Now the data has become some of the company's "day." In recent years, more and more companies recognize the value of data analysis, data jumped into the big station wagon. In fact, all things are to be monitored and measured, resulting in a large number of data streams, usually faster than the company processed. The problem is, by definition, big data is very large, so small differences in data collection or errors can cause major problems, misinformation and inaccurate inference.
For large data, business-centric way analysis of its challenge is the only way to achieve this goal, namely to ensure that companies develop data management strategy.
However, there are some techniques to optimize your big data analytics, and minimize the possible infiltration of these large data sets "noise." Here are a few technical tips for reference:
Optimize data collection
Data collection is the first step in a chain of events, eventually leading to business decisions. Ensure that the relevant index data collected with interest the business is very important.
Definition of the company's impact on the type of data and the analysis of how to add value to the bottom line. Essentially, consider what this customer behavior and specific to your business, then use the data for analysis.
Data storage and management is an important step in the data analysis. We must maintain data quality and productivity.
Take out the garbage
Dirty data is the scourge of big data analytics. This includes inaccurate, incomplete or redundant customer information, the algorithm might cause serious damage and lead to poor results. Dirty data based decision making is a problematic scene.
Clean up data is essential, involving discarding irrelevant data and retain only high-quality, current, complete and relevant data. Manual intervention is not an ideal example, is not sustainable and subjective, so the database itself needs to be cleaned. This type of data in various ways to *** system, including time-related transfer, for example customer information or change the data stored in the silos, which may damage the data set. Dirty data could significantly affect the marketing industry and potential customers and other generation, but also because of financial and customer relationship business decisions based on erroneous information be adversely affected. The consequences are widespread, including misappropriation of resources, focus and time.
The problem of dirty answer is to ensure clean data into the system of control measures. Specifically, repeat free, complete and accurate information. Some applications and technology company specializing in anti-debugging and clean up data, these pathways should be analyzed for interested companies to investigate any large data. Health data is the primary task of marketing personnel, because knock-on effect of poor data quality can greatly reduce business costs.
In order to obtain the maximum benefit in terms of data, you must take the time to ensure the quality of decision-making and marketing strategies to provide an accurate view of the business is enough.
Standardized data sets
In most business cases, data from various sources and in various formats. These inconsistencies may be converted to incorrect results, which may significantly distort statistical inference. To avoid this possibility, it is necessary to determine the standardized framework or format of the data and strictly abide by it.
Data Integration
Today, most companies contains different autonomous departments, many companies have orphaned data repository or "islands." It's challenging, because changes in customer information from one department will not be transferred to another department, so they will make decisions based on inaccurate data source.
To solve this problem, the central data management platform is necessary, integrates all departments to ensure the accuracy of the data analysis, because any changes can be accessed by all departments immediately.
Data isolation
Even if the data clean, organized and integrated in there, it could analyze the problem. In this case, the data will be grouped into teams helpful, keeping in mind the goal of the analysis are trying to achieve. In this way, you can analyze trends within the sub-group, which may be more meaningful and more valuable. This is especially true when viewing may be highly specific trends and behavior has nothing to do with the entire data set.
. A few tips when big data analysis of large data cube Chen pointed out: data quality is very important for large data analysis. Many companies attempt to directly use the analysis software, regardless of the content of the system. This can lead to inaccurate inference and interpretation, which may lead to costly damage caused to the company. A well-defined, well-managed enterprise database management platform is the use of large data analysis indispensable tool.