Full Answer Section

1. Executive Information Systems (EIS): Emerging in the 1980s, EIS were a specialized form of DSS tailored for senior executives. These systems provided easy access to internal and external information critical to strategic goals, often presented through user-friendly graphical interfaces (dashboards). The emphasis was on summarizing key performance indicators (KPIs) and providing drill-down capabilities for high-level oversight. For a hospital CEO in Kisumu, an EIS might have displayed aggregated data on bed occupancy rates, patient satisfaction scores, and revenue trends, enabling quick strategic insights without delving into granular operational details. EIS refined the idea of data presentation for strategic decision-making, setting the stage for dashboarding and high-level reporting prevalent in modern analytics.
2. Online Analytical Processing (OLAP): Developed in the early 1990s, OLAP represented a significant leap in data analysis capability. Unlike traditional relational databases optimized for transactional processing (OLTP), OLAP systems were designed for multi-dimensional analysis, allowing users to rapidly analyze data from different perspectives, such as “slice and dice,” “drill down,” and “roll up.” This enabled complex queries and ad-hoc analysis on large datasets. In healthcare, an OLAP system could allow a department head to analyze surgical outcomes by surgeon, procedure type, patient demographics, and facility location simultaneously, identifying patterns and trends that were previously difficult to discern. OLAP introduced the power of multi-dimensional data exploration, a core capability that underpins much of modern business intelligence and analytics.

Concepts of Descriptive, Predictive, and Prescriptive Analytics

These three concepts represent a progression in the sophistication and value derived from data analysis:

• Descriptive Analytics: This is the most fundamental level of analytics, focusing on what has happened. It involves summarizing historical data to yield insights into past events. Common techniques include reporting, dashboards, data visualization, and basic statistical measures (e.g., averages, counts, frequencies). In a healthcare setting in Kisumu, descriptive analytics might involve generating reports on the number of outpatient visits in the last quarter, the average patient wait time in the ER, or the most common diagnoses seen in a specific clinic. It provides a clear snapshot of the past performance, allowing organizations to understand their current state based on historical data.
• Predictive Analytics: Moving beyond simply knowing what happened, predictive analytics aims to understand what might happen in the future. It uses statistical models, machine learning algorithms, and historical data to identify patterns and predict future outcomes or trends. This involves forecasting, classification, and estimation. For a healthcare organization, predictive analytics could be used to forecast patient admissions based on seasonal trends and public health data, predict the likelihood of a patient developing a certain condition based on their medical history, or anticipate equipment maintenance needs. The value lies in its ability to provide foresight, allowing organizations to prepare for future scenarios.
• Prescriptive Analytics: This is the most advanced level of analytics, answering the question: what should an organization do? It builds upon descriptive and predictive insights by recommending specific actions or decisions to achieve a desired outcome or optimize a process. Prescriptive analytics often employs optimization algorithms, simulation, and decision modeling. In healthcare, this could involve recommending the optimal staffing levels for a hospital ward based on predicted patient census, suggesting personalized treatment plans based on a patient’s genetic profile and predicted response to therapies, or determining the most efficient supply chain logistics to minimize waste and costs. Prescriptive analytics goes beyond prediction; it provides actionable intelligence to guide decision-making.