Data Mining vs Data Analytics
Nowadays, companies are producing immense quantities of data tremendously quickly. This data could change the way businesses work by giving them useful knowledge that helps them make better decisions.
It can be very difficult to make sense of all the information that is out there. Using Data Analytics and Data Mining can help you to understand information very clearly.
In this blog post, we will discuss the difference between data analytics and data mining. You will learn different techniques and types, and, most crucially, when it is most advantageous to utilize one or the other.
What is Data Mining?
Data Mining is the processing of large amounts of data to find valuable insights & patterns which can help businesses in making better decisions.
What is the best part? No pickaxe is necessary!!!
Using specialized algorithms and statistical techniques to identify patterns and relationships in the data is part of the process. Which products are most likely to sell together or which customers are most likely to churn might be what businesses are looking for.
Data Mining Architecture
As you know, Data Mining is quite a tedious process. It has a lot of potential to grow businesses, but at the same time, any mistakes can cost you dearly. That’s where Data Mining architectures come into the picture. A data source, data mining engine, data warehouse server, pattern assessment module, graphical user interface, and knowledge base are the key elements of the data mining architecture.
Data Mining Techniques
Let’s see some of the Data Mining techniques.
Association rule learning
Association rule learning is one data mining technique which involves looking for intriguing connections between different variables in the data.
Clustering
Clustering is another data mining technique where data points are grouped together based on their similarities.
Regression Analysis
Regression analysis is another type of data mining technique that can be utilized to generate predictions. If you are looking to forecast sales for the upcoming quarter, you could utilize regression analysis to pinpoint the factors that are most likely to have an impact on those sales.
Anomaly Detection
Finally, anomaly detection is here. This data mining technique involves recognizing the anomalous data points that stand out from the others. This is especially beneficial for applications such as fraud detection or identifying problems in production procedures.
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What is Data Analytics?
Essentially, “Data analytics” is the process that analyzes the data for deriving insights or knowledge from it using statistical tools.
You might ask, “And how do we do that?
There are specialized software and Data Analytics tools which assist us in extracting meaning from data.
Then Why go through all this trouble?
The aim of data analytics is to enable informed decisions that will lead to corporate success. By analyzing the data, we can derive valuable insights about what is successful, what is failing, and where we should concentrate our efforts.
Types of Data Analytics
Let’s see some of the Data Analytics types:
Descriptive Analytics
Descriptive Analytics involves looking back at what has occurred in the past in order to gain insights. We’re discussing sales trends, customer demographics, website traffic, and everything else.
diagnostic analytics
For understanding why something happened, we now rely on diagnostic analytics. By examining prior data, we can pinpoint the fundamental reason for a problem or trend.
Predictive Analytics
Predictive Analytics involves using data and statistical models to make predictions about what may happen in the future. We can use statistical algorithms and machine learning techniques to forecast sales, customer behavior, and trends.
Prescriptive Analytics
Prescriptive Analytics utilizes optimizations algorithms to furnish guidance on the optimal course of action to take for the specified situation. It takes into consideration constraints and goals in order to maximize business results.
Data Analytics and Data Analysis are not the same. Click here to learn more about data analysis.
Key Difference between Data Mining and Data Analytics
Difference in definitions
Data Mining Definition
The process that extracts the patterns from the data, is Data mining.
Data Analytics Definition
The process that analyzes the data for deriving insights or knowledge from it using statistical tools, is Data analytics.
For example, in the retail industry, if a customer buys a product, then we can use mining techniques like association rule learning, etc. to extract the customer’s buying pattern. Whereas if the retailer (or a business analyst) wants to analyze why a particular customer bought the product, then he/she can use analytics tools like Python programming to derive insights from it, such as “the product was bought 3 times in the last one month” and so on…..
Difference on the type of data
The type of data that each approach tends to work with is different – data mining works with structured data and analytics with unstructured one; however, both approaches can work with both types of datasets as well, depending on the use case at hand.
For example, Transactional data can be mined for patterns and trends to predict future outcomes, while social media feeds can be used for sentiment analysis and insights generation from customer reviews about products based on those reviews…. etc.
The main point is that these two methods work better rather than compete with each other. Hence they are called as ‘twin siblings’ in the data science world!!
Difference in Tools & Technologies
Tools and technologies used in Data Analytics and Data Mining:
Tools & Technologies used in Data Mining
Technologies used in Data Mining include Tableau and R Programming Language,
For example- Mining companies collect the datasets through database technologies like Oracle, etc. Then they use Data Mining technologies to extract insights and generate business value from these datasets.
Data Analytics Tools & Technologies
Tools Used for Data Analysis include Python programming language for analysis purposes. Machine learning tools like Scikit-Learn, and algorithms like clustering, and Google Prediction API are used for generating predictive models on the data sets mined by data scientists during the data analysis process to generate accurate predictions on unknown target variables.
When to use Data mining vs Data Analytics, to Achieve Business Goals?
When to use Data Mining
If you’re looking to analyze and understand trends and patterns in your business operations (i.e. sales data), then data mining would be the way to go, as it’s more geared towards analyzing and predicting trends from historical data rather than current/live data.
It is better suited to that task than data analytics which is more suited to understanding and analyzing live and current business performance indicators such as: customer churn rates, etc….etc.. etc…. and so forth!
When to use Data Analytics
In a situation where you need to identify key drivers in your business operations in order to optimize them and improve profitability and performance, you will use Data Analytics.
For example, identifying customers who are churning at high levels of frequency and then targeting them with retention campaigns to try and reduce this rate to acceptable levels so that overall profits are increased as a result. So, both approaches have their own strengths & weaknesses. Hence, it’s best to use them together to achieve the best results possible for your business operations!
When to use Both Data Mining and Data Analytics
Sometimes, It is best to use both to get the best of both worlds!
Catering the datasets and deriving crude but important insights, is Data Mining. Data analytics then uses these data and insights to build upon that and create a model based on the data. Data mining is a step in the process of data analytics.
For Example – you can use a Clustering Model to find out the leaving or unsatisfied customers in your business. Then use this model in conjunction with your analytics tool to understand the reasons behind why the customer is unsatisfied. So that you can take the necessary steps to reduce these reasons and therefore reduce the chances of this happening again in future.
For Example, identify customers who are likely to leave due to high product costs, etc. Use predictive analytics to target those customers with promotions offering discounts, etc. to reduce their cost and thus increase their likelihood of staying with you in the future = win-win situation for both your business and your customers alike!
Skills & Jobs in Data Mining vs Data Analytics
To succeed in the data world there are multiple skills that are important. Let’s see the jobs & salary requirements in Data Mining vs Data Analytics
Data Mining skills
Following are the skills required for data mining.
1. Statistical analysis
2. Programming skills
3. Data visualization
4. Machine learning
5. Big data technologies
6. Business acumen
7. Communication skills
Data Analytics Skills
Following are the skills required for data analytics
1. Math and Statistics
2. Data Management
3. Data Visualization
4. Critical Thinking
5. Problem-Solving
6. Communication
7. Business Acumen
Data Mining Jobs
If you’re interested in pursuing a career in data mining, here are some potential job titles to search for:
1. Data Mining Analyst
2. Data Scientist
3. Machine Learning Engineer
4. Business Intelligence Analyst
5. Big Data Engineer
6. Data Engineer
7. Database Administrator
8. Research Scientist
9. Quantitative Analyst
10. Data Analyst
Data Mining Salary
Experience, job function, firm size, and location are all variables in determining a data miner salary in India. Data miners may make anywhere from INR 5 to 15 lakhs annually on average, with top earners taking home INR 30 lakhs or more. Cities like Pune, Mumbai, Delhi, and Bangalore have greater salaries than smaller towns.
Data Analyst Jobs
Data analytics jobs are in high demand across industries, and there are many different job titles and roles that fall under the umbrella of data analytics. Some examples of data analytics jobs include:
1. Data Analyst
2. Business Intelligence Analyst
3. Data Scientist
4. Data Engineer
5. Machine Learning Engineer
6. Database Administrator
7. Analytics Manager
8. Data Visualization Specialist
9. Data Architect
10. Data Warehouse Developer
Data Analytics Salary
The pay for data analytics jobs in India varies depending on the position, industry, level of expertise, location and size of the firm. A fresher data analyst may make between INR 4 & 12 lakhs PA, whereas a senior data analyst can earn up to INR 20 lakhs. Salaries are greater in cities particularly in finance, healthcare, and technology. In India, data analytics is in great demand & pays well.
Conclusion
So In conclusion, both Data Mining and Data Analytics play an very important role in business today. They can be used effectively to help businesses understand and optimize their business processes. This will help increase revenues and profitability over time by improving customer retention and reducing churn rates.
Also by identifying what makes our business perform well, we can make better decisions.Ultimately, this leads to a better-performing business as a whole.
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