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December 24, 2021 - 6 minutes

The Differences Between Data Analytics and Data Mining

Data analytics and data mining work together to help you make better business decisions.

Juliette Carreiro - Tech Writer

Data mining and data analytics are both central to an organization’s performance. Why? Well, an organization’s competitive edge is determined by its ability to make the most of data and data refers to different numbers and values collected and after it’s processed, it becomes factual information that’s key for decision making. Although different, data analytics and mining of data are related and used to optimize the performance of businesses.

In this article, we’ll dive into the differences between data analytics and data mining to help you better understand the role each plays and how they work together to help companies make better business decisions. 

What is Data Analytics?

Data analysis is a deep analysis of raw data to look for patterns, trends, and metrics on a dataset. Specialized computer systems work on raw data to identify trends and make conclusions.

There are many different kinds of data analytics and the patterns and trends found in this analysis improve a business’s efficiency and performance.

A successful data analytic process will tell you the position of your business and predict the future, telling you what you need to know to improve your business’ processes. There are several types of data analysis but let’s review some of the most common kinds to help you better understand how data analytics can be used in your day-to-day:

  • Descriptive analytics usually describes what has happened in the past. Have your sales gone up? Are more customers coming? Which products are fast-moving?

  • In diagnostic analytics, you apply a bit of hypothesis here to tell you why something happened. You use the results from the descriptive analytics to find out why things happened that way. Did the last advertisement campaign bring any changes to your sales?

  • Predictive analytics tells you how things will be in the future, using previously collected data to predict if the same occurrence will happen again.

  • Prescriptive analytics helps to make decisions on what should be done. Specialized computer systems do prescriptive analytics to find patterns from large volumes of data.

Data analysis begins with determining how to group the data. Data can be put in different categories, including age, gender, or income and can be collected through various sources.

After the data has been collected and categorized, use specialized programs to organize the data collected. Finally, you must ensure the data is correct and accurate before being processed.

Data Mining

Mining data is finding useful information from large volumes of data. It is done systematically and successively to uncover hidden trends and patterns in an extensive dataset. These hidden patterns and trends tell you about your customers and help you make decisions that bring more sales, providing reliable information beneficial for marketing campaigns.

Also, mining data can help identify where you can cut costs to reduce your operational expenses. 

This data can also be used to train the machine learning models used in artificial intelligence. Different types of mining data include:

  • Smoothing: to remove noise from the data, you use a smoothing algorithm to visualize trends.

  • Clustering: clustering is putting together groups with the same characters, helping marketers identify groups within their target market.

  • Classification: this comes after clustering and is when you put items or people into categories. 

  • Association: association identifies data points that are connected.

  • Anomaly detection: this quickly detects fraud by finding data that doesn’t fit the normal pattern; this is beneficial for banks and businesses looking to detect fraud.

  • Regression: regression is a statistical tool that helps to predict the future.

  • Text mining: text mining determines how often individuals use certain words, sending alerts to employees if there are data leaks.

  • Summarization: with data summarized in an easy to understand format, summarization allows you to calculate the average from a particular data set.

When beginning to data mine, the first step is to eliminate conflicting information. Different data sources are then integrated and connected and the next step is to select data from the dataset. After, aggregation operations to transfer data into a form applicable for mining is completed.

Data specialists will then choose their desired application to extract data before presenting the result to the customer through data visualization.

The Differences Between Data Analytics and Data Mining

While there are many differences to keep in mind when looking at data analytics and data mining, here are seven of the main ones:

  • Workforce: a single person, preferably a specialist with coding skills, is responsible for mining data. On the other hand, a team of specialists execute data analysis.

  • Function: when mining data, you want to find the hidden patterns in the data sets. However, in data analysis, you analyze the data sets.

  • Goal: when mining data, your goal is to make the information you have usable and identify patterns. For data analysis, your goal is to make data-driven decisions and hypotheses.

  • Method: you apply mathematical methods such as algorithms in the process of mining data. In data analysis, however, you use business intelligence to carry out your study.

  • Data sets: when mining data, you use large volumes of data collected in the data warehouse. For data analysis, you can use either small, medium, or large volumes of data.

  • Knowledge: you apply machine learning in the mining of data; in data analysis, you need to use subject and computer science knowledge.

  • Output: the output you get from mining data is data patterns and trends. As for data analysis, your results are actionable insights and hypotheses.

Possibly the most important difference is the following: before you get valuable information from the data, it is important to recognize patterns and learn the trends. Therefore, you need to data mine before carrying out data analysis. As a business, you need to apply both to optimize your business’s performance and cut your operational costs. And, most importantly, increase the sales of your company–which is why both data mining and data analytics are so crucial. 

If you’re looking to improve your overall data skills and bring both data mining and data analytics to your company, Ironhack’s Data Analytics Bootcamp is the perfect choice for you. From part-time flexible learning to intensive courses you can tailor the bootcamp to suit you as you deep dive into the world of tech.

Ready to jump into the world of data? We’ll see you in class.

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