Big data vs data mining techniques

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Although the terms of Big Data and data mining techniques are closely related, especially in today's digital context, among them are clear and important differences that we are going to briefly review.

By way of introduction, it is essential to anticipate that even when data mining refers to a set of techniques data extraction applicable for the analysis of structured information, It also makes it possible to explore and classify to look for patterns in Clave de Big Data.

This is possible thanks to the execution of a set of data analysis techniques., that enable predictive and trend analysis. Despite this, in no way can both terms be assimilated.

The evolution of data mining

In general, data mining techniques enable consultation and analysis. For this, are oriented to the discovery of patterns, trends, profiles or other relationships that are of interest and that, being present in the information, they remained hidden.

Thanks to proper treatment, this objective is achieved with data that is stored in conventional relational systems (Data Warehouse) O, if it is heterogeneous or unstructured information that comes from different sources, then you need to use Big Technologies. Data.

That treatment had to to adapt to new needs. Being in both cases the business knowledge baseSince it is now in a fully digital context, we have gone from confirmation or verification to finding. In other words, to the detection of something hidden through the implementation of predictive models.

Data mining in the key of Big Data

Big data o Big data, for his part, see huge amounts of information from which we can extract value through data science, and doing so is feasible thanks to an innovative technology that goes by the same name.

Thanks to Big Data technology, able to capture, to stock, quickly and accurately manage and process this data, it is feasible to take advantage of them. Fundamentally, focuses on predictive analytics and trend detection.

It does it using different techniques, including data mining. Through the definition of models and the use of different technologies, the goal is to turn data into a high-value asset.

Using it, we are able to identify common patterns for, among other purposes, find new niches, establish key characteristics about present or future clients, generate parameters, metrics or processes.

Compared to traditional analysis, it is a paradigm shift, a revolution without going back, that transforms the way of doing business, increasing the profitability and productivity of companies.

Extract value from Big Data

As we mentioned, for him data processing Robust frameworks are required, agile and scalable, as Hadoop, the leading technology in accessibility and high performance at low cost. But in addition, one or the other analysis tools are essential to promote decision-making processes and, in summary, get the most out of Big Data.

The urgent need to analyze big data in real time has generated new complementary solutions to help guarantee its performance.

Although data analysis to make better business decisions is not new, and the same can be said for data mining, the context of today's digital universe is, where the data comes from different sources, has different formats and has multiplied its amount. exponentially.

Data mining, a versatile resource

To that end, the vast majority of the solutions used for its analysis are innovative, and others, such as data mining techniques, They must respond to this new reality in order to offer the result that is expected of them.. To that end, data mining is versatile and has great potential to help us gain comparative advantages that differentiate us from our competitors. In the same way that it can help us to carry out a conventional analysis, it is a good resource to extract value from Big Data.

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