1. Slow query performance: One common issue with OLAP systems is slow query performance, especially when dealing with large datasets. This can be caused by inefficient data modeling, lack of proper indexing, or insufficient hardware resources.

To fix this issue, optimize the data model by properly structuring the data for efficient querying. Implement indexing on key columns to speed up query execution. Additionally, ensure that the hardware resources, such as CPU, memory, and storage, are adequate to handle the workload.

2. Inconsistent data quality: In OLAP systems, data quality issues such as duplicates, missing values, or incorrect data can lead to inaccurate analysis and decision-making. This can be a result of data integration from multiple sources or data transformation processes.

To address this issue, implement data cleansing and validation processes to ensure data accuracy and consistency. Use data profiling tools to identify and resolve data quality issues before loading the data into the OLAP system. Regularly monitor and maintain data quality standards to prevent future issues.

3. Limited scalability: As the volume of data grows, OLAP systems may face scalability challenges in handling increasing data loads and user concurrency. This can lead to performance degradation and system downtime.

To overcome scalability limitations, consider implementing distributed processing or partitioning strategies to distribute data across multiple servers or nodes. Use parallel processing techniques to improve query performance and handle larger datasets efficiently. Regularly monitor system performance and optimize resource utilization to ensure scalability.

4. Complex data modeling: Designing and implementing a multidimensional data model for OLAP systems can be complex and challenging, especially for users with limited experience in data modeling.

To simplify data modeling in OLAP systems, consider using tools or frameworks that provide intuitive interfaces for designing multidimensional data models. Utilize best practices and guidelines for data modeling to ensure the efficiency and effectiveness of the OLAP system. Provide training and support for users to understand and navigate the data model effectively.

5. Lack of user training and adoption: Users may struggle to leverage the full capabilities of OLAP systems due to a lack of training or understanding of the technology. This can result in underutilization of the system and limited benefits for decision-making.

To address this issue, provide comprehensive training and resources for users to learn how to query, analyze, and visualize data in the OLAP system effectively. Offer workshops, tutorials, and documentation to support user adoption and encourage the use of advanced features for data analysis. Regularly gather feedback from users to identify areas for improvement and enhance user experience.

One historical fun fact about OLAP is that it was first introduced in the 1970s by researchers at IBM, who developed a prototype system called "Cubed Data" for analyzing and visualizing large datasets. This early version of OLAP laid the foundation for modern business intelligence and data analytics tools that are widely used today.