Most people perceive data science as a mystical practice. But it isn’t. It would be totally disastrous if an organization fails to make use of data science since it would imply that the organization would not only make uninformed decisions but also make inaccurate projections. The most common strategies that data science employs are top-down modeling and machine learning.
To better understand what data science is, let’s assume you want to decide how many coffee bags you need to order if there is a drought in the near future in the coffee producing countries. To answer this question, you need a great deal of complex math and access to large sets of data.
There are individuals and organizations that possess this kind of valuable information and this is how data science manifests itself as an interdisciplinary field that gathers information from all data places and then avails this information to enterprises for use for decision-making.
Data sandbox, on the other hand, refers to the scalable and creation platform which used to explore an enterprise’s rich information sets. How practical is a data sandbox? Let us take a real life example of how the idea of data sandbox is supportive in the context of big data. Picture Amazon which has hundreds of millions of users, over 400 million items and an average of $2000 transactions per second. The amount of data generated by Amazon is so large that it requires thousands of analysts to work on it. For successful data analysis, geeks must use a data sandbox; simply put a platform to support the analytics.