mining
- Data Structures and Algorithms
- Binary Tree Types Explained
- Binary Search Algorithm
- Sorting in Data Structure
- Binary Tree in Data Structure
- Binary Tree vs Binary Search Tree
- Recursion in Data Structure
- Data Structure Search Methods: Explained
- Binary Tree Interview Q&A
- Linear vs Binary Search
- Priority Queue Overview
- Python Programming and Tools
- Top 30 Python Pattern Programs
- List vs Tuple
- Python Free Online Course
- Method Overriding in Python
- Top 21 Python Developer Skills
- Reverse a Number in Python
- Switch Case Functions in Python
- Info Retrieval System Overview
- Reverse a Number in Python
- Real-World Python Applications
- Data Science Careers and Comparisons
- Data Analyst Salary in India
- Data Scientist Salary in India
- Free Excel Certification Course
- Actuary Salary in India
- Data Analyst Interview Guide
- Pandas Interview Guide
- Tableau Filters Explained
- Data Mining Techniques Overview
- Data Analytics Lifecycle Phases
- Data Science Vs Analytics Comparison
- Artificial Intelligence and Machine Learning Projects
- Exciting IoT Project Ideas
- 16 Exciting AI Project Ideas
- 45+ Interesting ML Project Ideas
- Exciting Deep Learning Projects
- 12 Intriguing Linear Regression Projects
- 13 Neural Network Projects
- 5 Exciting Image Processing Projects
- Top 8 Thrilling AWS Projects
- 12 Engaging AI Projects in Python
- NLP Projects for Beginners
- Concepts and Algorithms in AIML
- Basic CNN Architecture Explained
- 6 Types of Regression Models
- Data Preprocessing Steps
- Bagging vs Boosting in ML
- Multinomial Naive Bayes Overview
- Gini Index for Decision Trees
- Bayesian Network Example
- Bayes Theorem Guide
- Top 10 Dimensionality Reduction Techniques
- Neural Network Step-by-Step Guide
- Technical Guides and Comparisons
- Make a Chatbot in Python
- Compute Square Roots in Python
- Permutation vs Combination
- Image Segmentation Techniques
- Generative AI vs Traditional AI
- AI vs Human Intelligence
- Random Forest vs Decision Tree
- Neural Network Overview
- Perceptron Learning Algorithm
- Selection Sort Algorithm
- Career and Practical Applications in AIML
- AI Salary in India Overview
- Biological Neural Network Basics
- Top 10 AI Challenges
- Production System in AI
- Top 8 Raspberry Pi Alternatives
- Top 8 Open Source Projects
- 14 Raspberry Pi Project Ideas
- 15 MATLAB Project Ideas
- Top 10 Python NLP Libraries
- Naive Bayes Explained
- Digital Marketing Projects and Strategies
- 10 Best Digital Marketing Projects
- 17 Fun Social Media Projects
- Top 6 SEO Project Ideas
- Digital Marketing Case Studies
- Coca-Cola Marketing Strategy
- Nestle Marketing Strategy Analysis
- Zomato Marketing Strategy
- Monetize Instagram Guide
- Become a Successful Instagram Influencer
- 8 Best Lead Generation Techniques
- Digital Marketing Careers and Salaries
- Digital Marketing Salary in India
- Top 10 Highest Paying Marketing Jobs
- Highest Paying Digital Marketing Jobs
- SEO Salary in India
- Brand Manager Salary in India
- Content Writer Salary Guide
- Digital Marketing Executive Roles
- Career in Digital Marketing Guide
- Future of Digital Marketing
- MBA in Digital Marketing Overview
- Digital Marketing Techniques and Channels
- 9 Types of Digital Marketing Channels
- Top 10 Benefits of Marketing Branding
- 100 Best YouTube Channel Ideas
- YouTube Earnings in India
- 7 Reasons to Study Digital Marketing
- Top 10 Digital Marketing Objectives
- 10 Best Digital Marketing Blogs
- Top 5 Industries Using Digital Marketing
- Growth of Digital Marketing in India
- Top Career Options in Marketing
- Interview Preparation and Skills
- 73 Google Analytics Interview Q&A
- 56 Social Media Marketing Q&A
- 78 Google AdWords Interview Q&A
- Top 133 SEO Interview Q&A
- 27+ Digital Marketing Q&A
- Digital Marketing Free Course
- Top 9 Skills for PPC Analysts
- Movies with Successful Social Media Campaigns
- Marketing Communication Steps
- Top 10 Reasons to Be an Affiliate Marketer
- Career Options and Paths
- Top 25 Highest Paying Jobs India
- Top 25 Highest Paying Jobs World
- Top 10 Highest Paid Commerce Job
- Career Options After 12th Arts
- Top 7 Commerce Courses Without Maths
- Top 7 Career Options After PCB
- Best Career Options for Commerce
- Career Options After 12th CS
- Top 10 Career Options After 10th
- 8 Best Career Options After BA
- Projects and Academic Pursuits
- 17 Exciting Final Year Projects
- Top 12 Commerce Project Topics
- Top 13 BCA Project Ideas
- Career Options After 12th Science
- Top 15 CS Jobs in India
- 12 Best Career Options After M.Com
- 9 Best Career Options After B.Sc
- 7 Best Career Options After BCA
- 22 Best Career Options After MCA
- 16 Top Career Options After CE
- Courses and Certifications
- 10 Best Job-Oriented Courses
- Best Online Computer Courses
- Top 15 Trending Online Courses
- Top 19 High Salary Certificate Courses
- 21 Best Programming Courses for Jobs
- What is SGPA? Convert to CGPA
- GPA to Percentage Calculator
- Highest Salary Engineering Stream
- 15 Top Career Options After Engineering
- 6 Top Career Options After BBA
- Job Market and Interview Preparation
- Why Should You Be Hired: 5 Answers
- Top 10 Future Career Options
- Top 15 Highest Paid IT Jobs India
- 5 Common Guesstimate Interview Q&A
- Average CEO Salary: Top Paid CEOs
- Career Options in Political Science
- Top 15 Highest Paying Non-IT Jobs
- Cover Letter Examples for Jobs
- Top 5 Highest Paying Freelance Jobs
- Top 10 Highest Paying Companies India
- Career Options and Paths After MBA
- 20 Best Careers After B.Com
- Career Options After MBA Marketing
- Top 14 Careers After MBA In HR
- Top 10 Highest Paying HR Jobs India
- How to Become an Investment Banker
- Career Options After MBA - High Paying
- Scope of MBA in Operations Management
- Best MBA for Working Professionals India
- MBA After BA - Is It Right For You?
- Best Online MBA Courses India
- MBA Project Ideas and Topics
- 11 Exciting MBA HR Project Ideas
- Top 15 MBA Project Ideas
- 18 Exciting MBA Marketing Projects
- MBA Project Ideas: Consumer Behavior
- What is Brand Management?
- What is Holistic Marketing?
- What is Green Marketing?
- Intro to Organizational Behavior Model
- Tech Skills Every MBA Should Learn
- Most Demanding Short Term Courses MBA
- MBA Salary, Resume, and Skills
- MBA Salary in India
- HR Salary in India
- Investment Banker Salary India
- MBA Resume Samples
- Sample SOP for MBA
- Sample SOP for Internship
- 7 Ways MBA Helps Your Career
- Must-have Skills in Sales Career
- 8 Skills MBA Helps You Improve
- Top 20+ SAP FICO Interview Q&A
- MBA Specializations and Comparative Guides
- Why MBA After B.Tech? 5 Reasons
- How to Answer 'Why MBA After Engineering?'
- Why MBA in Finance
- MBA After BSc: 10 Reasons
- Which MBA Specialization to choose?
- Top 10 MBA Specializations
- MBA vs Masters: Which to Choose?
- Benefits of MBA After CA
- 5 Steps to Management Consultant
- 37 Must-Read HR Interview Q&A
- Fundamentals and Theories of Management
- What is Management? Objectives & Functions
- Nature and Scope of Management
- Decision Making in Management
- Management Process: Definition & Functions
- Importance of Management
- What are Motivation Theories?
- Tools of Financial Statement Analysis
- Negotiation Skills: Definition & Benefits
- Career Development in HRM
- Top 20 Must-Have HRM Policies
- Project and Supply Chain Management
- Top 20 Project Management Case Studies
- 10 Innovative Supply Chain Projects
- Latest Management Project Topics
- 10 Project Management Project Ideas
- 6 Types of Supply Chain Models
- Top 10 Advantages of SCM
- Top 10 Supply Chain Books
- What is Project Description?
- Top 10 Project Management Companies
- Best Project Management Courses Online
- Salaries and Career Paths in Management
- Project Manager Salary in India
- Average Product Manager Salary India
- Supply Chain Management Salary India
- Salary After BBA in India
- PGDM Salary in India
- Top 7 Career Options in Management
- CSPO Certification Cost
- Why Choose Product Management?
- Product Management in Pharma
- Product Design in Operations Management
- Industry-Specific Management and Case Studies
- Amazon Business Case Study
- Service Delivery Manager Job
- Product Management Examples
- Product Management in Automobiles
- Product Management in Banking
- Sample SOP for Business Management
- Video Game Design Components
- Top 5 Business Courses India
- Free Management Online Course
- SCM Interview Q&A
- Fundamentals and Types of Law
- Acceptance in Contract Law
- Offer in Contract Law
- 9 Types of Evidence
- Types of Law in India
- Introduction to Contract Law
- Negotiable Instrument Act
- Corporate Tax Basics
- Intellectual Property Law
- Workmen Compensation Explained
- Lawyer vs Advocate Difference
- Law Education and Courses
- LLM Subjects & Syllabus
- Corporate Law Subjects
- LLM Course Duration
- Top 10 Online LLM Courses
- Online LLM Degree
- Step-by-Step Guide to Studying Law
- Top 5 Law Books to Read
- Why Legal Studies?
- Pursuing a Career in Law
- How to Become Lawyer in India
- Career Options and Salaries in Law
- Career Options in Law India
- Corporate Lawyer Salary India
- How To Become a Corporate Lawyer
- Career in Law: Starting, Salary
- Career Opportunities: Corporate Law
- Business Lawyer: Role & Salary Info
- Average Lawyer Salary India
- Top Career Options for Lawyers
- Types of Lawyers in India
- Steps to Become SC Lawyer in India
- Tutorials
- C Tutorials
- Recursion in C: Fibonacci Series
- Checking String Palindromes in C
- Prime Number Program in C
- Implementing Square Root in C
- Matrix Multiplication in C
- Understanding Double Data Type
- Factorial of a Number in C
- Structure of a C Program
- Building a Calculator Program in C
- Compiling C Programs on Linux
- Java Tutorials
- Handling String Input in Java
- Determining Even and Odd Numbers
- Prime Number Checker
- Sorting a String
- User-Defined Exceptions
- Understanding the Thread Life Cycle
- Swapping Two Numbers
- Using Final Classes
- Area of a Triangle
- Skills
- Software Engineering
- JavaScript
- Data Structure
- React.js
- Core Java
- Node.js
- Blockchain
- SQL
- Full stack development
- Devops
- NFT
- BigData
- Cyber Security
- Cloud Computing
- Database Design with MySQL
- Cryptocurrency
- Python
- Digital Marketings
- Advertising
- Influencer Marketing
- Search Engine Optimization
- Performance Marketing
- Search Engine Marketing
- Email Marketing
- Content Marketing
- Social Media Marketing
- Display Advertising
- Marketing Analytics
- Web Analytics
- Affiliate Marketing
- MBA
- MBA in Finance
- MBA in HR
- MBA in Marketing
- MBA in Business Analytics
- MBA in Operations Management
- MBA in International Business
- MBA in Information Technology
- MBA in Healthcare Management
- MBA In General Management
- MBA in Agriculture
- MBA in Supply Chain Management
- MBA in Entrepreneurship
- MBA in Project Management
- Management Program
- Consumer Behaviour
- Supply Chain Management
- Financial Analytics
- Introduction to Fintech
- Introduction to HR Analytics
- Fundamentals of Communication
- Art of Effective Communication
- Introduction to Research Methodology
- Mastering Sales Technique
- Business Communication
- Fundamentals of Journalism
- Economics Masterclass
- Free Courses
What is Data Warehousing and Data Mining
Updated on 20 November, 2024
6.44K+ views
• 11 min read
Table of Contents
Enterprise data was stored in information silos that were physically apart from other data repositories, and each silo served specialized functions – but that was before Big Data hit the world (by a storm, if we may say). Now, it’s practically impossible to practice the same methods on such large datasets. Just imagine the number of data extracts it would require from so many of such physically separated information silos – only to run a simple query. All thanks to the extremely massive pile of data that lie with organizations & big data engineering methods.
Let’s keep a close eye to how Data Warehousing and Data mining enters the scene. Data Warehouses were developed to combat this problem of data storage. Essentially, Data Warehouses can be thought of as a unified repository of data that comes from various sources and is in various formats. Data Mining, on the other hand, is the process of extracting knowledge from the said Data Warehouse.
In this article, we’ll take a detailed look at Data Warehouse and Data Mining. For better understanding, we’ve structured the article as follows:
- What is Data Warehousing?
- Data Warehouse Processes
- What is Data Mining?
- KDD Process
- Real Life Use-Cases of Data Mining
What is Data Warehousing?
If we were to define Data Warehouse, it can be explained as a subject-oriented, time-variant, non-volatile, an integrated collection of data. The introduction to Data Warehousing also comprises compiled data from external sources. The purpose of designing a Warehouse is to analyze and induce business decisions by reporting data at a different aggregate level. Before moving further from here, let’s first look at what these terms mean in the context of a Data Warehouse:
Subject-Oriented
Organizations can use the Data Warehouse to analyze a specific subject area. Suppose you want to see how well your sales team has performed in the last 5 years – you can query your Warehouse, and it’ll tell you all you need to know. In this case, “sales” can be treated as a subject.
Time-Variant
Data Warehouses are responsible for storing historical data for organizations. For example, a transaction system can hold the most recent address of a customer, but a Data Warehouse will hold all the previous addresses too. It continuously keeps adding data from various sources, apart from keeping the historical data – that’s what makes it a time-variant model. The data stored will always vary with time.
Non-Volatile
Once data is stored in a Data Warehouse, it can’t be altered or modified. We can only add a modified copy of the data we want to modify.
Integrated:
As we said earlier, a Data Warehouse holds data from multiple sources. Say we have two data sources – A and B. Both the sources might have completely different types of data stored in them, but when they are brought to a Warehouse, they’re made to undergo preprocessing. That is how a Data Warehouse integrates data from a number of sources.
Get Started in Data Science with Python
Data Warehouse Processes
Take a look at the above image. The data that is collected from various sources (operational system, ERP, CRM, Flat Files, etc.) is made to undergo an ETL process before it’s inserted into the data warehouse. This is essentially done to remove anomalies, if any, from the data – so that no harm is caused to the Data Warehouse. ETL stands for – Extraction, Transformation, and Loading. Let’s have a look at each of these processes in detail. To understand better, we’ll use an analogy – think of a gold rush and read on!
Extraction
Extraction is essentially done to collect all the required data from the source systems using as few resources as possible.
Think of this step like panning the river in search of gold nuggets as big as possible.
Transformation
The main aim is to insert the extracted data into the database in a general format. This is because different sources will have different formats of storing the data – for example, one data source might have data in “dd/mm/yyyy” format, and the other might have it in “dd-mm-yy” format. In this step, we’ll convert this into a generalized format – one that’ll be used for data from all the sources.
Now you have a gold nugget. What do you do? Melt it down and remove the impurities.
Loading
In this step, the transformed data is loaded into the target database.
Now you have pure gold – mould it into a ring and sell it away!
The process of bringing data from various sources and storing it in the Data Warehouse (after the ETL process, of course), is what is known as Data Warehousing.
Now, you have your data in place – all cleaned up and ready to go. What should be the next step? Extracting knowledge – yes!
Data Mining to the rescue!
How Can You Transition to Data Analytics?
Our learners also read: Top Python Courses for Free
upGrad’s Exclusive Data Science Webinar for you –
How upGrad helps for your Data Science Career?
What is Data Mining?
Data Mining is, quite simply, the process of extracting previously unknown but potentially useful information from the data sets. By “previously unknown”, we mean knowledge that can be acquired only after deeply mining the data warehouse – i.e., it won’t make sense on the surface. Data Mining essentially searches for the relationships global patterns that exist between the data elements.
For example, imagine you run a supermarket. Now, a customer’s purchase history might not look to reveal a lot on the surface, but, if analyzed carefully – recognizing the possible patterns, then merely this information is enough to give out a lot. If you haven’t guessed it yet, we’re talking about Target – a supermarket that figured out a teen girl (customer) was pregnant just by carefully studying her purchase history and looking for trends and patterns. So, the information that looked so trivial on the surface turned out to be of so much value when mined carefully – and that is exactly what we mean by “previously unknown knowledge”.
We feel it’ll be unfair to you if we give you the flavor of Data Warehousing and Data Mining and completely ignore the big picture – Knowledge Discovery in Databases (KDD). Data Mining forms one of the steps of a KDD process.Let’s talk a bit more about KDD.
Earn data science certification from the World’s top Universities. Join our Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.
Knowledge Discovery In Databases (KDD)
Data mining is one of the more crucial steps in the process of KDD. KDD basically covers everything from the selection of data to finally evaluating the mined data. The complete KDD cycle is shown in the image below:
Selection
It is of utmost importance to know the exact target data. Analyzing Data Mining to Data Warehousing subset is a very important step because removing unrelated data elements will reduce the search space during the Data Mining phase.
Pre-processing
In this step, the selected data is freed from any anomalies and outliers. Basically, the data is completely cleaned in this phase. Like, if there are some missing data fields, they’re filled with appropriate values. For example, in the table that stores the details of your organization’s employees, suppose there’s a column for “Middle Name”. Chances are, it’ll be empty for many employees. In such a scenario, an appropriate value is chosen (N/A, for ex).
Transformation
This phase attempts to reduce the variety of data elements while preserving the quality of the info.
Data mining
This is the main phase of a KDD process. The transformed data is subjected to data-mining methods like grouping, clustering, regression, etc. This is done iteratively to bring the best results. Different techniques can be used depending on the requirements.
Evaluation
This is the final step. In this, the obtained knowledge is documented and presented for further analysis. Various Data Visualisation tools are used in this step to depicting the acquired knowledge in a beautiful and understandable way.
How Does Simpson’s Paradox Affect Data?
Real Life Use-Cases of Data Mining
Every organization from Amazon, Flipkart, Netflix, to Facebook, Twitter, Instagram, to even Walmart, is putting Data Mining to good use. In this section, we’ll talk about four broad use cases of Data Mining that are an integral part of your day-to-day life.
Service Providers
Telecom service providers use Data Mining to predict the “churn” – a term used by them for when a customer ditches them for another provider. Apart from that, they collate billing information, website visits, customer care interactions, and other such things to give each customer a probability score. Then, those customers that are on a higher risk of “churning” are provided offers and incentives.
E-Commerce
E-commerce is easily the most known use case when it comes to Data Mining. One of the most famous of them is, of course, Amazon. They use extremely sophisticated mining techniques. Check out the “People who viewed that product, also liked this” functionality for instance!
Supermarkets
Supermarkets are also an interesting use case of Data Mining. Mining the purchase history of customers allows them to understand their purchasing patterns. This information is then used by the supermarkets to provide personalized offers to the customers. Oh, and did we tell you about what Target did using Data Mining? (Yes, we did!)
Retail
Retailers club their customers into Recency, Frequency, and Monetary (RFM) groups. Using Data Mining, they target marketing to these groups. A customer who spends little but frequently and his last purchase was fairly recent will be handled differently than a customer who spent a lot but only once.
Who is a Data Scientist, a Data Analyst and a Data Engineer?
Wrapping Up…
Data Warehousing and Data Mining make up two of the most important processes that are quite literally running the world today. Almost every big thing today is a result of sophisticated data mining. Because un-mined data is as useful (or useless) as no data at all.
Again, to understand the difference between Data Mining And Data Warehousing you have to indulge in, from the introduction to Data Mining to Data Warehousing- which is a method all centralizing the data from disparate sources in one database. We can define Data warehousing as compiled historical data or real-time data feed that gives backs mostly organic and integrated information.
We hope this article gave you clarity on what is Data Warehousing and Data Mining and much more. To conclude, the process of collecting, storing and organizing information in a single database is considered to be as Data Warehousing vs. Data Mining is mostly extracting meaningful information from the data using a different perspective. All the useful information which is collected can be used afterward to solve future issues that might be an obstacle in the growth of the company and can even cut costs too. If you are looking for a bright and fascinating future and if exploration is your passion then starting from learning the Whats’ What of Data Warehousing and Data Mining would be an excellent option for you.
We hope this article gave you clarity on what these two terms mean and much more! If you are curious to learn about data science, check out IIIT-B & upGrad’s PG Diploma in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.
Explore our Popular Data Science Online courses
Top Data Science Skills to Learn to upskill
SL. No | Top Data Science Skills to Learn | |
1 |
Data Analysis Online Courses | Inferential Statistics Online Courses |
2 |
Hypothesis Testing Online Courses | Logistic Regression Online Courses |
3 |
Linear Regression Courses | Linear Algebra for Analysis Online Courses |
Read our popular Data Science Articles
Frequently Asked Questions (FAQs)
1. How do businesses use Data Warehousing and Data Mining?
Both data mining and data warehousing are business intelligence techniques for transforming information (or data) into usable knowledge.
Data mining is a statistical analysis method. Technical tools are used by analysts to query and sort through gigabytes of data in search of trends. Businesses then utilise this data to make better business decisions based on their understanding of the behaviours of their consumers and suppliers.
Data Warehousing is the process of designing how data is stored in order to facilitate reporting and analysis. According to data warehouse specialists, the numerous data stores are both conceptually and physically integrated and related to one another. The data of a company is typically saved in multiple databases.
2. What is the core difference between Data Warehousing and Data Mining? Which is more practical in the business world?
A data warehouse is a data storage system. It usually entails a variety of data kinds acquired from multiple sources for a variety of objectives. The process of storing this data with discipline so that it may be retrieved later is known as data warehousing.
The process of extracting data is known as data mining. It entails locating the most pertinent information for a particular goal. It might come from your data warehouse, or from somewhere else entirely. You anticipate refining and cleaning the data you mine, just as you would with real ore.
The better your warehousing systems are, the easier it will be to mine.
3. Are Data Mining and KDD process similar?
Although KDD and Data Mining are the terms that are frequently interchanged, they refer to two distinct but related concepts.
Data Mining is a component within the KDD process that deals with recognising patterns in data, whereas KDD is the whole process of extracting knowledge from data. To put it another way, Data Mining is just the application of a specific algorithm to achieve the KDD process’s ultimate purpose.