The Difference Between Data Mining and Data Analysis
The amount of data being generated is exponentially growing every day. The process of getting that data is not complicated since there are numerous methods which can be used. However, getting meaningful information from that data is very complex and time-consuming.
Collected data is usually stored in a data warehouse, a place where data gathered from various sources is kept. Some of these sources include corporate databases, data from external sources and summarized information from internal systems.
The analysis of this data requires statistical analysis, simple query and reporting, more complex multidimensional analysis and data mining.
Data mining and data analysis are subsets of business intelligence (BI). The BI also incorporates data warehousing, online analytical processing, and database management systems.
Both data mining and data analysis are frequently used in customer relationship management CRM) to analyze patterns and query customer databases. However, many view data mining and data analysis as the same thing. They believe the two technologies perform the same tasks. This article will highlight the difference data mining and data analysis.
Data mining is the process of extracting hidden predictive data from large databases. This technology is very powerful and has the potential to assists companies concentrate on the most important information in their data warehouses. Data mining tools assist in predicting the future behaviors and trends, and this allows businesses to make decisions which are proactive and knowledge-driven.
Data mining offers automated and prospective analysis, and this goes beyond the analysis of past events provided by retrospective tools. Data mining tools have the capability assist businesses to get results and answers of issues very quickly. Most of these issues took a lot of time in the past using traditional methods.
The tools search data for hidden patterns, and this assists businesses to get predictive information which experts miss because it lies beyond their expectations.
The scope of data mining
Data mining is derived from the similarities between searching for valuable business information in a large database and mining mountain for a vein of valuable mineral. Both processes require either going through large volumes of material or keenly searching the materials to discover its value. Data mining technology provides the following capabilities for it to generate business opportunities:
- Automated prediction of trends and behaviors – it automates the processes of finding predictive information from large databases. Questions that used to take long before being answered traditionally can now be answered very quickly.
- Automated discovery of previously unknown patterns – data mining tools can easily go through databases and identify hidden patterns. Mostly this requires a one step process.
This is the process of analytically using logical or statistical methods to illustrate, describe, shrink, summarize and evaluate data. In data analysis, there are several analytic procedures which are used to provide a way of drawing inductive suggestions from data and differentiating the signal from the noise present in the data.
While data analysis in qualitative research can at the time include statistical procedures, sometimes the analysis becomes an ongoing repetitive process. This process requires data to be collected and analyzed almost at the same time.
Researchers analyze for patterns in observations throughout the entire data collection phase. The form of the data and the specific quantitative approach taken determines the shape of the analysis be done.
Considerations in data analysis
Having the necessary skills to analyze the data
Many people believe that they have the appropriate training to assist them to demonstrate a high standard of research practice. However, unintentional misconduct to poor data analysis can make the intended results not to be achieved.
Drawing unbiased inference
The main purpose of data analysis is to differentiate between an event occurring as either reflecting an exact effect or a false one. Biases in collecting data or selecting the analysis method increases the possibility of drawing a biased inference.