Successful businesses and organizations thrive by learning continuously the data in the past, current situations, and foresee future possibilities. But how do companies achieve this?
The answer lies in data analytics. Companies gather data constantly, yet the raw data itself lacks real meaning. The true value emerges from how this data is harnessed. Data analytics involves scrutinizing raw data to uncover patterns and insights that shed light on specific aspects of a business.
In this article, we will see different types of data analysis such as: Descriptive, Diagnostic, Predictive, and Prescriptive. These diverse approaches allow companies to unravel the past, solve current problems, predict future trends, and prescribe optimal strategies.
Descriptive Analysis
Descriptive analysis is the most commonly used analysis to describe what happened in the past. It involves summarizing and presenting data in graphs or charts to visually represent patterns and trends in the data. For example, Google Analytics is one example of a descriptive analysis tool. It shows summarized data to users about what happened in the past.
Descriptive analysis focuses on the main features of the data, such as central tendency, dispersion, and frequency distribution. This type of analysis helps in organizing and describing raw data to make it more interpretable.
Applications:
1. Let’s consider a retail business analyzing its daily sales data. Descriptive analysis would involve calculating the mean, median, and standard deviation of the sales figures. It may also include creating visualizations like histograms or bar charts to showcase the distribution of sales across different products.
2. In the healthcare sector, hospitals can use descriptive analysis to study patient demographics, such as age and gender distribution. By analyzing these factors, hospitals can allocate resources more effectively and plan for specialized medical services based on the prevalent patient demographics.
Diagnostic Analysis
The diagnostic analysis is the next step of descriptive analysis. The diagnostic analysis involves investigating and identifying the root causes of specific issues or problems within a given dataset. For example, a company made a graph and found out there is a 50% increase in sales last month. The possible next move will be performing a diagnostic analysis to know what happened. This type of analysis is particularly valuable when there is a need to understand the reasons behind certain outcomes or anomalies in the data.
A diagnostic analysis is like a magnifying glass that allows us to zoom in on a particular event, behavior, or phenomenon. It involves a systematic and rigorous examination of data to identify factors that contribute to a specific outcome. This type of analysis is especially valuable when we need to go beyond the surface and gain insights into the underlying mechanisms that drive observed trends.
The process of diagnostic analysis depends on the difficulty of finding the root cause behind a particular issue. The complexity of the problem influences the depth and breadth of the analysis, as well as the tools and techniques used to uncover the underlying reasons. Here is a general process for diagnostic analysis:
- Problem Identification
- Data Collection
- Exploratory Analysis
- Hypothesis Formulation
- Testing and Analysis
- Root Cause Identification
- Validation and Interpretation
- Actionable Insights
Applications
1. Consider a manufacturing company experiencing a sudden increase in product defects. Diagnostic analysis would involve examining the production processes, quality control measures, and other factors to determine the underlying causes of the defects.
2. In the medical field, diagnostic analysis plays a critical role in identifying diseases and conditions. For instance, medical professionals use diagnostic analysis to analyze patient symptoms, lab test results, and medical history to accurately diagnose illnesses and recommend appropriate treatments.
Predictive Analysis
Just like the name itself, predictive analysis involves using historical data to make predictions about future outcomes. Predictive analysis involves the application of statistical algorithms, machine learning techniques, and data mining to create predictive models.
These models learn from historical data patterns, identify relationships between variables, and extrapolate these insights to make predictions about what is likely to occur in the future. It’s like taking the puzzle pieces of the past and using them to assemble a picture of what lies ahead.
Real Case Study:
Business and Marketing: Organizations use predictive analysis to forecast customer behavior, sales trends, and demand for products. It helps optimize marketing campaigns and tailor strategies for maximum impact.
Healthcare: Predictive models can predict patient outcomes, disease progression, and potential health risks. It aids doctors in making timely interventions and providing personalized care.
Finance: In the financial sector, predictive analysis is used to predict stock prices, detect fraudulent transactions, and assess credit risk.
Manufacturing: Predictive analysis helps optimize production processes, minimize downtime, and predict equipment failures, enabling proactive maintenance.
In general, predictive analysis provides a roadmap for organizations, enabling them to make well-informed decisions and allocate resources strategically. It empowers us to anticipate future trends, challenges, and opportunities, transforming data into actionable insights. Just as a weather forecast guides our plans, predictive analysis guides organizations on their journey, helping them navigate the uncertain seas of the future with greater confidence.
Prescriptive Analysis
Prescriptive analysis provides actionable recommendations on what should be done. By far, prescriptive analysis is the most complex analysis because it goes beyond descriptive and predictive methods. While descriptive analysis explains “what happened” and predictive analysis forecasts “what might happen,” prescriptive analysis dives into “what should we do.” It leverages historical data, real-time information, and advanced algorithms to generate a range of potential solutions, recommending the one that promises the most favorable result.
How Prescriptive Analysis Works:
Prescriptive analysis uses data and advanced algorithms to recommend actions that can help us reach specific goals or outcomes. It considers various possible scenarios and suggests the most effective steps to take. It’s like having a map that not only shows you different paths but also highlights the one with the fewest obstacles and the smoothest journey.
Real-Life Example
Supply Chain Management: It helps optimize inventory levels, distribution routes, and production schedules to minimize costs and meet demand efficiently.
Healthcare: Prescriptive analysis can suggest personalized treatment plans for patients based on their medical history, test results, and genetic makeup.
Energy Management: Organizations use prescriptive analysis to optimize energy consumption, allocate resources, and reduce environmental impact.
Finance: It aids in portfolio optimization, risk management, and investment strategies, recommending the most profitable and risk-efficient options.
Summary
In essence, these four types of data analysis form a progressive continuum, with each type building upon the insights gained from the previous one.
- Descriptive analysis sets the stage by presenting the data landscape
- Diagnostic analysis uncovers the reasons behind observed phenomena
- Predictive analysis forecasts future trends
- prescriptive analysis guides decision-makers toward the most advantageous actions
Together, these analysis types empower organizations to leverage data as a strategic asset. They help unravel complex challenges, inform strategic planning, optimize operations, and drive innovation across various domains.
