If you are not familiar with data science, the concept of big data vs data mining can be confusing. However, there are important differences.
It is essential to remember that data mining and big data are entirely different concepts. Despite these important distinctions, the key similarity is that both deal with the collection and analysis of large data sets. With the intelligence provided by these processes, enterprises can make more astute business decisions. However, data mining and big data comprise two different elements of this process. Put simply, big data is a data set that is too unwieldy for traditional data handling architectures. Conversely, data mining refers to the process of examining large data sets to extract valuable information. Below, we examine the question of big data vs data mining more closely.
What defines big data?
As briefly described above, big data is a byword for a large data set. A data set enters the big data realm when it outgrows traditional databases and handling architectures. For instance, if data are difficult to analyze in Microsoft Excel, then they become big data. Furthermore, big data comes in numerous forms. These include structured, semi-structured and unstructured formats. For example, the enormous amount of pictorial data that social media generates is unstructured, whereas a catalog of sales transactions constitutes structured data.
As a result, big data can facilitate a contemporary approach to business strategy. Companies can analyze these data to gain insights that inform decision making. In order to leverage the most valuable insights, companies need to address the three V’s of big data: volume, variety, and velocity. With a wide variety of data that is effectively managed and delivered in real time, businesses can drive streamlining and growth.
How is data mining different?
When considering big data vs data mining, the key point is that data mining refers to the analysis of big data. From here, data mining aims to uncover relevant or useful intelligence. Often, businesses collect big data automatically and indiscriminately; therefore, data mining is the process of unearthing specific data from large data sets. It is this information that will directly inform decision making and corporate strategy.
Data mining can involve many different software packages and analytics tools. The process can be automatic or manual, depending on the demands of the project. In essence, data mining describes sophisticated searching protocols that return specific results from large databases. For instance, a data ming tool might examine decades of financial information to calculate expenses for any given period. Analysts can then cross-reference this information to discover patterns or trends.
Big data vs data mining: A summary
In summary, when considering big data vs data mining, big data is the asset and data mining describes the method of intelligence extraction. However, data mining does not depend on big data; software packages and data scientists can mine data with any scale of data set. However, the value of big data is contingent on data mining. If data mining cannot uncover actionable insights, big data is of no use. Although big data in itself fulfills the variety and volume criteria, data mining delivers business intelligence at a rapid pace.