I need two comments, each part is around 100 words.Part1:What new Analytics tool needs to be invented and why?With the growing need for Analytics in our world today, there is always a need for a new tool. However, each tool that is invented must be both effective and efficient at solving a particular need. For example, one tool that would be extremely useful in my opinion, is a data preparation software. With 60% of a data scientist’s time spent cleaning the data (Forbes 2016), this tool would have the most impact on the analytics pipeline. By investing the time, effort, and resources to build a more user-friendly, easy-to-use “data cleaner”, we would be able to generate gains in productivity, as well as overall effectiveness and efficiency of the analytics team. In addition, the data scientists would feel happier as a result, with 76% of data scientists viewing data preparation as the least enjoyable part of their work (Forbes 2016).What personal needs do you have that would benefit from such a new tool?If built in the right manner, there are three (3) areas that the data preparation software would be beneficial in:Time and cost savings: By completely outsourcing the data preparation phase of the analytics pipeline, a large amount of time and unnecessary costs would be saved in doing so.Greater flexibility: With a system that can easily recognize different file formats, the data preparation tool would be able to work on different data sets in tandem.Increased scalability: With data now being available in large quantities, the system will be designed in order to scale it to much larger data sets.Leveraging the already-built technology and considering the various limitations of current data preparation tools (i.e. Hadoop), the new tool will be improved with feature enhancements that take in to account the above three (3) needs. Finally, the User Interface Design for this new tool will also need to be considered, in order to ensure it is user-friendly, which will in turn increase its market share and bring more customers on board.Part 2:I think an analytics tool that would be useful is a data integration tool for merging big data repositories. An example where such a tool would prove helpful is specifically in public health care. Currently, there exists many levels of health records covering diverse aspects of a person's wellbeing which are collected from either surveys, medical practitioners, hospitals, or other sources. This information remains mostly unexploited due to the fact that the data has not been combined into one large integrated repository. With one central data lake, new insights, patterns, predictions, or even disease prevention can be obtained while looking at the many facets of an individual from multiple angles, from multiple data sources. A new sophisticated data integration tool would make this possible, allowing for a low-cost and time-saving process of creating highly detailed, multi-faceted population health profiles. Such a system would also enable physicians to better track the social needs most pressing for their patients (Bu et al., 2020).Personal needs that would be benefitted from such a tool are my own health and well-being. If there was a data integration tool that could create a data lake, merging all my past and current health records into one fully integrated profile, perhaps I could catch an early detection of a disease or enable my physician to take a more informed approach towards possible treatments.

Small mistakes are the stepping stones to large failures. How might this saying apply to this lesson, and do you agree? In your responses, provide an example of a real-life seemingly small mistake with large consequences. By real-life, I mean a situation that actually happened, not a theoretical one.