With the exponential growth of data generated by enterprises every day, sorting out TB-level information may become a problem, reducing the efficiency of analysis. Such a large data set takes longer to filter and organize correctly. For companies dealing with high-bandwidth multi-data streams, clear business and analysis of target routes are helpful to reduce decision-making mistakes.
It is very important to establish clear objectives for data, create parameters and filter out irrelevant or unclear data points. This helps to pre-screen data sets and makes filtering and classification easier by reducing noise. In addition, you can focus more on measuring specific KPIs to further filter out the correct data from the stream.
2. Simplify and centralize data flow
Another problem facing the analysis suite is to coordinate different data from multiple streams. Enterprises have internal, third-party, customer and other data, which must be regarded as part of a larger whole rather than viewed in isolation. Because different sources may use unique formats or different styles, retaining data may destroy data insight.
Before allowing multiple data streams to connect to your data analysis software, your first step should be to establish a process of centralized data collection and unified data collation. This centralization makes it easier to seamlessly input data into data analysis tools, and simplifies the method for users to find and manipulate data. Consider how to best set up the data flow to reduce the number of sources and eventually generate a more unified collection.
3. Filter the data before storing it.
Endless data raises questions of quality and quantity. Although people need more information, when data is surrounded by noise and irrelevant points, it loses its usefulness. Unrerefreshed data sets increase the difficulty of finding insights, managing the database correctly and accessing information later.
Before data warehousing and access, consider using appropriate procedures to clean up data to generate clean collections. Create a stage to ensure data relevance, and at the same time effectively filter out irrelevant data. In addition, please ensure that the process is as automated as possible to reduce the waste of resources. Realizing the functions of data classification and pre-classification is helpful to speed up the data filtering process.
4. Establish clear data management rules
At present, one of the biggest problems with data is data management. Because of the sensitivity of many sources-consumer information, sensitive financial details and so on-the question of who has access to information is becoming the core topic of data management. In addition, allowing free access to data sets and storage may lead to operational errors and deletions, which may lead to data corruption.
It is very important to establish clear rules about who has access to data, when and how. Creating a hierarchical authority system (read, read/write, manage) can reduce errors and avoid leakage. In addition, sorting data in a way that facilitates access to different groups helps to better manage data access without setting permissions for all team members.
5. Create a dynamic data structure
Many times, the stored data will be reduced to a single database, which limits the way to manipulate the data. Static data structure is effective for saving data, but it is limited when analyzing and processing data. On the contrary, data managers should pay more attention to creating a deeper analysis structure.
Dynamic data structure provides a way to store real-time data and allows users to connect points better. Using three-dimensional database, finding ways to quickly reshape data and creating more interrelated data islands can help realize more agile business intelligence. Generate databases and structures to simplify data access and interaction, rather than isolating them.
The field of data management and analysis continues to develop. It is very important for the analysis team to create a future-oriented infrastructure to provide users with the best analysis experience. By establishing the best data analysis standards and practicing them as much as possible, enterprises can significantly improve the quality of their decision-making suggestions based on data.