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Data Mining and Machine Learning: What’s the Difference?

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Data will change the way you conduct your current business in the future years to come.

Today data holds more value than any of your other techniques or methods or processes.

With data, you can change the way you do business.

How?

Catering to clients becomes more relevant, communication takes place on the right topics, creating or rather enhancing a product solution takes place as visualized in the mind of your prospects.

This all takes place with the power of data.

Now that you are aware of how important data is, you need to understand that there are two techniques to get the above process conducted.

Names as ‘Data mining’ and ‘Machine learning’, these processes are claimed to be the same in meaning and nature. 

However, it isn’t. This article will serve proof of that.

These two techniques are essential when conducting your data activities and it is important that you understand them better.

In this article, we will be covering the following:

Table of Contents:

1. Why is data important in 2020?

2. What do you mean by Data mining?

3. What do you mean by Machine learning?

4. Differences between data mining and machine learning

5. Future of data mining and machine learning

Let’s get started.


Why is data important in 2020?

Change is going to take place anytime.

When this happens your business cannot stumble, instead, it needs to take the next action to survive in the business world. 

Imagine if you had valuable data in your hand, wouldn’t you be able to cater to your business better.

Let’s understand this with an example.

Say that you are selling your business online and only depending on social media channels to sell. In the next few days, you realize that your prospects have been telling you to have a mobile application which they can buy from you better. Say that you ignore this request and continue to do what you are doing, suddenly when to change hits where a mobile application should be a business priority, you are stuck.

Interesting Read : How to Start Your Data Collection Project?

The time you will take to understand the application and create it, would be enough to put your business on a slow growth track. Now think what would happen if you took the data of your multiple prospects who said that they wanted you to have a mobile application.

You would invest in that and when the change hits, you are left with no worries as you can start to launch that and get more prospects in your favour. This is the power of data.

Data is important for your business because:

1. It lets you cater to your prospects exact or near needs

2. It lets you enhance your business in the way your prospects expect it to engage it in

Your business surrounds around your prospects’ needs, data is what will help you fulfill their needs which is why data mining and machine learning serve its purpose here.


What do you mean by Data mining?

Data mining is the process of extracting valuable information from the data heap you’ve collected.

Now you have collected your data, but not everything in your data will be valuable to your business which is why data mining comes in. 

Data mining ensures that it captures only the useful information from the crowd that can help strengthen your business for the best. It tries to find new, accurate, and useful patterns that can help your current business to flourish better.

Interesting Read : How to capture powerful data for Sales Intelligence like a Pro?

Data mining activities is used in multiple places such as:

1. E-commerce– The e-commerce industry is huge and what happens is with the data being collected, the businesses under this, can easily capture the prospect’s attention better via recommendations, suggestions, and more. They can use data mining to capture what their prospects are looking out for and more actions of theirs.

2. Retail– The retail industry too can make use of data mining to understand their prospects purchasing patterns and conduct promotional activities on the basis of that. This will help them to sell what is being needed and enhance any other retain activities.


What do you mean by Machine Learning?

Machine learning is a branch of artificial intelligence where robots are used to understand the behaviour of humans which in this case is your prospects. It uses algorithms to retrieve knowledge from the data and then make a predictive analysis.

In simpler terms, machine learning is giving you a prediction from the data collected that in the next few years this might be how the market you are investing in would look like.

This is important because it helps you to prepare for the future better. You will be ready with what is about to come and that will help your business to sustain the tough competition and changing business environment. 

Machine learning is too used in multiple places such as:

1. Business intelligence– Here machine learning is helping any brand to walk in the right direction. They help to create an analysis of what the business will look like and how they can align according to that be it in the sales sector or marketing and even more

2. Online customer service– Have you noticed that when you interact with a chatbot you start to receive questions on the basis of your actions? This is exactly what is happening, machine learning is being used here to understand what the prospects are going to ask, capturing their information and then framing better communication on the basis of relevant actions

Interesting Read : Guide to Data Wrangling: What It Is and Who Should Do It

As you can see from above, the reason why would both these terminologies are considered to be the same is because both of them are using data to identify valuable patterns, they conduct analytic processes, they use data to make their purchase decision and more.

But as we confessed to you at the beginning of this article, we are proving to you that both of these terms are in fact different in meaning, nature, and more.

Let’s get you hooked on the differences more:


Data Mining v/s Machine Learning

1. Data analysis

Data mining ensures that they conduct a data analysis so that they can capture the relevant information from it, on the other hand, machine learning uses the pre-existing data to make the predictions. They dig in deeper and go beyond the past in order to conduct better futuristic planning.

2. Rules

In data mining, the rules are not stated when the process is beginning. With machine learning, the rules are stated so that data understanding can take place well.

3. Manual efforts

Data mining is conducted manually and depends on the decision making of humans. With machine learning, the rules are placed and the process of extracting information and learning it takes place automatically.

4. Dataset

Data mining uses an existing dataset to help identify patterns. With machine learning, they are using a training data set where they will know what to do with data, understand it, and then make the latest predictions about the new data sets.

5. Purpose

Data mining is being used to capture quality information whereas with machine learning it is using the data to make predictions or outcomes from the data collected.

Interesting Read : Data Harvesting v/s Data Mining: Which one is better for data capture?

6. Use

Data mining is being conducted so that it can be used to make forecasts for businesses and other organizations. With machine learning, its main use is algorithms as it depends and works in that format. 

7. Dependency

Data mining does depend on machine learning as it incorporates two elements, one which is being stated and the other is a database. With machine learning, this isn’t the case, it isn’t dependent on data mining.

8. Growth

Data mining fails to learn or adapt as it follows pre-set rules and is static. On the other hand, machine learning gets smarter with every learning. They can learn and adapt well.

9. Recognizing patterns

When you have the data collected, it is important that you are able to recognize the patterns. With data mining, this is possible via classification and sequence analysis. With machine learning, it makes use of the same algorithms which data mining uses in order to learn and adapt to from the collected data automatically. 

10. Applications

Data mining is being used more for retail, e-commerce, forecasting sales, and marketing efforts. Machine learning is used for offering accurate insights in real-time such as preventing frauds, personalizing a prospect’s shopping experience, and more.


Future of data mining and machine learning

Data is going to be important now and even in the future and with multiple brands being dependent on such a crucial process, data mining and machine learning are bound to grow and be used more often. 


The Bottom Line…

Data mining and machine learning as you are now aware are not the same things, they have a difference and the proof is evident in the information provided above.

Before you leave, let’s get a quick summarization of what was covered in the article:

Key takeaways:

1. Data is important for your business because it lets you cater to your prospects exact or near needs and also it lets you enhance your business in the way your prospects expect it to engage it in

2. Data mining is the process of extracting valuable information from the data heap you’ve collected.

3. Machine learning is a branch of artificial intelligence where robots are used to understand the behaviour of humans which in this case is your prospects. It uses algorithms to retrieve knowledge from the data and then make a predictive analysis.

4. The reason why both these terminologies are considered to be the same is because both of them are using data to identify valuable patterns, they conduct analytic processes, they use data to make their purchase decision and more.

5. The difference between data mining and machine learning are data analysis, rules, purpose and more

6. Data is going to be important now and even in the future and with multiple brands being dependent on such a crucial process, data mining and machine learning are bound to grow and be used more often.

So what do you think? Which data technique would you use and why?

About the author

Rachael Chapman

A Complete gamer and a Tech Geek. Brings out all her thoughts and love in writing blogs on IOT, software, technology etc

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