Limitations of Predative analytics (6)

 

 

 Dependence On human Interpretation 

 The Role of Human Interpretation in Predictive Analytics In spite of the employment of complicated algorithms and huge data sets, human reasoning is still required for predictive analytics to become indispensable. The figures provided by the model are often just quantities or probabilities that have to be understood in a practical situation. This is the point where human experience and understanding enter.

Computers can detect patterns, but they lack the capacity to comprehend ethics, human feelings, or business objectives. An instance, a model could imply cost-cutting by laying off employees, but a human manager could provide evidence that this would be the case only if it were necessary to ensure that the team morale or customer service were not affected. Besides this, humans take into account factors that the model might not consider, such as possible market trends, local events etc.

One of the hazards is that when people misunderstand or uncritically follow the model's outputs, they do not even make the least effort to question them. If a speaker were to use predictions without knowing how they were created or what data were the basis thereof, then he or she might come to make a wrong or even dangerous decision. The people who make the decisions need to regard the models as just one of the many tools at their disposal and not as those that are "always right."

Comments

Post a Comment

Popular posts from this blog

Application of big data techniques to a problem 4

Image
  Application of Big Data Techniques A high dropout rate — 28 percent of freshmen in one program — is a major obstacle in online STEM education. Teachers collected a huge amount of data to attempt to tackle this, including assignments submitted, posts made in forums, marks on quizzes, views of video lessons and actions in LMS logins. After detecting stress and disengagement indices from chat contributions via natural language processing (NLP), regression models were used to predict who among the students would be at risk of performance dropout. Cohort dropout risk heatmaps, weekly teacher trend reports, and individual student engagement dashboards were applied to visualize this data. These findings enabled more targeted interventions: teachers were automatically alerted to reach out individually to students showing early warning signs, those at risk received specific prompts (e.g., offers of tutoring, inspirational messages, or resources); and teachers were given individual feedbac...
Read more

Application of big data techniques to a problem 1

Image
  Explaining Big Data Concepts Massive amounts of data are coming out of educational platforms because of the rapid growth of online learning, everything from discussion forum activity and video watch time to the frequency of login and assignments submission available. The titanic volume of data, often called “big data,” is being utilized by institutions for the purpose of the improvement of learning results and for the thorough understanding of student behaviour. Edutrack is one such example, where the amount of data annually went up from 1TB to over 10TB, making it necessary to use smarter data strategies instead of manual ones. This comprises, among others, the situation when manual progress tracking was the main method of monitoring, and the end-of-semester survey was the instrument that gave the teachers clues about the best topics for the next semester. The effective scope of the latter was not only limited by its low response rates but also by the time taken to supply an inp...
Read more

Value of Data

Image
Khalid Nawaz 10 March 2025    Value of Data (Including Future Value)  In today’s digital era, big data has become one of the most valuable assets for organizations across industries. It allows businesses to gain deep insights into customer behaviour, preferences, and trends, enabling more informed decision-making. By analysing large volumes of data, companies can optimize marketing strategies, improve product offerings, and enhance customer service. For example, retailers can use purchasing data to forecast demand, personalize recommendations, and manage inventory efficiently. In the healthcare sector, big data supports early disease detection, patient monitoring, and more effective treatment plans by analysing patient records and medical histories.  Governments use it to improve public services, monitor traffic patterns, and respond to emergencies. In agriculture, data from sensors and satellites helps farmers monitor crop health and manage resources more sustainabl...
Read more