top of page
  • Writer's pictureAndriy Link

Big Data vs Data Mining: What is the Difference?

Technologies never stop evolving. The global markets are constantly producing innovations to meet human demand for automation and convenience. With all the technologies present, humans produce data volumes exponentially. Information is stored in media and audio files, written in e-documents, and on different devices. Information introduces Big Data – large data sets that can be analyzed computationally.


By 2025, Statista projects that there will be more than 181 zettabytes of human-produced data. With such a quantity of data sources, it would be unwise not to use the data obtained. Therefore, we have Data Mining – a practice to analyze the given data and receive/create new information.

By 2025, Statista projects that there will be more than 181 zettabytes of human-produced data. With such a quantity of data sources, it would be unwise not to use the data obtained. Therefore, we have Data Mining – a practice to analyze the given data and receive/create new information.

At first glance, these notions are completely different. To understand what exactly makes Big Data differ from Data Mining, let’s discuss these concepts in a nutshell.


Big Data Explained

The term “big data” has been coined since the 1990s, owing to John R. Mashey - an American computer scientist, director, and entrepreneur. It refers to data sets of enormous sizes. This voluminous information is beyond the ability to capture, curate, manage, and process unless using specific software tools.


Big Data is formed from structured, unstructured, and semi-structured data. However, only unstructured data has the biggest focus and usage. The size of Big Data has no universal quantity because this number is constantly increasing in capacity. Moreover, to process these changing volumes of information, newer tools and techniques are required every now and then.


Characteristics

When we speak about Big Data, we understand it according to the following six V criteria:

  • Volume. Quantity of data that has been generated or stored (a lot larger than terabytes and petabytes).

  • Variety. Data type and nature.

  • Velocity. The speed with which data is being generated and processed.

  • Veracity. Data reliability impacts data quality and data value.

  • Value. The significance of information gained via processing and analysis of large datasets.

  • Variability. Big data has formats, structures, or sources with their own characteristics that are constantly changing.

Tools

Big Data has the core components and ecosystem it uses. McKinsey Global Institute defines the following applications:

  • Techniques: A/B testing, Machine Learning, and Natural Language Processing

  • Technologies: business intelligence, cloud computing, and databases

  • Visualization: charts, graphs, and others

Industry Applications

Big Data has the potential to be unveiled almost in every industry there is. The other question is how it is going to be used to receive direct benefits. The industries that make use of Big Data are:

  • Government

  • International development

  • Finance

  • Healthcare

  • Education

  • Media

  • Insurance

  • Internet of Things (IoT)

  • Information technology

As it was mentioned, Big Data stands for large data sets that humanity can analyze with the help of appropriate tools and techniques. However, the process of analyzing data and creating new knowledge out of it is called Data Mining. Therefore, let’s find out more about it.


Data Mining Explained

Data Mining processes different large data sets and aims to discover data patterns using machine learning, statistics, and database systems. It belongs to the subfield of computer science and statistics. The main goal of Data Mining is to intelligently extract information and transform it into usable valuable information for others.


The first to use this term was Michael Lovell in 1983. Within years, this term had different positive and negative connotations. Nowadays, it is used interchangeably with “knowledge discovery”.


Characteristics

Before you use the Data Mining algorithms, there is a need to assemble the target data set. As Data Mining finds patterns that are 100% present in the data, the target data set must be large enough to contain these patterns. Also, it has to be concise enough to meet the acceptable time frame. This process is called pre-processing.

There are six classes of Data Mining tasks:

  • Anomaly detection. Identification of unusual data records or errors that require further investigation.

  • Dependency modeling. Relationships between variables or the search of such.

  • Clustering. Discovery of groups and structures that are similar (patterns).

  • Classification. Structure generalizations to apply to new data.

  • Regression. Search for a function that models data without errors and estimates relationships among data sets.

  • Summarization. Compact data set presentation via visualization and reporting.

After the data has been discovered it is crucial not to misuse it. However, anything can happen if you have tested lots of hypotheses at the same time. For this reason, it is vital to perform proper statistical hypothesis testing. With hypothesis testing you can make probabilistic statements about certain parameters.

Tools

According to Javatpoint, the following are the latest and most popular Data Mining tools in use:

  • Orange Data Mining

  • SAS Data Mining

  • DataMelt Data Mining

  • Rattle

  • Rapid Miner

Modern technological stack allows the usage of multiple open-source components mostly as Python modules. It requires custom data engineering as a service which Sencury is happy to provide.


Industry Applications

  • Healthcare and Structured Health Monitoring

  • Customer Relationship Management

  • Fraud Detection and Intrusion Detection

  • Manufacturing Engineering

  • Financial Data Analysis

  • Retail

  • Telecommunication

  • Media and entertainment

  • Logistics and trucking

  • Biological Data Analysis

  • Other Scientific Applications

This is the basic information you should know about Big Data and Data Mining. Let’s also make a comparison of both.

Big Data vs Data Mining: Comparison

To understand the basic differences between Big Data and Data Mining, let’s structure and visualize them in a comparison table.

Big Data vs Data Mining: Comparison   To understand the basic differences between Big Data and Data Mining, let’s structure and visualize them in a comparison table.    Big Data   Data Mining   Is defined as data sets of enormous sizes   Is defined as data patterns/relationships   The purpose is to discover insights from large data sets   The purpose is to discover patterns or anomalies within large data scope   Is used to understand relationships between data   Is used to analyze the obtained data and extract new value from it   Answers the question of “Why?” with regards to data   Answers the question of “What?” with regards to data    Contains only large volume data   Contains both large and low volume data   Is a base for machine learning and artificial intelligence applications and is applicable in almost every industry   Use cases include such industries as finances, logistics, healthcare, telecommunications, media and entertainment, e−commerce, education, IoT, etc.

From the table above it is clear that Big Data is a whole concept that includes tools and techniques to process data. However, Data Mining is one of the tools that helps to deal with Big Data and find value within it.


Sencury’s Experience with Data Mining and Big Data

Our team provides customers with a broad scope of Data Engineering Services. Sencury has vast data engineering knowledge and experience. We create data-driven solutions that can enhance your performance optimization.

Our data engineering can enhance your business decision-making by leveraging business intelligence and advanced reporting! Different organizations accumulate huge amounts of data daily. With data engineering, it becomes easier to make use of this data the right way. Sencury’s data engineers can assess your raw data and create predictive models that display short-and long-term trends. We also identify data insights, find correlations, and derive new business data that is valuable for your company's growth. Interested in trying? Contact us for the details.

1 view0 comments

Recent Posts

See All
bottom of page