Sample Solution

Data mining involves the extraction of meaningful patterns from large datasets. As more and more information is being generated, businesses have sought to leverage this data for insights into consumer behaviour and identify trends in the market. However, there are certain issues that arise when dealing with big data such as data quality, privacy considerations and lack of resources to process it properly (Agarwal et al., 2020).

One example of an information system used for data mining is Hadoop which is an open source framework designed for processing big data (Shvachko et al., 2010). While Hadoop can efficiently store and analyze large amounts of data; it has limitations when dealing with too much data. This can be caused by a number of factors such as memory constraints or long wait times due to traversing through massive datasets (Ramirez & Barros 2017). Additionally, query optimization could become difficult if too many queries are posed against huge datasets (Dinu-Bubulac et al., 2018).

In order to limit these issues related to big data, organizations should ensure they only collect relevant pieces of information needed from their customers instead of collecting everything . They should also take steps towards optimizing their databases by indexing fields so that searches on them will execute faster(Henchal & Thakur 2021 ). Organizations should periodically audit their databases in order to detect any anomalies which could lead to errors while mining or executing queries. Moreover, deploying distributed systems such as Hadoop across multiple nodes can help manage storage capacity better than a single machine would(Kumar et al.,2020 ) .

In conclusion, while leveraging available technology such as the open source framework hadoop may allow businesses access into large datasets at low cost ; utilizing it improperly or having too much dataset presented poses challenges with regards to speed and accuracy when performing analysis tasks.