The difference between traditional analysis and large data analysis

Why large data analysis is important to business

The purchase of analysis software and application system vendors by major IT companies has become a daily phenomenon. We have seen the term "Big data analysis" used in many enterprise solutions.

Large data is a general term used to represent a large number of unstructured data that is not stored in an enterprise database in a traditional relational format. The following are the general features of large data.

The storage limit of the amount of data storage relative to the current enterprise TB (TERA BYTES) byte, defined in PB (PETA BYTES) bytes, Exa bytes, and higher capacity order.

It is often considered unstructured data and is not suitable for companies that are accustomed to using relational databases

Data generation uses non-traditional means of data entry, such as radio frequency identification (RFID), sensor networks, etc.

Data is time-sensitive and consists of data collection and related time zones.

In the past, the professional term "analysis" was applied to the business Intelligence (BI) world to provide tools and intelligence to gain insights through fast, consistent, interactive access to a wide range of possible information perspectives.

is very close to the concept of analysis, data mining has been applied to enterprises to maintain critical monitoring and mass information analysis. The biggest challenge is how to dig out all the hidden information through a lot of data.

Traditional Data Warehouse (DW) analysis relative to large data

The analysis of enterprise data toward a meaningful insight into the information in that content over a period of time is the reason why large data analysis differs from traditional data warehouse analysis. The following table summarizes some of the differences between them.

Large Data analysis use case

Based on use cases, enterprises can understand the value of large data analysis and how to solve traditional problems with the help of large data analysis. Here are some uses.

Customer Satisfaction and Assurance analysis: Perhaps this is the biggest area of concern for product-based businesses. In today's era, there is no clear way to measure product problems and issues related to customer satisfaction unless they appear in a spreadsheet in a formal way.

Information quality, it is collected through a variety of external channels, and most of the time the data is not cleaned.

Long-term solutions are provided to customers because the data is unstructured and cannot be associated with related issues.

The problem statements of classification and grouping are missing, which causes the enterprise not to group the problems.

From the above discussion, the use of large data analysis for customer satisfaction and assurance analysis will help companies gain insight into the much-needed customer attention settings, effectively address their problems, and avoid these problems in their new product line.

Competitor Market penetration Analysis: In today's highly competitive economy, we need a real-time analysis to measure competitors ' strong areas and their pain points. This information is available for a wide variety of web sites, social media sites, and other public areas. Large data analysis of such data can provide companies with information about the strengths, weaknesses, opportunities, and threats of their product lines.

Health care/epidemiological research and control: epidemics and seasonal diseases such as influenza begin in a certain pattern in the population, and they can spread to larger areas without early detection and control. This is one of the greatest challenges for both developing and developed countries. The problem with most of the current time is that people have different symptoms, and different medical staff treat them differently. There is also no common symptom classification in the population. Using large data analysis in this typical unstructured data will help local governments respond effectively to outbreaks.

Product Features and Usage analysis: Most products companies, especially consumer goods, continue to add a lot of functionality to their product lines, but it is possible that some features are not really being used by customers, and some functions are more used, An effective analysis of the data captured through a variety of mobile devices and other radio frequency identification (RFID) inputs can provide valuable insights into the product enterprise.

Future direction Analysis: The research team analyzes trends in a variety of businesses that are available through industry-specific portals and even common blogs. An ongoing analysis of this future data will help companies look to the future and bring those expectations into their production lines.

Summary

Large data analysis provides a new way for businesses and governments to analyze unstructured data, which has so far been rejected by the practice of data cleansing in a typical enterprise Data warehouse scenario. It is clear from the above use cases, however, that these analyses have a long way to go in improving the operations of the enterprise. We will see more products and application systems in this market in the coming days.

(Responsible editor: Liu Fen)