augmented analytics has become an increasingly important part of most data-driven institutions

1.As detailed in the article, augmented analytics has become an increasingly important part of most data-driven institutions. It entails the integration of machine learning and artificial intelligence with data analytics to achieve the obtaining of useful information that is essential in such data-driven institutions (Chen, 2019). Augmented analytics helps in the automation of repetitive tasks, such as the preparation of data for use, which usually involves sorting and labeling of data for easy access, and the initial analysis which often involves the determining of patterns in a dataset (Chen, 2019). Such tasks are repetitive, and in the usual office setting, they are done manually, hence are time-consuming and, in large volumes, increase the chances of occurrence of a mistake. Augmented analytics takes away such responsibilities, and leaves the analysts with the more important duties, seeking out unusual trends in the datasets and relating it to certain aspects of the business, so solutions  can be thought up to improve performance.

In the face of covid-19, the sourcing of data on numbers of infections and deaths has become essential. The raw data on its own, however, though painting a grim picture of a health crisis on the offing, doesn’t give the whole story of the pandemic. The data, once collected has to be prepared by labeling, for instance, by determining what states or cities they were from, the gender, the dead, recovered, and the still active cases, and patterns have to be drawn up to determine the varying impact of the disease on certain groups in the population. Augmented analytics can go a long way in achieving this. The sorting of the cases into their respective groups can be done with the top-most accuracy, and the impact on one gender, for instance, over the other,  and the chronic nature of the disease on a certain age group as opposed to another can be determined. All this with the highest of accuracy, compared to human beings, leaving scientists and health practitioners to focus more on other aspects such as the underlying reasons for such inconsistencies.

2.For the delivery of my story on the specific problem being dealt with to my team, I will make sure to include certain aspects relating to their fields of expertise. Take, for instance, the covid-19 pandemic, as detailed above. The problem presented to us is finding the possible areas of weakness in the population that may get way more ravaged by the disease, the circumstances that may possibly lead to this, and suitable solutions to avoid such a scenario. That is the story, in summary form, but it lacks any tangible data that may be referred to so as to determine such patterns. Therefore, in narrating the story to my team, I may decide to off-handedly offer some snip bits of data, for instance, raw data on the number of deaths in the states, which, in the long run, may prove to be irrelevant to the course, but will kick start their though process on the subject matter, and, to some extent, bring out a measure of dependence between the problem and the whole solving process. To achieve this, a certain level of empathy is required, to be able to recognize such a feeling of nonchalance towards the underlying subject matter in the first place. To convince them on the importance of the problem being analyzed however, one needs to be knowledgeable on its benefits, as it enables the team to come up with better solutions, as patterns are drawn up with emphasis on their relation to the subject matter, which results in more streamlined and effective solutions.

References

Chen, M. (2019, September 9). What Is Augmented Analytics? Retrieved June 3, 2020, from Oracle: https://blogs.oracle.com/analytics/what-is-augmented-analytics-v2