Owing to their architecture, data warehouses seem to be the best solution to enterprises that need to store data for future references. There are countless benefits to implementing data warehouses within your corporation, especially for historical reporting.
Some other applications also find using this data model to be very effective with its capabilities to archive insights over a longer period. Be that as it may, this model is not perfect and has some disadvantages but before you throw it in the sewer, find out more about those drawbacks. Also, explore if there are any solutions to the most common disadvantages of data warehouses.
Warehoused data has the major inconvenience and disadvantage of inflexibility when it comes to storing and retrieving insights. When data has been stored in warehouse architecture, the file type gets set into the format used when collecting it. Subsequently, using it in other applications can be a bit challenging because of format limitations.
For example, dynamic data processing architectures such as using an operational data store can be used in a variety of applications. Also, you can’t have real-time analytics set up with a data warehouse because its main focus is archiving the information for future reference. Therefore, storing data and using it can be a bit of a hassle when it has been warehoused and it limits real-time BI tools that might need to gain access to actionable insights.
Using a data warehouse for storing organization reports can also be very challenging for larger enterprises with numerous divisions within the company structure. Organization reports need to be uploaded by tech support personnel which creates a bottleneck in the flow of data. At the same time, some reports that need to be amended and altered with the latest information require to be resubmitted in their entirety.
As a result, some organization reports could remain unamended for a long period, feeding inaccurate insights to applications the warehouse powers. A data warehouse model that solely services applications as a relational database might not be the best solution for data analytics and reporting tools. They need to be reinforced with other relevant data models that support an easier collection of organization reports.
One of the biggest disadvantages of data warehousing is the cost element to it, especially if it will be managed in-house. Managing the hardware needed to facilitate a data warehouse requires a large budget for a high-quality seamless experience. Also, setting it up in the first place might take a while to pay off because of the high upfront costs.
In the long haul, the revenue made from such systems might not make sense when compared with customer acquisition figures. Thus, the Cost/Benefit ratio might not be the best especially considering other business intelligence tools that a company needs to invest in. Sometimes the upfront costs and maintenance expenses could lead the organization to a loss which is another major disadvantage of using a data warehouse managed in-house.
When using or managing a data warehouse, there are more legal responsibilities than other simpler models. The reason why data warehouses have more legal responsibility is the fact that they store information for longer. Some of the information that is stored could be sensitive consumer data which comes with a lot of responsibility.
Keeping it for a longer period increases the risk of losing or landing it in the wrong hands. Additionally, the systems that are used could be vulnerable to an internal breach and leakage of data. Whether this data is stored on a cloud-based management system provided as a SaaS solution or is kept on-site, legal obligations still cause an inconvenience. You need to ensure that your data retention policy aligns with regulatory requirements and that abiding by these policies is essential for continued compliance.
One might assume that data warehouses are built for large volumes of data stored at mass for easier availability and thorough repository. On the contrary, data warehouses fail to support large volumes of information streaming in such as big data lakes. Extremely large volumes of data tend to slow this system down and make it unreliable to the applications it services.
Also, big data requires dynamic data that can be exported in different formats and warehouse structures do not support this. This limitation also limits the applications data warehouses can be implemented in since it does not give the full experience of big data. In this case, as well, using data warehousing solely is not the best solution for meeting the objectives of the company or development project.
As mentioned above, implementing data warehousing technology solely may not be the best solution depending on the application and objective of using it. Therefore, most of the disadvantages outlined above can be fixed using data warehousing as part of tiered data access layers. You can then implement other data models with useful architectures such as ODS to solve the particular problem you face with data warehousing.
In addition, using data warehouses in parallel with other data architectures provides solutions to the specific problems you’re facing. Some developers have integrated data warehouses with DIH architecture systems to have useful aggregated insights for user-end applications. There are some workarounds that you can try to branch out to other data processing models that provide holistic solutions to boost business operations by giving you better control over the data processing.
Data warehouses have many benefits in the analytics field by collecting and storing insights for a longer period. However, this solution is not perfect and thus has some disadvantages for certain users with special requirements. Getting over some of these disadvantages is possible by having a tiered data access layer system integrated with other data processing and storing methods such as using ODS, DIH, etc.
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