What are the limitations and challenges of Big Data analytics?
Big Data analytics offers immense potential for organizations to gain valuable insights and make data-driven decisions. However, it is not without its limitations and challenges. Here are some of the key challenges faced in Big Data analytics: Data Quality and Accuracy: One of the major challenges is ensuring the quality and accuracy of the data. Big Data is often sourced from diverse and complex data sets, which can contain errors, inconsistencies, and duplicate records. Pre-processing and cleaning the data become critical to ensure the accuracy of the analysis and prevent biased outcomes. Scalability: Big Data analytics involves processing and analyzing massive volumes of data. Ensuring scalability is a challenge, as traditional computing infrastructures may not be equipped to handle such large data sets. Organizations need to invest in scalable and distributed systems, such as Hadoop and cloud-based platforms, to handle the volume, velocity, and variety of Big Data. Security and Privacy: Big Data analytics requires handling sensitive and confidential information. Ensuring data security and privacy