Integrating human sense with Data Analytics

Data analytics has become increasingly important in the contemporary society as a result of the recognition of the fundamental nature of the same in decision-making pertaining to a business entity.Data analytics has become increasingly important in the contemporary society as a result of the recognition of the fundamental nature of the same in decision-making pertaining to a business entity. Indeed, it is well acknowledged that the capacity of an organization to respond to emerging market conditions is predicated on the accuracy and relevance of the information at its disposal at any given time. In this case, organizations have increasingly used data analytics to enhance their capacity to easily and speedily make sense of data collected from the market and, essentially, support their market operations in the long-term and the short-term. 

At its most basic, the concept of data analytics underlines the science of raw data analysis to allow an organization to make conclusions regarding that information. A large proportion of processes and techniques pertaining to data analytics have become automated into algorithms and mechanical processes that efficiently process the raw data to allow for human consumption. The AI algorithms have been designed to make decisions using real-time data. They differ from passive machines that only have the capacity to make predetermined and mechanical responses. In this case, they utilize remote inputs, sensors and digital data to blend information from varying sources, with the material being analyzed instantly and the insights obtained therefrom acted on in the shortest time possible. 

Nevertheless, it has become increasingly important that organizations not only have data and undertake data analytics but also ensure that the data analytics does not outpace the corresponding social developments. There are varying reasons for the need to integrate human sense in data analytics. As much as machines have shown enhanced efficiency in sensory tasks such as object detection, language translation and image recognition, machines are incapable of acquiring some skills including critical thinking skills, imagination and creativity. Indeed, scholars have acknowledged that even in area where machines beat humans, their behavior is more in line with diligent learners rather than smart learners given the significant amount of data that they require, as well as the energy consumption. Even more noteworthy is the fact that pure-data drive models has the potential to result in unintended behaviors including gradient vanishing or even categorization of wrong labels in high confidence. The integration of human sense in data analytics would be fundamental in significantly enhancing the efficiency of the systems through reduction of the data requirements, enhancement of robustness and reliability of machine learning, as well as the creation of explainable data analytics systems. Even more fundamental is the potential for feed personalization. The incorporation or integration of human sense into data analytics enhances the potential for customization of the different products that are “fed” to the customers. The personalization of the product feed for users assists in the improvement of their experience and increases the potential for the customers to make purchases. It is noteworthy that the product listings that users see on varying parts of a website or app would be chosen based on the data analytics and machine learning models that would make predictions on the purchase intent of the users via signals like time spent on a particular product, number of times they have searched for the product, as well as wish-listed products, number of clicks and the number of likes for similar products. In this case, the integration of data analytics and human sense would allow for more accuracy in making data predictions and interpretation of the data thus obtained. 

There are varying elements that may be required for an organization to integrate human science with data analytics. Key among them is the personnel capability, which underlines the managerial and technical skills pertaining to employees. The concept of personnel capability encompasses functional expertise, technical skills, social expertise and collaboration. These are all the skills that employees have the potential to develop via further training, education, as well as capabilities and seminars, which the organization may acquire via hiring skilled individuals. Employees have to be cognizant of the limitations, weaknesses and strengths pertaining to applied statistical techniques and, essentially, of the generated modeling results to safeguard appropriate deployment of the findings. This may be complemented by their having the capacity  to work using python and Java Script in setting up the analytical models. Moreover, entities must increasingly adopt project-based approach to applied data analytics through collaboration with other departments in an organization. 


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