In today’s complicated data world, traditional data analysis has a few disadvantages that limit its efficacy. One of these limitations is the fact that it relies on structured data and therefore cannot take advantage of unstructured material such as text, images and social media interactions. This may lead to valuable insights being foregone. Another limitation is dealing with large volume of data which is not only difficult but also time consuming. Most importantly, historical approaches in data analysis sometimes fail to give the exact suggestions for future trends. Moreover, traditional techniques are ineffective in handling big data and hence hinder efficient study of enormous datasets. Also, conventional analysis puts too much emphasis on historical information while predicting future trend may be inaccurate. Finally, old-fashioned methods can be time-consuming and resource-intensive due to extensive manual work involving cleaning up, processions or analysis of this information among others. As an outcome this can slow down decision making processes while restricting adaptability to emerging developments at hand.
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