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Big Data Analytics vs Data Mining

Big Data Analytics vs Data Mining

Big Data Analytics vs Data Mining

Tuesday January 08, 2019,

null min Read

It is a popular misconception that big data analytics and data mining are the same thing, they are not! The commonalities between both these terms are that they use large datasets, they handle the collection of data and also report the data which is used by the business. With that being said, it must be understood that big data analytics and data mining are used for two separate operations. Let’s have a deeper look at these terms for a better understanding.

Big data analytics

The process of analyzing large datasets with the objective to glean useful information is big data analytics. This information may be in the form of hidden patterns, unknown correlations, customer preferences etc. The findings revealed using big data analytics usually lead to better operational efficiency, new revenue opportunities, effective and efficient marketing campaigns and other business benefits.

Organizations usually count on big data analytics to aid them in making strategic business decisions. Data scientists, predictive modelers and analytics professionals make use of big data analytics to analyze large volumes of transactional data. Big data analytics also helps discover and analyze data that may not have been discovered by conventional business programs. These may include:

  •     Activity on social networks and content on social media
  •         Sensors connected to the IoT providing data
  •         Emails from customers as well as responses from surveys
  •         Clickstream data from the internet as well as web server logs

While implementing big data analytics, the biggest challenge faced by companies is the rather high cost of hiring experts in the field and the lack of internal analytics. The 4 V’s of data also pose a huge challenge to the management. The quality and consistency of data is also required to be stable.

Sometimes, a challenge may be faced in the form of integration of Hadoop systems with data warehouses. Using this as an opportunity, software firms now offer software connectors to link Hadoop and relational databases as well as separate data integration with big data capabilities.

Data mining

For people familiar with data mining, you will also know that it is termed as data discovery or knowledge discovery. It is the process of gathering data from various viewpoints, and summarizing it into useful information. The information summarized is used by organizations to increase revenue and reduce operational costs. It is often referred to as the “Knowledge discovery in databases” step.

The data mining software in use allows for users to analyze data from varied angles, classify it and summarize the data trends identified. Simply put, data mining focuses on the minute details within the data and big data focuses on the relationships between the data.

Data mining focuses on what the data represents. It is predominantly aligned with statistical analysis with a focus on prediction. Data mining is usually done to identify abnormalities in cluster analysis of the data files, records and sequential pattern mining. It helps uncover previously unknown patterns or unusual patterns.

Once all these processes are complete, the patterns will be seen as the summary of input data and can further be used in predictive analytics or machine learning. Through these data mining steps, multiple groups of data can be identified.

Using decision support system, more accurate prediction results can be obtained with the use of these groups. When we speak about the data mining steps, the data collection, preparation, result interpretation and reporting are not part of the steps. They are considered as additional KDD processes.

Some of the key data mining parameters include:

  •        Association – Finding patterns where events are connected.
  •        Sequence or path analysis – In this step we keep an eye out for one event which leads to another
  •         Classification – This is keeping an eye out for different or new patterns. This may cause a change in the organization of data. But that is normal.
  •       Clustering – Clustering is where groups of facts that were unknown earlier are discovered and documented.
  •         Forecasting – Using the data patterns to make reasonable and stable future predictions.

Data mining is usually seen in various research fields like mathematics, genetics, marketing and cybernetics. For customer relationship marketing, web mining is done which is another type of data mining. Large volumes of data which are collected by websites are analyzed to find patterns in user behavior. 

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