Three major issues to be avoided in deploying a data Warehouse system

Enterprises in the decision to purchase Data Warehouse integration equipment, the products to be implemented after the selection, the project management phase began, the goal is to successfully deliver to meet the specific business and technical requirements of the Data Warehouse system. But there are plenty of challenges on the way to achieving that goal.

Remember that it projects typically have a high failure rate, and companies that deploy data warehouse equipment should ensure that a more detailed project plan is in place. Other priorities are also sensitive to other problems encountered in device deployment, so you can learn a lot from them. By flexibly planning and identifying potential obstacles, you will avoid encountering more difficulties.

Looking at the problems that may be encountered in implementing a data warehousing equipment project, both technical and business challenges need to be considered in order to ensure the success of the project.

Business challenges. Sometimes when deploying a device, the enterprise may overlook the business factor due to the detailed technical requirements of the Data Warehouse project. In addition to identifying hasty business problems and requirements for data Warehouse devices, the performer should look at the data-related business rules in the Data warehouse, trying to calculate the project's return on investment and the total cost of ownership of the new schema/expansion architecture. ROI and TCO charts help to determine the project and evaluate its total value.

Unfortunately, some companies cannot demonstrate the potential value of data warehouses in addition to data cleansing and the benefits they offer. The problem is that even if the initial device deployment is approved, and if there is no direct visible value to the business, future expansions are likely to be not prioritized, such as cost savings or increased profitability.

Technical challenges. During the implementation of the Data warehouse, there are some common technical challenges that are related to the project. The Data Warehouse team should understand the other IT projects that are being implemented and how they affect the data source-for example, whether any changes will result in source data or any related business rules. These factors affect project time and the ability to deploy the device in terms of proper data quality and integrity assurance.

Identify integration requirements-including data and system aspects related to existing IT architectures within your organization. Building internal hardware and software standards can reduce integration challenges and the process will guide the work required. Of course, the database is always a challenge to the project team in terms of basic technology-data warehouse equipment projects are no exception.

Data preparation. When implementing the application, the initial data loading and table joins indicate the success and failure of the project, which is the process of developing the data quality rules. Do data validation if the control is reasonable, even the repeated data cleaning can not solve the quality problem. The phrase "garbage in, garbage out" is useful for data Warehouse equipment projects: Business intelligence and advanced analysis require high data quality for effective knowledge discovery. Some companies focus on ensuring data integrity or developing business processes to support this integrity, and their deployment is more likely to succeed.

Whether the potential problem is a business or technical issue, it is important to assess how each problem affects the implementation process and how the Data Warehouse system environment results are affected. Develop a detailed deployment plan and checklist, and then follow this list to make sure that all the tasks that need to be done are completed on schedule, which helps an organization avoid problems and errors in the Data Warehouse application project. In addition, there should be sufficient sustainability plans to enable the Data Warehouse team to handle the differences between any project plan and actual deployment.

Application vendors strongly advocate that data warehouse software and hardware bindings are easier to deploy than traditional data warehouses. Data Warehouse projects as with any IT project, there are a number of issues and obstacles to pay attention to--but if you do a detailed plan, and you can do a lot of careful work to avoid many challenges.