4 basic steps for how to manage data effectively.

 2020-04-13

4 basic steps for how to manage data effectively.

4 basic steps for how to manage data effectively.

It is normal if your IT team is having data management trouble. Most of the businesses nowadays are facing the challenge of how to leverage the amount of data without increasing the scale and cost of the IT team.

Of course, there are still suitable solutions for the above challenge. Here are 4 steps to manage data effectively  

  1. A comprehensive view

The prerequisite condition is to understand the problem. Likewise, you need a comprehensive and profound look of your business’ data.

If you do not understand which data is important to prioritize the human resource, and which data is not important, the storing and analysis data cost can be wasted.

You can use the specialized software, based on metadata, to determine factor like when a detailed data file opened last, changed, by whom and by which application, etc. The data which cannot be accessed, or edited after a certain time (1 month, 1 year, etc.) should be determined as low-priority data and transferred to low efficiency and cost storage system.

Before starting handling other information management problem, you need to observe comprehensively how the data is handled in the whole business, instead of the retail system.

 

  1. Storage solution integration. 

According to a survey in 2016, most of the large-sized enterprises in the USA are using over 20 different data storage solution. Even small-sized businesses are running many storage systems at the same time.  As the business grows, the scale of these systems is also expanding and costing the company a considerable amount of both infrastructure and software.

Besides, the difficulty of transferring data between these storage systems also leads to important data is not stored in high-efficiency systems.

Enterprises can avoid this problem by virtualizing data with specialized software, thereby creating a global namespace (global namespace) for all data storage systems. Thus, applications can access any data no matter where it is stored.

With virtualization, the control path (control path) and data path (data path) are separated. Thus, you can easily control all data fragmented in many different systems.

Also, data can be moved quickly between storage systems without affecting the operation of the software that is using that data. Therefore, important data can be prioritized for transfer to high-efficiency storage servers or solutions, while low-priority data is transferred to lower cost and efficiency server and storage solution. Moreover, you will also avoid the data migration process which is very laborious.

  1. Storing data on the cloud or object storage. 

Adding cloud or object data storage solutions is one of the ways businesses can reduce IT costs. The biggest challenge is how to integrate cloud storage solution with other enterprise data storage solutions.

One point to pay attention when moving enterprise data to the cloud is to ensure you can easily move data back to on-premises storage systems at the file level as needed.

In general, data input to the cloud will be more reasonable than the opposite process. Data, when inputted to the cloud, is often anti-duplicated. If you want to bring data back to on-premises storage systems, you need to reverse this process and recreate data chunk (also known as rehydration). Without careful consideration, the cost advantage of cloud technology may disappear.

     4. Automating data management  

The final step to complete the business data management process is automation. Some storage solution providers may offer this capability individually or for their entire ecosystem of solutions. If you want to automate all data management activities on multiple storage systems, you can find metadata engine (metadata engine).

With the explosion of machine learning technology, the application of artificial intelligence to information management is no longer an imagination. Shortly, the software itself can identify model, such as which data is used at the end of each month, or each quarter, and can move by itself, if it was set, into the high-efficiency storage system.