Will data analytics go the way of the calculator? Much like someone can solve a math problem without understanding the calculations involved, will we be solving business problems without understanding the analytics behind those decisions? In your opinion, how much of a limiting factor is trust and risk on automated decisions becoming the norm, without explain ability?

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In mathematics concepts and formulars are used to make certain conclusions with explicit methods leading to the answer and in the same way one has to interpret the data in order to understand its meaning. In normal learning people understand how they get to an answer except of course maybe in rot learning. The limiting factor will be realised when human beings stop applying their minds and leaving computers to provide answers that the human being does not understand. It is crucial that data scientist are help those that have difficulty in interpreting data sets.

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In my opinion, data analytics will not likely to go the way of the calculator, because it requires more than just applying mathematical formulas to data. Data analytics involves complex skills like interpreting, communicating, and acting on the insights derived from data, which are skills that cannot be easily automated or replaced by machines. Data analysts also need to have domain knowledge, creativity, and critical thinking to solve complex business problems and generate value from data.

However, according to my research, data analytics tools are becoming more advanced, accessible, and user-friendly, which could enable more people to perform data analysis without understanding the underlying algorithms or methods. Some tools, for instance, can produce graphs, tables, and other data visualizations automatically from input data or offer natural language explanations of the outcomes of machine learning models. These tools enable people to quickly analyze data and draw conclusions without a lot of technical knowledge or training.

I believe that trust and risk associated with automated decision-making, such as AI and Machine Learning, are a constraining issue, but they may be improved by utilizing different approaches and strategies that are suited to the demands of the user.

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I don't think so, as analytics can drive more questions, further analysis and potentially oncover some business opportunities.

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There is a broad spectrum of data analytics, ranging from simple descriptive or basic reporting (e.g., metrics) to complex forecasting, theoretical development (e.g., psychological test development), and cause and effect modeling. The more complex the problem, the more likely that there will be underlying assumptions that need to be identified and considered as part of the analysis process, which will likely require human intervention. Additionally, causal modeling is based on an underlying theoretical model (the researcher develops a theoretical model or conceptual framework) that is imposed on a dataset and validated through advanced statistical models (e.g., confirmatory factor analysis, structural equation modeling). I doubt all these components can be sufficiently automated with computers, but I suppose anything is possible at this point.

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I think over the last few years we've all, if we were paying attention, have learned to put much less trust and faith in the &#34;experts&#34;. The opportunities with analytics will spread across a spectrum. Smaller industries and companies will find opportunity with &#34;off the shelf&#34; or more generic cloud solutions for analytics that will be black-box. Large industries and companies, with bigger budgets may find additional opportunity for differentiation with more custom solutions and getting more into the guts of the analytics to find things or go in directions that others haven't.

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