Big data analytics and data mining are not the same. Both of them involve the use of large data sets, handling the collection of the data or reporting of the data which is mostly used by businesses. However, both big data analytics and data mining are both used for two different operations. Let’s look deeper at the two terms.
Big data analytics
This is the process of analyzing larger data sets with the aim of uncovering useful information. Examples of this information include market trends, customer preferences, hidden patterns and unknown correlations. The analytics findings usually lead to new revenue opportunities, improved operational efficiency, more efficient marketing and other business benefits.
Companies often rely on big data analytics to assist them in making strategic business decisions. Big data analytics enable data scientists, predictive modelers and other professionals in the analytics field to analyze large volumes of transaction data. They can also use big data analytics to analyze data which might not have been discovered by conventional business programs. This includes:
- Social media content and social network activity reports,
- Data from sensors connected to the Internet of Things,
- Customer emails and survey responses,
- Web server logs and Internet clickstream data.
The greatest challenge that companies face while implementing big data analytics include the high costs of hiring experts and the lack of internal analytics. The amount of data to be handled and its variety also presents a big challenge to the management. This mostly includes data quality and its consistency.
Additionally, it can be challenging to integrate Hadoop systems and data warehouses. However, some vendors have started to offer software connectors between Hadoop and relational databases and other data integration with big data capabilities.
Data mining, also known as data discovery or knowledge discovery, is the process of analyzing data from different viewpoints and summarizing it into useful information. This information is used by businesses to increase their revenue and reduce operational expenses. The software programs used in data mining are amongst the number of tools used in data analysis.
The software enables users to analyze data from different angles, classify it and make a summary of the data trends identified. Technically, data mining involves the process of discovering patterns or relationships in large areas of related databases.
The actual data mining task is the automatic or semi-automatic analysis of large datasets. This is done to assist in the extraction of previously unknown and unusual data patterns. These include detecting abnormalities in records, cluster analysis of data files and sequential pattern mining. Database techniques like spatial indices are commonly used in these processes.
After these processes, the patterns can be seen as the summary of the input data and can be used in further analysis like predictive analytics or machine learning. For instance, multiple groups of data can be identified through data mining steps.
These groups can be used to acquire more accurate prediction results through decision support system. The data collection, data preparation and the result interpretation and reporting are not part of the data mining steps. However, they are additional KDD processes.
Data mining parameters include:
- Association – this is looking for patterns where events are connected.
- Sequence or path analysis – here, we look for one event which leads to another event later.
- Classification – this is looking for new patterns. It may result in changes in the way data is organized. However, that’s normal.
- Clustering – discovering and documenting groups of facts which were not known.
- Forecasting – finding data patterns which can lead to reasonable future predictions.
Data mining techniques are commonly used in different research fields like marketing, cybernetics, mathematics and genetics. Web mining is another type of data mining, which is commonly used in customer relationship marketing. It utilizes the large data volumes of data collected by websites to search for patterns in user behavior.