Data Warehousing (DW) has numerous advantages and is an irreplaceable requirement for Business Intelligence to work. Data warehousing projects are usually very expensive and takes a tremendous amount of time and resources to complete effectively. Even when these projects are so expensive, statistics show that only about 50% of DW projects are completed successfully and even lesser provide the results the users expect.
Over the years, XCEL Corp has been asked to resurrect several failed and dead Data Warehousing projects. From our experience we have understood that no two project failure causes are similar. We have come to understand DW projects fail because of different reasons. We have also come to understand that DW projects would always run into problems. But many of these reasons end up being small mistakes that could have been easily avoided.
We at XCEL Corp with our extensive experience in implementing new Data Warehousing projects and in correcting failed projects, we have learnt some of the most common mistakes that cause Data Warehousing projects to fail. Here are some of the most common mistakes that could be easily avoided. These are mistakes which could even cause your project to be scrapped all together as well.
1. DW projects aren’t similar to other IT projects:
One of the most common mistakes many organizations commit while implementing data warehousing is that the organizations assume DW projects are just like other IT projects. Many of the technologies used for Big Data and Data Warehousing are very different from what regular IT developers use. Many IT engineers in normal IT organizations develop their skills in Project and Website development and these skills are of no use in DW projects. Technologies used in DW projects including Hadoop, EMR, and Storm are quite complicated and would take time for your regular developers to learn. You can of course ask them to learn but is it really worth it? Do you want to take them off their core competencies and engage them in a onetime project?
2. Improper Data Transformation:
Integrating data from different sources is always complicated. The process of compiling data from different sources is referred to as ETL (Extraction, Transformation, and Loading) process. Though it may seem simple and easy, it is much more than copying your data from different sources and pasting them in your warehouse. There are several complexities with the ETL process as data from different sources don’t get together very easily. Some of the complications you might face are:
- Updating records without compromising on performance
- Search term and value normalization
- Establishing key relationships across the different data sources
- Time zone consistency
When the transformation is not done with care, it would make your DW project way more complicated for everyone. You would not be to develop and inferable business intelligence leading to inconsistencies prompting decision makers to scrap the implementation.
3. Doing all at once:
Many believe in creating the Data warehouse completely and then test it and deploy it. This again might work for normal IT projects but DW projects are much more complicated with many different aspects to consider. This is because you have to integrate dissimilar sources of information, along with numerous metrics and measurements that need to be aligned for your Data Warehouse to make sense.
Instead of doing it all at once, Data Warehousing projects should be broken down into smaller and manageable projects which could be tested and deployed individually. This not just ensures that your project is successful at every level, it also means that every sector of the organization would be getting their data integrated effectively.
4. Lack of communication between end-users and stakeholders
The data requirements of each organization vary within their different groups. If you divide the groups into technical staff, business analysts, and stake holders, each have different requirement with DW implementation. While technical group understands in analytical and functional terms, the business analysts like to see behaviors and work flow, and the stakeholders would need outcomes and high level results. While working on a DW project, project managers should ensure there is hassle free communication between the groups and all the needs are attended to.
Communication is important in every step of the way from gathering requirements, setting expectations, deployment, and training. It is also important that the managers ensure that project moves forward that same pace even while involving the different groups.
4. Training and Transition:
Training and transition go together in DW projects. During the deployment stage of the project, there is a lot to do for everybody. Many technical staffs who are familiar with their usual practices should now adjust themselves to the data warehouse and learn how to use it effectively as well. In many cases a large percentage of your staff may have got used with their traditional workflow and would find it difficult to adapt. This is why transition and training is vital in making sure your project is successful. This also ensure acceptance within the organization and it means that your expensive DW project doesn’t end up neglected and unused.
One of the easiest ways to avoid such mistakes in your DW projects is to hire someone with the right experience and the best project management skills. Like already mentioned, at XCEL Corp, we have completed several successful DW and BI projects. Our services are transparently priced and we employ the latest technology available. We provide constant support and inexpensive maintenance along with regular updates.
Bhavani Suri ( Content Writer)
Our in-house content writer, who develops and creates content marketing strategies. She writes about the latest trends and advances related to IT in particular, and Technology in general.
