Comparing Descriptive, Predictive, Prescriptive, and Diagnostic Analytics

February 17, 2023

What Are the 4 Types of Data Analysis?

Today, most organizations emphasize data to drive business decisions, and rightfully so. But data alone is not the goal. Facts and figures are meaningless if you can’t gain valuable insights that lead to more-informed actions.

Data analytics solutions offer a convenient way to leverage business data. But the number of solutions on the market can be daunting—and many may seem to cover a different category of analytics. How can organizations make sense of it all? Start by understanding the different types of data analytics, including descriptive analysis, diagnostic analysis, predictive analysis, and prescriptive analysis.

What are each of these categories? Are the different types of data analysis related? In short, they are all forms of data analytics, but each uses the data to answer different questions.

The 4 Types of Data Analytics

• Descriptive Analytics tells you what happened in the past.
• Diagnostic Analytics helps you understand why something happened in the past.
• Predictive Analytics predicts what is most likely to happen in the future.
• Prescriptive Analytics recommends actions you can take to affect those outcomes.

Let’s dive into each type of analytics and put them in context.

What is Descriptive Analytics?

Descriptive analytics looks at data statistically to tell you what happened in the past. Descriptive analytics helps a business understand how it is performing by providing context to help stakeholders interpret information. This can be in the form of data visualizations like graphs, charts, reports, and dashboards.

How can descriptive analytics help in the real world? In a healthcare setting, for instance, say that an unusually high number of people are admitted to the emergency room in a short period of time. Descriptive analytics tells you that this is happening and provides real-time data with all the corresponding statistics (date of occurrence, volume, patient details, etc.).

With its focus on summarizing data sets to answer the question, “what happened?”, descriptive analysis is the bread and butter of data analysis. It helps in identifying causes and effects, uncovering relationships, and understanding the reasons behind past outcomes. By leveraging the power of analysis tools and business intelligence, your data scientists can use descriptive analysis to see the patterns hidden within your raw data.

What is Diagnostic Analytics?

Diagnostic analytics takes descriptive data a step further and provides deeper analysis to answer the question: Why did this happen? Often, diagnostic analysis is referred to as root cause analysis. This includes using processes such as data discovery, data mining, and drill-down and drill-through.

Continuing the earlier healthcare example, diagnostic analytics would explore the data and make correlations. For instance, it may help you determine that all of the patients’ symptoms—high fever, dry cough, and fatigue—point to the same infectious agent. You now have an explanation for the sudden spike in volume at the ER.

By digging deeper and going beyond the “what” to discover the “why” of your data, diagnostic analysis helps your data scientists not only understand what has happened, but to draw actionable insights from the data that will inform better decision-making moving forward.

What is Predictive Analytics?

Predictive analytics takes historical data and feeds it into a machine learning model that considers key trends and patterns. The model is then applied to current data to predict what will happen next.

In our hospital example, predictive analytics may forecast a surge in patients admitted to the ER in the next several weeks. Based on patterns in the data, the illness is spreading at a rapid rate.

This type of analysis can provide some of the most exciting results for businesses, and its popularity is growing. By leveraging statistical methods like artificial intelligence and regression analysis for predictive modeling, data analysts can use predictive analytics to forecast future outcomes with greater accuracy. Business leaders can then use these forecasts to make better decisions.

What is Prescriptive Analytics?

Prescriptive analytics takes predictive data to the next level. Now that you have an idea of what will likely happen in the future, what should you do? It suggests various courses of action and outlines what the potential implications would be for each.

Back to our hospital example: now that you know the illness is spreading, the prescriptive analytics tool may suggest that you increase the number of staff on hand to adequately treat the influx of patients.

Considered by many to be the most advanced data analysis process of these four, prescriptive analysis is key to your business strategy. By using optimization algorithms and statistical modeling, it provides recommendations on the best actions to take for your desired outcome, from sales strategies and product pricing to resource allocation and staffing goals. Prescriptive analytics empower you to make truly data-driven decisions.

In summary:

These four types of data analysis work together. Each type of analysis builds upon the previous one, offering deeper insights and more actionable information for decision-making.

Both descriptive analytics and diagnostic analytics look to the past to explain what happened and why it happened. Predictive analytics and prescriptive analytics use historical data to forecast what will happen in the future and what actions you can take to affect those outcomes. Forward-thinking organizations use a variety of analytics together to make smart decisions that help your business—or in the case of our hospital example, save lives.

Digging Deeper: Additional Analysis Methods

Augmented analytics is a relatively new approach to data analysis that leverages artificial intelligence and machine learning to automate and improve the process of data exploration and analysis. It can be particularly helpful when dealing with big data, as it essentially augments human data analysts by providing them with intelligent tools and assistance. By simplifying data exploration and analysis with features like natural language query, augmented analytics makes data insights accessible to a wider range of users, even those without extensive data expertise.

Key characteristics of augmented analytics:

• Automates repetitive tasks like data cleaning and preparation
• Recommends relevant data visualizations
• Identifies patterns, trends, and anomalies in data and offers potential explanations

Statistical analysis involves the collection, analysis, interpretation, presentation, and organization of data using mathematical techniques. It relies on the application of statistical theories and formulas to quantify and draw conclusions about data.

Key characteristics of statistical analysis:

• Involves numerical data and quantitative methods
• Examines relationships between variables to create probabilities
• Allows for inferential analysis to make inferences based on a subset of your data

Non-statistical analysis involves qualitative analysis methods that do not rely on statistical techniques. It focuses on understanding patterns, themes, and meanings in data that are often non-numerical.

Key characteristics of statistical analysis:

• Involves non-numerical data such as text, images, or observations
• Often more subjective and reliant on the analyst’s interpretation
• Emphasizes understanding the context, themes, and underlying meanings

What is the difference between statistical analysis and non-statistical analysis?

Statistical analysis and non-statistical analysis are two prominent data analysis techniques that often go hand in hand. Each serves a unique purpose and involves different techniques and methodologies. While statistical analysis deals primarily with quantitative data and relies on probability to draw general conclusions, non-statistical analysis works with qualitative data to understand its meaning and context and tell the story behind the numbers.

Data science experts typically use both methods together to get a well-rounded picture of their data. For instance, our hospital noticed a change in public sentiment towards it after its recent surge in illnesses and wants to understand why. They can use Python libraries like Beautiful Soup or Scrapy, or an API, to pull data from social media posts related to the hospital and recent illness. Statistical analysis allows the hospital to assign sentiment scores to posts and to calculate and summarize the overall sentiment distribution while non-statistical analysis provides insight into experiences that drove social media users to post what they did. By putting the sentiment scores into context with the recent surge in illness, hospital administrators can fully understand how its real-world performance impacted its social media reputation in this situation.

How Can Data Analytics Improve Business Decisions?

Data analytics can significantly improve business decisions by providing insights that drive more informed, accurate, and timely decision-making. Here are some key benefits:

• Data-Driven Decisions: Intuition and guesswork are replaced by cold, hard facts. Effective data analysis provides a clear picture of what’s working and what’s not, empowering businesses to make data-driven decisions that are more likely to succeed.
• Improved Customer Understanding: By analyzing customer data, businesses can gain a deeper understanding of their target audience, their preferences, and their buying behaviors. This allows for better product development, targeted marketing campaigns, and improved customer service.
• Increased Efficiency: Data analytics can be used to identify inefficiencies in operations, logistics, and supply chains. By pinpointing areas for improvement, businesses can streamline processes, reduce costs, and boost productivity.
• Risk Management: Data analysis can help businesses identify and mitigate potential risks. For example, it can be used to assess creditworthiness of loan applicants or predict fraud in financial transactions.
• Innovation: Data insights can spark new ideas and opportunities. By uncovering hidden patterns and trends in data, businesses can develop innovative products and services that meet the evolving needs of their customers.
• Competitive Advantage: In today’s data-driven world, businesses that leverage data analytics effectively gain a significant edge over their competitors. By using data to inform their strategies, they can make faster, more informed decisions and stay ahead of the curve.

Leveraging data analytics empowers businesses to make smarter decisions across all aspects of their operations. It’s a powerful tool for understanding customers, optimizing processes, managing risk, and driving innovation. Read this whitepaper to see how Spreadsheet Server from insightsoftware helps SaaS businesses enhance their NetSuite data analysis.

What Are the Biggest Roadblocks to Good Data Analysis?

While data analytics can dramatically improve business decisions by providing insights into key performance indicators (KPIs) and metrics, integrating data from scattered sources can be cumbersome and requires time-consuming manual data collection.

Businesses often have access to multiple data sources, such as marketing or financial data extracts in a CSV or Excel file format, which must be merged before analysis. This manual process is time-consuming and not repeatable, leading to data consistency issues. Spreadsheet sharing can also cause formula errors and broken links, making it difficult to access a common source.

Governance and security concerns can also arise from manual data collection and aggregation processes, as sharing core financial information on spreadsheets or SharePoint can expose a company to cybercrime.

How a Data Analytics Solution Can Help

To build a more insight-driven organization, consider a data analytics solution that integrates analytics and data management capabilities, and avoids compatibility issues. A cloud-based platform with on-premises and hybrid data access is crucial for fast, easy access to insights and informed decision-making. An end-to-end analytics solution, like Logi Symphony from insightsoftware, supports the entire analytics process, from data gathering to providing insights and prescriptive actions, with security, flexibility, reliability, and speed.

The right data analytics solution will help your business:

• Utilize data-driven insights and analytics to improve decision-making and user experience
• Leverage embedded analytics to create interactive dashboards and personalized visualizations
• Democratize data analytics by empowering business users with self-service analysis tools
• Harness advanced analytical features to differentiate and outperform in the competitive landscape

By transforming your raw data into actionable insights, data analytics software like Logi Symphony allows you to uncover hidden patterns and trends, improve predictive capabilities, and make better, data-driven business decisions. Schedule a demo to learn more about how insightsoftware can give your business a significant competitive advantage in today’s data-driven world.