As organizations move towards becoming more data-driven, the use of data warehouses has become increasingly prevalent. While this transition requires companies to invest immense amounts of time and money, many projects continue to fail. Let’s take a look at the most common reasons why data warehouse projects fail and how you can avoid them.

There’s No Clear Big Picture

In most cases, these projects don’t fail due to technical challenges. While there might be some obstacles when it comes to loading and connecting data, the leading pitfalls of project failure are predominantly organizational. Stakeholders commonly feel that there is a lack of clarity surrounding the warehouses’ goals and primary objectives.Companies often see this most prevalently in the division between technical teams and the ultimate end user. You don’t want your architect or engineers to be on a different page than your analysts. Therefore, it’s important to establish the high-level goals behind why you are undertaking this project to all members of your team before putting processes into place. Before beginning, the team should have definitive answers to questions like:

• What are our data goals?
• What insights are we looking for to satisfy our business needs?
• What types of questions do we need the data to answer?

Developing a clear understanding of the big picture early on will help you avoid uncertainty around strategy, resource selection, and designing processes. Knowing the company’s “why” behind taking on the initiative will also allow those involved to recognize the purpose of their efforts.

The Heavy Load of Actually Loading the Data 

Despite the organizational obstacles, there are also many hurdles on the technical side of things. Before data can be loaded into the warehouse, it has to be prepped and properly cleaned. This poses an initial challenge as cleaning data is notoriously a time-consuming task. IT leaders are often frustrated by the wasted hours spent preparing data to be loaded.The primary main concern is the ability of organizations to easily move and integrate their data. Movement and ease of access to data are crucial in order to generate any kind of insights or business value. According to a recent study conducted by Vanson Bourne and SnapLogic, 88% of IT decision-makers experience problems when it comes to loading data into their data warehouse. The most common data loading inhibitors were found to be:

1. Legacy Systems - Migrating data from legacy technology can be time-consuming. However, the primary issue here is that these systems can be difficult to access, making any kind of data movement restrictive.
2. Unstructured and Semi-Structured Data - Complex data types are tough to manage in any situation. Inconsistencies surrounding structure and formatting drains time and technical resources, preventing effective loading.
3. Data Siloed in Different Infrastructures - Disconnection of data sources prevents integration across the organization. Many companies have hundreds of separate data sources as they continually grow across departments and with the addition of various projects. 
4. Resistance to Sharing Data Across Departments - Oftentimes departments act as their own separate entities and aren’t willing to share. The sales team may not want finance to have access to their customer data due to misaligned goals. 

All of these warehouse factors drain an organization’s time and resources, contributing to a lengthier and more costly project overall. Additionally, improperly loading data can cause a number of problems in itself such as errors and data duplication.

Low End User Acceptance

So you’ve successfully moved your data into the warehouse, now what? Another issue that commonly contributes to the failure of data warehouse projects is end user acceptance. As much as new technologies can be exciting, people are inevitably creatures of habit and might not always delve into acceptance. This is where education and training come into play. Onboarding users is vital to the success of any project. Establishing a data-driven culture is the first step to promoting user acceptance and engagement. End users should be encouraged to indulge in their data curiosities. Implementing a form of self-service analytics will increase the ease of use for non-technical users and help them quickly gain access to information. These transitional efforts will not only help with the success and use of your data warehouse but also drive better decision making throughout the organization in the long run.

Conclusion

Overall, there are a variety of reasons that contribute to the failure of data warehouse projects. Whether those pitfalls are organizational or on the technical side of things, there are proven ways to properly address them in order to maximize investment and foster successful insights. 

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