Shulou(Shulou.com)06/03 Report--

Now the data has become the "day" of some enterprises. In recent years, more and more companies have realized the value of data analysis and jumped into big data station wagons. In fact, everything is being monitored and measured now, generating a large number of data streams, usually faster than companies. The problem is that, by definition, big data is very large, so small differences or errors in data collection can lead to major problems, misinformation, and inaccurate inferences.

For big data, analyzing its challenges in a business-centric manner is the only way to achieve this goal, that is, to ensure that the company has a data management strategy.

However, there are techniques that can optimize your big data analysis and minimize the "noise" that may infiltrate these large data sets. Here are a few technical tips for reference:

Optimize data collection

Data collection is the first step in the chain of events that ultimately leads to business decisions. It is important to ensure that the data collected is relevant to indicators of interest to the business.

Define the types of data that affect the company and analyze how to add value to the bottom line. In essence, consider customer behavior and how it is relevant to your business, and then use this data for analysis.

Storing and managing data is an important step in data analysis. Data quality and analytical efficiency must be maintained.

Take the garbage out of here.

Dirty data is the scourge of big data's analysis. This includes inaccurate, redundant or incomplete customer information, which may cause serious damage to the algorithm and lead to poor analysis results. Making decisions based on dirty data is a problematic scenario.

Cleaning up data is critical and involves discarding extraneous data and retaining only high-quality, up-to-date, complete and relevant data. Human intervention is not an ideal example, it is unsustainable and subjective, so the database itself needs to be cleaned up. This type of data is transferred to the system in a variety of ways, including time-related transfers, such as changing customer information or storage in data islands, which can corrupt the dataset. Dirty data may affect obvious industries such as marketing and prospect generation, but financial and customer relationships can also be adversely affected by business decisions based on misinformation. The consequences are common, including misappropriation of resources, priorities and time.

The answer to this dirty data problem is the control measures to ensure that the data entering the system is clean. Specifically, repeat free, complete and accurate information. Some applications and companies specialize in anti-debugging technology and cleaning up data, which should be investigated by any company interested in big data's analysis. Data health is the top priority of marketers, because the knock-on effect of poor data quality may greatly reduce the cost of the enterprise.

To get the most out of the data, you must take the time to ensure that the quality is sufficient to provide an accurate view of the business for decision-making and marketing strategies.

Standardized data set

In most business cases, data comes from a variety of sources and formats. These inconsistencies can translate into erroneous analytical results, which can greatly distort statistical inferences. To avoid this possibility, a standardized framework or format for data must be identified and strictly adhered to.

Data integration

Today, most enterprises contain different autonomous departments, so many enterprises have isolated data repositories or "islands". This is challenging because changes in customer information from one department will not be transferred to another, so they will make decisions based on inaccurate source data.

In order to solve this problem, the central data management platform is necessary and integrates all departments, thus ensuring the accuracy of data analysis, because any changes can be accessed by all departments immediately.

Data isolation

Even if the data is clean, organized and integrated there, it can be an analytical problem. In this case, it is helpful to group the data into groups, while keeping in mind the goals that the analysis is trying to achieve. In this way, trends within the subgroup can be analyzed, which may be more meaningful and valuable. This is especially true when looking at highly specific trends and behaviors that may not be relevant to the entire dataset.

Several skills in doing big data's analysis. Big data, the Rubik's cube in Zhongchen, pointed out: data quality is very important for big data's analysis. Many companies try to use analysis software directly, regardless of the content of the system. This can lead to inaccurate inferences and explanations, which can lead to high costs and damage to the company. A well-defined and well-managed database management platform is an indispensable tool for enterprises to use big data for analysis.

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