Final answer:
Data Analytics involves analyzing existing data to draw conclusions, while Data Science is a broader field creating new ways of understanding information, including predictive models and machine learning. Decision-support systems have evolved from simple data processing in the 1960s to complex data analytics and data science today, providing advanced decision-making tools.
Step-by-step explanation:
Data Analytics vs. Data Science
Data Analytics generally refers to the process of examining datasets to draw conclusions about the information they contain. It involves applying an algorithmic or mechanical process to derive insights and includes the use of statistical analysis, and predictive modeling to analyze and transform data into actionable information. Unlike Data Science, which is more exploratory and seeks to discover patterns and previously unknown insights, data analytics is more focused on processing and performing statistical analysis on existing datasets.
Data Science encompasses a wider scope that includes creating new ways of modeling and understanding the unknown by using raw data, alongside being interdisciplinary including techniques and theories from mathematics, statistics, information science, and computer science. A data scientist will use a variety of techniques from machine learning, predictive modeling, and data mining to analyze and interpret complex datasets.
Evolution of Decision-Support Systems
In the 1960s, decision-support systems (DSS) primarily relied on simple data processing, which was largely about automating existing manual processes. As technology advanced, these systems evolved to incorporate more sophisticated forms of data storage, retrieval, and manipulation. By the 1990s and early 2000s, the focus shifted towards providing tools for data analysis and decision-making support. Currently, decision-support systems harness the power of both data analytics and data science, employing advanced algorithms and machine learning capabilities to predict, prescribe, and enhance decision-making processes across various industries.
Modern-day data analytics tools and decision-support systems are critical for deriving insights from large datasets, aiding in evidence-based decision-making. Such capabilities are essential for academic research, economic forecasting, health sector growth, and many other fields requiring complex data interpretation.