In the era of big data, what data analysis can t do?

26- What big data can't do.

Big data and data analytics are not exactly the same concept, they are slightly different. To put it simply, big data refers to massive and complex data collections, while data analysis refers to the process of processing and analyzing data.

Specifically, big data typically includes structured data (e.g., data in databases) and unstructured data (e.g., web logs and social content). These datasets are so large that they are almost impossible to process and manage with traditional methods and tools, requiring specialized technologies and platforms to store, process, and analyze these data.

Data analytics refers to the process of extracting, transforming, and generating useful information on big data or other data sets using tools and algorithms. Data analytics can help businesses or organizations discover new business opportunities, identify market trends, optimize operational processes, and more, thus providing a reliable basis for business decisions.

Therefore, although there is a certain correlation between big data and data analysis, their concepts and purposes are different. Big data is a collection of data, and data analysis is the process of processing and analyzing these data sets, both of which are very important concepts in the field of data.

Literally, big data analytics is defined as "the process of examining large data sets (i.e., big data) containing various data types to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information." ”

Big data analytics companies and businesses often see more business benefits, including more effective marketing campaigns, new revenue opportunities, improved customer service, more efficient operations, and competitive advantage. Companies implement big data analytics because they want to make more informed business decisions. Big data analytics provides data analytics professionals, such as data analysts and modelers, with the ability to analyze big data from a number of different sources, including transactional data and other structured data.

Big data analytics refers to the analysis of huge amounts of data.

1. Visual analysis.

2. Data mining algorithms.

3. Ability to analyze sexuality.

4. Semantic engine.

5. Data quality and data management.

1.Visual analysis: The users of big data analysis include big data analysis experts and ordinary users, but the most basic requirement for big data analysis is visual analysis, because visual analysis can intuitively present the characteristics of big data, and at the same time, it can be very easy to be accepted by readers, just like looking at pictures and talking.

2.Data mining algorithm: the theoretical core of big data analysis is the data mining algorithm, a variety of data mining algorithms based on different data types and formats can more scientifically present the characteristics of the data itself, and it is precisely because of these statistical methods recognized by statisticians all over the world that can go deep into the data, dig out the value of fairness, and on the other hand, because of these data mining algorithms can process big data faster.

3.Sexual analysis ability: One of the most important application areas of big data analysis is sexual analysis, mining characteristics from big data, through the scientific establishment of models, and then new data can be brought in through the model, so as to improve the future data.

4.Semantic Engine: Big data analysis is widely used in network data mining, which can analyze and judge user needs from user search keywords, tag keywords or other input semantics. The result is a better user experience and ad matching.

5.Data quality and data management: Big data analysis is inseparable from data quality and data management, high-quality data and effective data management, whether in academic research or commercial applications, can ensure the authenticity and value of analysis results.

The foundation of big data analysis is the above five aspects.

What is Big Data? Is it an operating model, a capability, a technology, or a collection of data? What is the difference between what we call "big data" today and "data" in the traditional sense in the past?

What are the best aspects of big data? Wait a minute. Of course, I am not an expert or scholar, and I cannot give an authoritative definition that convinces everyone.

Let's start with the difference between "big data" and "data", in the past, we said "data" largely refers to "numbers", such as the number of customers, business volume, operating income, profit, etc., are all numbers or simple texts that can be encoded, these data analysis is relatively simple, and traditional data solutions (such as databases or business intelligence technology) can easily cope with it in the past; And what we call "big data" today does not simply refer to "numbers", but may also include "text, audio, ......It covers a wide range of content, such as our blog, Weibo, light blog, our audio ** sharing, our call recording, our location information, our review information, our transaction information, interactive information, etc., all-inclusive. In formal terms, "data" is structured, while "big data" includes "structured data", "semi-structured data", and "unstructured data". "Structured", "semi-structured" and "unstructured" may be difficult to understand literally, but here I try to use my language to see if I can express it vividly

Since the data is structured, the data analysis can follow certain existing rules, such as through a simple linear correlation, the data analysis can roughly ** the next month's operating income; Big data is semi-structured and unstructured, and the laws it follows in the analysis process are unknown, it simulates by integrating all aspects of information, it evaluates the evidence in the form of analysis, assumes the response result, and calculates the credibility of each possibility, through big data analysis we can accurately find the next market hotspot.