Basic approaches to Big data analysis

Big Data, a collection of data that cannot be captured, managed, and processed by conventional software tools within a manageable time frame, requires a new processing model to have greater decision-making, insight and process optimization capabilities to accommodate massive, high-growth, and diversified information assets.

Big data not only means big data, the most important thing is to analyze big data, only through analysis can get a lot of intelligent, in-depth, valuable information. Here are five basic aspects of big data analytics-

Predictive analytics capabilities: Data mining allows analysts to better understand the data, and predictive analytics allows analysts to make predictive judgments based on visual analysis and data mining results.

Data quality and Data management: data is processed by standardized processes and tools to ensure a well-defined and high-quality analysis result.

Visual Analytics: Data visualization is the most basic requirement for data analysis tools, whether it is for data analysis experts or ordinary users, and visualization can visualize data, let the data speak for itself, and let the audience hear the results.

Semantic Engine: Because the diversity of unstructured data brings new challenges to data analysis, we need a series of tools to parse, extract, and analyze data, and the semantic engine needs to be designed to be able to extract information intelligently from the document.

Data mining algorithms: Visualization is for people to see, data mining is to see the machine, clustering, segmentation, isolated point analysis and other algorithms let us drill into the data inside, mining value, these algorithms not only to deal with the amount of big data, but also to deal with the speed of big data.

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Basic approaches to Big data analysis