Top Data Warehouse Challenges & How To Overcome Them

Data teams face a variety of challenges with a traditional data warehouse. Learn what these are and how to overcome them with modern data warehousing.
Last updated
May 2, 2024

Data warehouses are an essential solution for modern companies to store and access data. However, that doesn’t mean that they don’t come with their own set of challenges. Implementing a data warehouse, integrating data from multiple sources, managing data operations and analyzing large amounts of data can all prove to be daunting challenges.

In this blog post, we’ll discuss some of the most common data warehouse challenges and provide strategies to overcome them. Read on to learn more about realizing the full potential of your data warehouse.

What Is a Data Warehouse?

First, let’s define what a data warehouse is. In short, it is a system that can collect and store data from multiple sources. It can act as a centralized repository, so organizations can easily store large amounts of data in one place. From a data warehouse, users can access, analyze and use the data in an organization’s daily operations. 

Data warehouses are essential for data-driven organizations. They can help businesses make data-driven decisions, gain insights into their customers, optimize business processes, reduce costs and much more. 

A data warehouse is typically made up of a database, an extract-transform-load (ETL) layer, access tools and metadata. These are the most common components of data warehouse architecture. A database forms the foundation, the ETL layer extracts the data, the access tools help users search and query data and metadata helps to provide context for data and define it.

An organization should prioritize leveraging these data warehouse components so they can implement an effective data management strategy. With the right data warehousing approach, businesses can improve data accuracy, consistency and integrity while also gaining better data insights.

Common Issues Data Teams Face With Traditional Data Warehousing

Now we have a better understanding of a data warehouse and its components. If your company is using traditional data warehousing methods, you may find that you’re running into several issues. It’s important to be aware of these issues so you can prevent them. With a modern data warehousing solution like Secoda, you can make things much easier for your data team and prevent common issues like these:

Data Quality

It can be difficult to maintain data quality in a traditional data warehouse structure. Manual errors and missed updates can lead to corrupt or obsolete data. Inevitably, this leads to issues with data-driven decision-making and causes inaccurate data analysis for users pulling data from your warehouse.

Modern data warehousing solutions like Secoda can automate the data quality process. This prevents data silos, outliers, manual errors and other data inefficiencies from occurring. With an automated data warehouse solution, you can make sure you’re maintaining high-quality data that provides the maximum value for your organization.

Manual Data Processing

Manual data processing is another obstacle data teams face when dealing with traditional data warehouses. Manual data processing can be time-consuming and lead to human error. Instead of spending an inordinate amount of time on manual tasks, these processes can be automated with modern data warehouse solutions.

Converting data with manual processing can be a multistep process, posing various challenges and increasing the risk of error. It’s much simpler to circumvent these manual issues and automate the processing step to ensure data quality and accuracy consistently.

Not only does automating data processing save your data team time, but it saves your organization time overall. Automation makes data processing much more efficient while improving accuracy in the process.


Testing needs to be a regular part of any data warehousing strategy. Testing is crucial to ensure that data within your warehouse is accurate and updated. In a traditional data warehouse structure, it can be difficult to continually test data and make sure it’s reliable.

However, there are multiple solutions for improving testing and ensuring your data is useful and accurate. Implementing a modern solution to automate testing tasks can make it easier to conduct tests and forego the complexity of traditional data warehouse architecture. Modern solutions allow you to consolidate your data and centralize it as a single source of truth, reducing the complexity of testing tasks and the time it takes to do them. By creating an integrated testing environment, it’s much simpler for data teams to improve testing and make the process more effective and efficient.

While fully automating the testing process is difficult, implementing automation techniques and tools for the more tedious tasks can give your data team the help they need to make the testing process as efficient and accurate as possible.

Data Accuracy

Data accuracy is another essential component of data warehousing. If you want to make sure your business intelligence and data insights are useful and reliable, the data being analyzed needs to be accurate. However, traditional data warehouses can be prone to inaccurate data due to manual processing and other errors.

There are several strategies for circumnavigating this challenge and improving the accuracy of your data warehouse. The first step is ensuring the process for collecting and storing your data is accurate and that incoming data is formatted correctly and entering the warehouse in an accurate state. One way to do this is to make sure you have a centralized data repository acting as a single source of truth for your users.

Data accuracy should also be regularly reviewed through testing. Fortunately, many of the tasks to ensure data accuracy can be automated with the right data warehousing solution. Automation technology can greatly reduce the chance of human error. Not only can it help reduce inaccuracies from entering the data warehouse in the first place, but it can flag inaccuracies that do get through so you can optimize your data accuracy processes at the source.


Preferably, all data warehouses would operate at peak performance. Unfortunately, this is rarely the case, especially with traditional data warehouse architecture. When your data warehouse has performance issues, the problems can cascade throughout your organization. Poor performance can cause slow query speeds and reduce the ability of your users to make efficient, data-driven decisions. Conversely, optimized performance allows your users to quickly get the answers they need.

Once again, automation and modern data warehousing solutions can provide your data team with the tools they need to overcome performance challenges. Automating processes like data ingestion and indexing can greatly speed up performance and optimize your processes. Data queries can be processed much faster, and your users will efficiently get accurate data.

Non-technical Users

Traditional data warehouse solutions can sometimes be complex. In an ideal world, everyone would be able to easily understand data analysis, query data from any source and know how to use the data provided to them. Of course, this is rarely the case. Non-technical users will often need to use and interact with company data. This can be inefficient with a traditional data warehouse. Due to the complexity, non-technical users will typically need to submit a request for data to the data team, wait for the data team to fulfill their request and finally utilize the data once it’s returned to them. For small teams, this process might be tenable, but for larger teams, it can be incredibly time-consuming and inefficient.

Data teams can quickly become inundated with requests, leading to bottlenecks and frustrations. In short, organizations need to overcome the user acceptance challenge to reduce these bottlenecks and inefficiencies.

With the right modern data warehouse solutions, this challenge can be easily overcome. Data catalog solutions like Secoda can allow any user to easily search and query the data they need without the data team having to get involved. Users can simply get the data they need, and the data team can focus on other tasks. With solutions that are easy to use and accessible to even non-technical users, you can maintain user acceptance and ensure it doesn’t slow down your operations.

Try Secoda for Free

With a data catalog solution like Secoda, your data team can overcome many of the challenges that a traditional data warehouse would usually pose. Secoda allows for intuitive data discovery across your organization, with tools for searching, accessing and understanding data. You can also consolidate all of your data knowledge in one secure location, creating a centralized depository that helps eliminate data silos and inconsistencies. Essentially, Secoda makes searching for organizational data as easy as doing a Google search.

If you want to make sure your data storage and analysis solutions are as efficient as possible, Secoda is your solution. Your data team will run into far fewer challenges, and each member of your organization will understand how to use your company data to its full potential. Ready to learn more and see how Secoda can help your organization? Try our platform for free today!

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