Data is becoming increasingly important, some companies even data as their "day." In recent years, more and more companies have realized the value of data analysis can bring, and have jumped on the wagon big data. In fact, everything is now being monitored and measured, created a large number of data streams, usually faster than the company can handle. The problem is, by definition, a great big data, the data collection of small differences or errors can cause major problems, misinformation and inaccurate inference.
Five great tips to optimize data analysis
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." Below are five tips to make technical reference.
First, 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.
Second, the data should 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.
Third, 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.
Fourth 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.
Fifth, do 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.
For large data analysis it is essential to optimize five tips for big data analysis. Chen Cube in support of data quality. Many companies try to go straight to dive with analysis software, regardless of content into the system. Leading to inaccurate inference and interpretation, which can be expensive and cause damage to the company. A well-defined, well-managed database management platform is an indispensable tool for analyzing big data companies use.