Five steps to create an efficient large data analysis project

Large data is not only a popular topic, but also a real demand in the enterprise. Many companies are starting to work on large data analysis projects, but before that, we need a good deployment plan to ensure that the end result can be business services. Choosing the right technology is the first part of the plan, and when the enterprise chooses database software, analytics tools, and related technical architectures, we can proceed to the next step and develop a truly successful large data platform.

Of course, we do not have to exaggerate the role of the project management process, the success of large data analysis projects from a number of aspects. In this article, we will introduce five tips, enterprise users for platform deployment can be used as a certain reference:

Just select the data you need. For its own nature, large data analysis projects will encounter massive datasets. But the mass data does not represent all the data of an enterprise, nor does all the information in the relevant data source need us to analyze. Businesses need to determine which data is strategically valuable and capable of providing analysis services. For example, what kind of information combination plays a vital role in customer maintenance? Or the stock market, what data are hidden in the trading model? In the planning phase, focusing on the business goals will help the enterprise pinpoint the analysis, and on top of that we can and should understand what data can meet these business goals. In some cases, there will be cases that contain all the data, but few are. We often only need a subset of large data to analyze.

Build efficient business roles and then process the corresponding complexity. Coping with complexity is one of the keys to the success of large data analysis projects. In order to be able to finally get the right analysis results, we need to involve all the relevant business data owners in the process to ensure that the necessary business roles are developed in advance. Once the business role is finalized, the technician can assess the complexity and the work that needs to be done. This points to the next stage of deployment.

The business role is transformed into the relevant analysis in a synergistic manner. Establishing a business role is the first step for a large data analysis application, and the next it or analyst needs to create the appropriate algorithm. But this part of the work should not be independent, the more accurate the initial query, then the need for less development work. Many projects require continuous and iterative development, and the reason for this is that there is a problem with the communication between the project's executive staff and the business unit. Therefore, in the process of project development, we need to double coordination and timely communication in order to ensure the smooth progress of the project.

Determine a maintenance plan. In addition to some of the development work before the project, we also need to continuously pay attention to the changes. Day-to-day query maintenance on the basis of changes in business requirements is important, but it is only part of the overall analysis project management. As data sets grow and business users become familiar with the process, their requirements for the system increase accordingly. The analysis team must be able to meet additional requirements in a timely manner. In addition, one of the elements that must be considered when evaluating software and hardware options is the ability to support iterative development processes in a changing business environment. The analytic system that can change according to the change of demand will reflect its value for a long time.

Keep in mind the needs of users, not some users, but all users. With the popularity of self-service bi tools, it is not surprising to put end users into consideration in large data analysis projects. Of course, it is important to be able to deal with different data types, but the operability and interactivity of the system are also issues that we need to consider. This requires us to take into account the feedback of different types of users, from executive level to operator, from analyst to statistician, to the need to be able to access large data analysis applications, regardless of the manner in which they are used. And their acceptance of the tool, to a large extent, determines the success or failure of the project. For example, an average employee or business manager doesn't have to run a large data analysis query on its own, and only needs to be able to access a visual report or dashboard. And analysts and IT departments may need some in-depth functionality.

There is no way to ensure that all large data analysis projects are successful, but knowing some of the best practices will certainly make your big data project planning clearer. The technical aspects of large data analysis are too detailed and complex to be explained overnight, so we do not mention technical details in this article. But both technology and business determine the success or failure of large data projects, focusing only on technology and ignoring business requirements will lead to project imbalances and vice versa.