With the application of advanced technologies such as artificial intelligence and machine learning, the analytics market is growing at a rapid pace across all verticals. Data analytics platforms continue to become smarter over time as deep learning tools get more accessible and cost-competitive. Also, following the surge in data sources and big data, data science platforms are growing more intelligent with greater capabilities to assess the data and glean actionable insights. With the proliferation of IoT devices, the generation and accumulation of data has increased by folds which in turn are playing a major role in enabling machine learning tools to learn faster from the aggregated data and discern hidden insights on future trends and practices in significant industries.
While machine learning today contributes considerably to data analytics, there are other factors to consider when it comes to data science. Cohesiveness is imperative between data science platform and the entire analytics pipeline, beginning from the primary access, to data collection, to an eventual utilization of the data. With regard to data science platform’s accuracy and reliability, it is critical to ensure proper interoperability between platform tools and IT infrastructure tools. Organizations must ascertain that the data science platform they leverage complements their existing IT infrastructure and hence evaluating the current IT infrastructure must be the first step in deploying the right data analytics platform. Additionally, organizations must monitor the data that they aggregate to ensure that they provide accurate information to machine learning tools for deriving meaningful insights. As data science and machine learning demand precise data to construct better business models, information management forms a major step to determine that the models are based on correct inputs.
