The growing volume of corporate data, fast technical advances, and falling average selling costs of smart devices all contribute to the production of vast amounts of organised and unstructured data. More than 80% of the data collected by corporations is not stored in a traditional relational database. It is instead hidden in unstructured papers, social media posts, machine logs, photographs, and other sources. The tremendous expansion of data gives chances for companies to obtain new insights, which has boosted the demand for innovative tactics and processes. This, in turn, plays an important role in propelling the growth of data science platforms.
Companies should research the business problems for which they intend to use the data science platform. Choosing the mechanical technique of discovering datasets and executing data analysis before determining what business issue to solve is less successful. This is especially problematic when firms use the Data Science Platform for effective decision-making. Even if a company has a clear objective in mind, if the expectations from the data science platform deployment are not linked with the end-goals, the efforts are fruitless.
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