Data Mining and AI

Data Mining and Artificial Intelligence: A Guide to Profitability

Digital data is of so much importance nowadays for businesses. We are surrounded by data, but extracting useful information from data is crucial. Extracting insights from big data is not possible manually, we need intelligent systems to implement this. The companies implement intelligent systems to improve sales and the growth of their business.

With the growth of the digital world, many new terms come into the picture like Data Mining, Machine Learning, and Artificial Intelligence.  These terms are used interchangeably, but we need to first understand these terms before going further.

Artificial Intelligence

Machines do not have a natural intelligence like we humans have. So, humans like intelligence are infused in machines artificially which is called Artificial intelligence. Machines are programmed to learn and take actions and decisions like humans.

Artificial Intelligence has shown tremendous growth in the past few years. AI contributes to many industries and brings a revolution in various areas like Computer Vision, chatbots, Self-Driving Cars, Automatic Music Generation, Speech Recognization, Automatic Game playing, etc. Women are doing incredible work in Artificial intelligence. For details click the link below.

Women in Artificial Intelligence

AI vs ML vs DM

Artificial Intelligence is the broad term used for intelligent machines. Machine Learning is a part of AI that learns and predicts based on past data.

Machine Learning

As we humans learn from the past and try to predict the future, similarly machines learn from past data and try to predict the future. There is a set of algorithms that helps machines to learn from the past and keep on learning from the new data and based on this is trying to predict future events.

Earlier, input is given to the machine and calculated based on the algorithm-programmed output. But, now in Machine learning algorithms input and output both are provided, and ML algorithms learn from it and predict output for the future unseen input data.

We can say, in machine learning machines keep on learning with data.

Traditional Programming vs Machine Learning
Image: Traditional Programming vs Machine Learning

There are many machine learning algorithms like K-Nearest Neighbour, Regression, Naive Bayes, SVM, Logistic Regression, etc. For more complex problems Neural Networks are designed which is based on the human brain.

Data Mining

Data Mining is the process of extracting useful information and insights from the data pool. Data mining helps in discovering relationships and various properties between groups of data.

Data Mining. Source: www.pixabay.com
Data Mining. (Source)

Extracting useful information from big data is not a single step, rather this is a multi-step process. The various process that comes under data mining are:

  • Combing data from various sources
  • Data cleaning and removal of noise
  • Data transformation, to bring data in an appropriate form for mining
  • Statistical techniques and ML algorithms are used to extract patterns from data
  • Various Visualization tools are used to present data insights to users

The insightful information derived using data mining is very helpful in various sectors like banking, marketing, health care services, manufacturing units, education sector, e-commerce, etc.

Difference between Data Mining and Machine Learning

Data Mining and machine learning often use the same machine learning algorithms but their goals are different although some overlap is always there. Let’s find out some key differences between data mining and machine learning:

Age

Data Mining existed much before Machine Learning. Data Mining is also known as knowledge discovery in databases (KDD) in some areas. Machine learning was first introduced by Arthur Samuel of IBM, who developed a computer program for playing checkers in the 1950s.

Scope

Machine learning focuses on prediction for future events based on training data learnings while data mining focuses on learning insights and different properties of the data. Data mining is the method of researching data that helps in understanding data, trends, etc. Information extracted with data mining is used by various BI tools to create data visualizations. Machine learning is used to train with complex data and its results make machines smarter and they can predict futuristic unseen data.

Type of Data

Data mining mainly works with raw and unstructured data. Machine Learning works with structured, clean, and organized data. Data mining is used as a pre-processing step before implementing a machine learning model to improve the accuracy of the model.

Learning Capability

Data mining mainly relies on humans. It is created by humans for their use. In Data Mining no learning by machine is there. But, in Machine Learning after initial training, the machine automatically learns by itself, and with every new data, the machine keeps on learning. Machine learning is ahead of data mining, as the machine learns by themselves, no babysitting is required like data mining.

Relationship

There is a relationship between Data mining and machine learning as well. Data mining connects with databases for data and uses machine learning algorithms for mining. Likewise, machine learning uses data mining as a pre-processing step to understand and analyze data.  We can say both data mining and machine learning are interrelated in some terms, but data mining is not necessary for machine learning.

Growth Opportunities

Growth opportunities for data mining are limited, but when it merges with ML, opportunities are endless. Every second we spend on the internet on any website, some AI system is working behind it. They are always looking for an opportunity for more sales, more promotion, and more growth with every person who visits their site.

Implementation

For some time, Data mining has been implemented in various industrial sectors like banking, retail, marketing, e-commerce, etc. It helps in understanding customer requirements, customer segmentation, trends, and season’s impact on sales, thereby helping businesses to grow and increase revenue.

Data mining helps a lot in social media and in gathering information like user’s profiles, likes, dislikes, associations, etc. All this information is used by machine learning algorithms and intelligent systems for friend suggestions, product advertisement sales, etc.

Machine learning with neural networks shows huge advancement in many areas like self-driving cars, chatbots, automatic music generation, automatic customer service, etc.

ML and AI scope keeps on increasing in different sectors, providing huge success and exposure to these technologies

How to profit from Data Mining and Artificial Intelligence?

Insights from data help in increasing companies’ revenue lead to the growth of companies and provide an edge over their competitors. Data Mining helps in every step at an industry level like decision making, market analysis, predicting future trends, fraud detection, etc.

Data Mining with AI Applications
Image: Data Mining with AI Applications

1.    Market Analysis

Extracting new valuable insights from existing data helps the marketing team to analyze the market for future market scenarios, customer demands, and cost analysis. When data mining is combined with machine learning and AI, it is easy to prepare future offers and promotions as per the demands of the market, which leads to more sales.

2.    To predict future trends

Nowadays trends and customer demands change so quickly, that many big companies fail due to a lack of future trend prediction. Analysis of present data and predicting future trends helps many companies to have an edge in the market.

Companies can easily predict, which product needs improvement and what kind of improvement before launching it to the market.

3.    To increase company revenue

Imagine yourself as a business owner, if you are aware of the future demands of the market in your sector, then definitely you will focus more on the production and sales of that product. Launching the right product at the right time is the key to a successful business.

After all, the result is the company’s revenue which will increase with the right launch at the right time.

4.    Helps in Decision Making

Decision-making is the toughest job to do from time to time by senior authorities of an organization. To make the right decision, managers need knowledge and solid evidence. Extracted knowledge from data analysis helps in making the right decision.

Extracted information and future predictions help the company’s growth touch the sky with the correct decision at every step.

5.    Fraud Detection

Fraud detection cases increase so much in the banking and insurance domain. Millions of dollars are lost every year because of fraud. Data mining helps in the collection of data and extracting meaning patterns and insights from data. With the help of machine learning on this data, it is easy to classify the record as fraud or not.

Nowadays, using an intelligent system many fraud cases can easily be predicted earlier which results in lots of money saved.

6.    Customer Segmentation

Artificial intelligence helps in analyzing huge data sets and can easily segment customers according to their performance automatically. Customer segmentation is very important, it helps an organization understand a segment of the customer providing most business to them. Accordingly, they can offer their new products and promotion schemes to different categories of customers.

Let’s take an example: a mobile company owner offers high-segment mobile to customers whose monthly income is above a particular threshold value. Customer who is only meeting their daily needs, will not invest on high prices mobiles. To this segment of customers, the company will offer mobile according to their affordable range.

Understanding customers is very important in saving time, energy, and money. It helps in building a relationship with the customer and securing new business opportunities.

The Future of Data Mining and Artificial Intelligence

In today’s digital world, the business can’t thrive without data mining and artificial intelligence. And the importance of data mining and machine learning techniques will reach sky-high over the next few decades.

Artificial intelligence rapidly growing in various industries. Major future implementation of data mining with artificial intelligence will be research, robotic industry, customer interaction, e-commerce, health care services, and security solutions.

Get ready for the new intelligent and automated world with AI.

Stay Tuned

Keep learning and keep implementing!!

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