Why do intelligent people keep making poor decisions?

Why do people make bad decisions?

People make bad decisions for various reasons. Decision-making is an event that occurs when there is uncertainty, and it involves a process of selecting the best course of action from available options. Sometimes, people make decisions based on intuition or their gut feelings, which may not always be reliable. Other times, they may seek advice from others who may not have the necessary expertise or experience to provide sound advice.

Furthermore, decision-making is often influenced by various biases, such as confirmation bias (seeking information that confirms pre-existing beliefs) or overconfidence bias (overestimating one's abilities or the accuracy of information). People may also be influenced by emotions or cognitive limitations, such as limited attention or memory capacity.

Another factor that can affect decision-making is the availability of information. Sometimes, people may not have access to all the relevant information, or they may not have the necessary skills to analyze the available data effectively. In such cases, their decisions may be based on incomplete or inaccurate information.

Can AI replace human decision-making?

AI can assist in decision-making by analyzing large amounts of data and providing insights that humans may not be able to detect. However, it is unlikely that AI can completely replace human decision-making, as decision-making often involves more than just data analysis. Many decisions are based on principles or values that may not be quantifiable or that require human judgment.

Moreover, AI is not infallible and can be subject to the same biases and limitations as human decision-making. Therefore, AI should be used as a tool to augment human decision-making, rather than replacing it entirely.

What is Augmented Analytics?

Augmented Analytics is a technology that uses AI and machine learning to assist in data preparation, analysis, and insight generation. It helps users to quickly and easily explore data, identify patterns and anomalies, and generate insights that may not be immediately obvious.

The goal of Augmented Analytics is to make data analysis more accessible to a wider range of users, including those who may not have a background in data science or analytics. By automating many of the tasks involved in data analysis, Augmented Analytics allows users to focus on generating insights and making decisions based on the data.

"As technology evolves, the amount, dimensionality, and complexity of data are increasing, making it increasingly difficult for business decision makers and analysts to identify important factors in the vast sea of data. More data means more data to analyze, explore, and test, and to find relationships between more data factors. This includes data management, data processing, data analysis, and the creation of artificial intelligence machine learning models, evaluation of results, and ultimately converting them into actionable decisions. The current process can no longer support such a large amount of data and allow users to find various combinations and relationships. Manual model building and management by data scientists may also lead to an inability to find key messages or incorrect or incomplete conclusions. Enhancing analytics can solve these problems.

From the perspective of users who need data analysis, enhanced analytics reduce the threshold for using data analysis tools by combining and applying automation and artificial intelligence technologies, opening the door to data analysis for more users and making it possible for more people to do data analysis.

Benefits of AI-enhanced analytics for businesses

The business analysis process is lengthy, with a large amount of data and many personnel involved. From data importation, to identifying needs and generating decisions, the process may involve several departments, and personnel responsible for producing reports may spend a week each month on it. Technologies like enhanced analytics can speed up this process."

For analysis stakeholders: They can enhance their understanding of analysis, reduce time spent communicating with data analysts, accelerate the analysis process, and improve decision-making agility.

For data analysts: The previous burden of regular analysis reporting is alleviated, allowing them to focus on more valuable tasks, discovering algorithms and techniques that are more important and impactful for the company's problem-solving.

For IT personnel: The time previously spent on data dredging can now be saved through centralized management of the enhanced analysis platform, allowing for more focus on data maintenance and security.

Do you think this fancy new term can be explained in a few simple words? Of course not. If it were that simple, you wouldn't be wasting your time reading this article! Now we are going to explain in detail the technology behind enhanced analytics. The technology behind enhanced analytics uses machine learning automation and artificial intelligence to enhance user abilities by transforming data management, analysis techniques, business intelligence, and data science, machine learning, and AI model development and usage. Ultimately, enhanced analytics combines data processing and business analysis to enhance data processing, and speed up the process of data preparation through machine learning and artificial intelligence technologies. This includes automating all the data management, data quality and profiling, data harmonization, data modeling, processing, enrichment, raw data development, and data cataloging required for Analytics and BI (ABI) and Data Science and ML Platform (DSML).

Firstly, in the ABI part, AI and ML technologies are used to assist in automating data preparation, insight generation, and insight description to enhance business analysts' exploration and analysis of data. Business analysts and citizen data scientists can use enhanced analytics tools to automatically and quickly search, visualize, and associate exploration, such as correlation, anomaly, clustering, key factors, and prediction, without the need for algorithm development and modeling. Business analysts and citizen data scientists can also easily create prototypes and hypothesis development to analyze data without the need for manual experiments. The ABI system also uses ML and AI to strengthen data exploration work. In addition, through the automatic generation of insights, the visual data exploration and interactive visualization exploration provided by the analysis BI tool will be provided. After adding a conversational interface and natural language search, the convenience and operational experience of users will be greatly improved through natural language technology.

The DSML platform automatically generates models for citizen data scientists (analysts or application developers) or data science experts. These DSML platforms assist in model development and lifecycle management.

Enhanced analytics is unstoppable, and 50% of people in the enterprise will make data decisions.

Gartner predicts that enhanced analytics will become a major driver of enterprise procurement analytics, BI, machine learning, and data analysis platforms in 2021. Through natural language processing, the utilization rate will be greatly increased, allowing employees to drive data analysis and business intelligence (BI) through natural language, increasing from the original 32% to over 50% usage, meaning that 50% of all leaders in the enterprise will make data-driven decisions. Automated data science will enable citizen data scientists to produce more advanced analysis in greater quantity. Even if enterprises have a small number of data scientists in 2025, they will be able to achieve the same amount of analysis as they do now.

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