With the promotion of various departments and industry information construction, various operational support systems for operational and management, management information systems are gradually completing and improving. After accumulating rich information technology and data, various data analysis systems for decision-making layers have received more and more attention. Software projects have such a risk, which is determined by the creative essence of software projects. The construction of data analysis projects not only requires the common risks of general software projects, but also with more than two reasons, the information system for operational layers, management information systems: 1. Data analysis system It is usually a decision-making layer, and has a lot of uncertainties in terms of personal habits, work style, and knowledge background, and a lot of uncertainties in terms of system range and system performance. 2. Data analysis system, especially various data mining systems, need to rely on mathematical models. The application of the data model requires systematic analysis, and designers should have good mathematical background knowledge, and they are very understandable. These often have a data warehouse based on the computer software and hardware technology. In this article, the author will focus on the risk identification, risk analysis and risk response of data analysis projects based on the characteristics of the data analysis project. Risk identification Due to the fact that the demand for data analysis is often blurred, data analysis technology is very complicated, so data analysis projects have large risks, and domestic international decision analysis systems, customer relationship management systems have proved This is this. This requires organizational layer and project managers to pay more attention to risk management when implementing data analysis projects. In the start of the project, it is to pay attention to project risk analysis and do a good job in the risk management plan. Analysis of risk values based on the characteristics, range estimates, technical conditions, historical data. During the implementation of the project, constantly monitor the changes in risks, strive to avoid risks. During specific risk identification, we must focus on the following risks: 1. Risk of uncertainty of demand; 2. Risk of communication barriers to decision-making; 3. Analyze the risk of technical routes; 4. Other risks. Risk analysis The three major risks of the aforementioned data analysis projects are very large, and if the risk analysis method is based on qualitative risk analysis, the impact is "high" level. If the influence is calculated according to the first item, the score is 0.8 or more. The risk of uncertainty in demand will also lead to project progress, cost, quality, resource, contract, etc., so that the project is out of control. In actual work, we have encountered a number of projects to end more, and the customer leaders still have a customer leader to present "the need to pull it in the customer analysis". "In the business indicator analysis, it is necessary to strengthen the spirit of X-minister's speech". Demand. Decision layer communication barrier risk is easy to make the final decision-making leader to preserve the project bidding stage, ideal and abstraction, the project team cannot really understand the needs of leaders, even at a very suitable, a large number of work "Week" waste, make time and cost into the cost increase. Technology route risk can directly lead to failure. The project's goal, the scope exceeds the project group selected. The selected product is known as an analytical model that is actually unsuccessful, and the project team itself is not familiar with the product, which will undoubtedly make the project in devastating risk. The national credit analyzer that is implemented in a national-level bank adopts SAS and Cognos is almost unused is a typical case. The purpose of risk avoidance risk analysis activity is to establish a strategy for processing risks. An effective strategy is best to avoid risks. The best way of risk avoidance is to control the risk control in the project startup stage, and reduce the item loss to the lowest level. According to the project experience of the long-term responsible data analysis, the above three major data analysis project risks can be avoided or decreased: 1. Project managers have unloaded communication channels with the highest decision-making layer; 2. Project development model uses iterative model; 3. Careless use is still in the analysis technology and model of the research phase; 4. High level and reasonable project organizational structure; 5. More reference views.
