Data Analytics VS Data Mining - What is the Difference?

post written by Dimitris
Data Analytics vs Data Mining | Main Differences | Coara

As we cruize through the age of information, the amount of data we create is bigger than ever before. In the last two years alone, we generated more than 90% of the world’s data, and the growth curve is increasing exponentially.

It’s safe to say that data governs nearly all areas of life. And in a world like that, knowing how to properly categorize and analyze data is a very important factor for business success.

As many new companies start to make data-driven decisions, they often compare data analytics vs data mining. If you can’t tell the difference between the two terms, you’re in the right place. In this article, we’ll help you understand their main differences while offering a non-technical introduction to the components of data science.

Data Analytics VS Data Mining

Data mining and data analytics are different components of data science and operate in an interrelated manner.

Data mining explained

Data mining is a process used to discover patterns and relationships in raw data. The process does not aim to confirm a hypothesis or provide insights, but rather to find “interesting” relationships.

Here’s a typical example: A data engineer decides to look into a supermarket’s raw sales data. After reviewing the data, the engineer discovers a high correlation of men buying alcohol and flowers on Friday evening.

Data analytics explained

Data analytics are statistical techniques that help analysts draw conclusions from the data. The process is mainly used to test hypotheses and provide actionable insights to improve business decisions.

Following the example presented above, data analytics could look into the correlation of men buying alcohol and flowers on Friday evening and offer valuable insights to create targetted advertising campaigns.

The following illustration explains the main differences between the two terms, and how they relate to one another.

Difference between data analytics and data mining

data analytics vs data mining

In essence, data mining is an important component of data analytics. Some can even argue that the former needs to be performed before the latter. In order to extract valuable information from the data (data analytics), it is important to first recognize hidden patterns and relationships (data mining).

When looking at the image above, one of the most important differences is the presence of a hypothesis. For data analytics to provide clear answers, analysts will usually need to define particular expectations. On the contrary, data mining doesn’t need testing or proof. The latest emphasizes on patterns or trends between the data set (observations).

That being said, data mining is a goal-driven practice too; whether your goals are more abstract (discover patterns or trends) or specific (e.g. improve a recommender system), the process is meant to be done in a structured manner.

Delving into data science & its subcomponents

To better understand the two terms, and how these are interrelated, we should give a small intro to Data Science.

Data Science is a field that uses scientific methods and systems to extract actionable insights from raw data. A good way to explain its subcomponents is by illustrating the process of gradually filtering the raw data until a desired insight or conclusion is reached.

data analytics vs data mining

Each of the above processes is interrelated but operates independently, requiring a different type of skillset. When it comes to data analytics vs data mining, you can see what type of knowledge is needed in the previous illustration.

At this point, let us briefly talk about the terms you may not be familiar with.

What is Big Data?

Big data is a bulk of information that you can extract from a source. The total number of posts made on Facebook each day, the number of photos uploaded on Instagram, the purchases made in an e-commerce store - all this information can be labeled as big bata.

The information load is usually very large and unorganized, which is why it’s often referred to as “raw” data. To make sense of it, data scientists will need to “mine” this information, after which they will be able to analyze it. This is where data analysis comes in.

Data analysis vs data analytics

Data analysis and data analytics can often be treated as the same, but they are slightly different in scale. Data analysis is broader, with data analytics being one of its subcomponents.

  • Data analysis is the process of analyzing and arranging a dataset to examine it in further depth and extract useful information.
  • Data analytics are the techniques that a data scientist utilizes when performing data analysis.

The sequence of progression looks as follows:
Big data → Data Mining → Data Analysis → Data Analytics → Actionable insights

Big (raw) data does not provide any useful information for a company unless it is categorized properly. This is where data mining comes in. After data engineers uncover tendencies and patterns, data scientists will analyze the data using specific techniques (data analytics). This results in actionable insights that a business can use to improve.

A quick note on machine learning (AI)

Even though data science and machine learning (ML) don’t share any correlation, they are often discussed in the same context. The reason why this happens is that the latter helps machines improve their performance based on experience (information derived from data).

In other words, it is a field in computer science that helps computers improve in specific tasks by using statistical techniques. In many ways, ML is simply a reframed way to present modern statistics, even though many would argue otherwise.

So, how is machine learning changing data analysis?

When there is a large and diverse amount of datasets that need examination, data analysis often has its limitations. The size of data is proportional to the complexity of creating accurate models that lead to conclusions. The more the data, the more difficult it would be to analyze it.

This is the reason why machine learning is often seen as superior to statistics (common data analysis technique). They pose several differences, the most popular of which are:

  • Statistics (and statistical models) focus on understanding data based on mathematical formulas and their relationships to different variables, ML aims to provide the most accurate solutions possible.
  • Data analysis focuses on the examination of static data sets. In time, this can lead to inaccurate and unreliable results. On the contrary, ML processes data in real-time, provide more accurate results.

We will most likely write an article on the topic in the coming months, to better understand the connection between the two fields.

Conclusion

After getting a broad overview of data science and its different subcomponents, understanding data analytics vs data mining should now be easier. Each has its own unique application, but they both act synergistically to translate complex data into detailed insights.

  • Data Mining is mainly used for the process of extracting, filtering, uncovering past tendencies, and predicting from a data set. It is mostly performed by computer scientists and data engineers.
  • Data Analytics is more for analyzing and understanding the collected data and requires this implementation of visualizations. It is mostly performed by data scientists.

In the next few years, more companies will depend on their team’s ability to deconstruct and understand customer data. As such, we expect to see an increase in demand for data scientists and related tools in the future.

Frequently Asked Questions

The following Q&A will help you get additional information on data analytics vs data mining.

Is data mining part of data science?

Yes, data mining is part of data science and can be categorized as its subcomponent. Data science is the interdisciplinary field that deals with data examination, while data mining is simply the process that uncovers hidden patterns, trends, and correlations. You can refer to the illustration presented above to better understand the role data mining plays in the field of data science.

What are the most commonly used data mining techniques?

Before delving into the different techniques, it is important to understand that data mining is divided into predictive (forecasts the future) and descriptive (offers knowledge about the past).
The main techniques used for predictive data mining are Regression and Classification. On the contrary, descriptive data mining is performed through Association Rule Discovery and Clustering.

What is a data analytics report?

A data analytics report presents the findings and insights of your data analysis in a non-technical and clearly understandable manner. Based on the findings of the report, all involved parties are able to draw conclusions and approve or reject their hypothesis.