What is a disadvantage of using granular data sources in implementations?

Using granular data sources in implementations can indeed complicate data processing. Granular data refers to highly detailed data sets that provide specific information at a very fine level of detail. While this level of detail can be beneficial for in-depth analysis, it also means that the volume of data can be significantly larger and more complex to manage. This complexity can result in longer processing times, increased computing resource requirements, and the potential for difficulties in integrating and analyzing the data effectively.

In contrast, other options present advantages or neutral aspects rather than disadvantages. For instance, while granular data can lead to better insights by allowing for more detailed analysis, it does not negate the complications involved in handling such data. Similarly, stating that granular sources require less storage space does not accurately reflect their property; in fact, they typically demand more. Lastly, enhanced granularity can actually increase reporting accuracy because it provides more information for decision-making, so the notion that it decreases reporting accuracy is inaccurate.