7 predictive analytics skills to improve simulation modeling

Statistical analysis

Statistical analysis is integral to both predictive analytics and simulation modeling. It involves the collection, organization and interpretation of data. It's often considered one of the foundational elements of data science. As such, learning more about statistics and statistical analysis is a good starting point for aspiring data professionals.

This technique compares data sets and studies the relationship between them. It helps determine which data to include or exclude from analysis. It can analyze historical data, synthetic or experimental data and random data. In the latter case, probability theory is often core to analysis.

Statistical analysis begins with identifying and organizing relevant data, then interpreting it to test hypotheses. These hypotheses can then inform decision-making. For example, data professionals use statistical analysis first to create the framework for a simulation model, with the goal of portraying the system being simulated more accurately.

Predictive analytics is often considered a type of statistical analysis that focuses on extrapolating results to predict future events or outcomes. It can significantly improve a data professional's ability to uncover relationships between data variables, understand the underlying factors driving predicted outcomes and improve the accuracy of interpretations and insights.

Data preprocessing

Data preprocessing is a data preparation technique that transforms raw data into a usable format for tasks such as predictive analytics and simulation modeling. It serves two primary purposes: streamlining data access and improving result accuracy.

Without preprocessing, such data is essentially useless.

This begs the question: Why does data need to be processed twice? In a business environment, data enters systems from a variety of sources with no consistent format. Errors might be present, data might be duplicated, and data sets might not be complete. The data might also not be in the correct format for processing by certain tools, such as machine learning algorithms or business intelligence (BI) platforms. Without preprocessing, such data is essentially useless.

Data preprocessing also includes related data preparation techniques, such as data profiling, data cleansing, data reduction and data enrichment. These skills allow data professionals to transform any type of data set they work with, which can directly influence the quality and accuracy of predictive analytics results and simulation models.

Model selection and evaluation

Model selection involves choosing the most appropriate model from several options based on how well each one represents the system or scenario being analyzed. A data professional may have a few models to choose from based on existing data sets. However, only one may meet all the criteria needed to appropriately simulate the system.

Model evaluation measures how well a model performs. Key questions include whether the model accurately captures the behavior of the system it is simulating, how well it performs on new or previously unseen data and whether the results are reliable enough to inform decision-making.

Both skills are grounded in statistical analysis. Model selection draws on established principles to help data professionals identify suitable models, whether they're for simulation modeling, machine learning or predictive analytics. Some models are designed specifically for prediction and may be better suited to predictive analytics use cases.

Model evaluation teaches data professionals the fundamental criteria for assessing models. This covers cross-validation, stepwise regression and measuring false discovery rates. Applying both model selection and evaluation to simulation modeling and predictive analytics can lead to more accurate results.

Model optimization

Simulation modeling is used to mimic a system's behavior, which can provide insights into how a system operates under certain conditions or in different scenarios. In contrast, model optimization focuses on finding the best solution for a given problem. This is often in the form of an actionable recommendation.

Model optimization complements simulation modeling by narrowing down results. For example, a data professional can run several scenarios through simulation modeling to get a better grasp of how a system might behave, then use those insights to identify an optimal course of action.

Model optimization also refines predictive analytics by narrowing the range of predicted outcomes. When extrapolating a pattern, the predictions from analytics might range widely. This can be due to several factors, such as not enough complete data or missing variables in a data set. When decision makers need more concrete results, optimization helps fine-tune results and settle on the most realistic data points.

Simulation integration

Simulation integration involves combining multiple simulation models to more accurately reflect system behavior in a broader context. For example, simulating how different software tools might interact might require linking models for each tool to explore the interactions between them. This could provide insight into the interoperability of the simulated software and how it may behave in the real world.

Data professionals can apply several simulation integration techniques that are useful in different scenarios. This skill can help in industries that involve many moving parts, such as manufacturing, where simulating different component interactions can save a significant amount of work when prototyping.

Simulation integration can also incorporate predictive analytics and real-time data. This involves integrating additional data sets into a simulation model to see how it performs or changes. Doing so can provide a more dynamic view of behavior that can be useful for experimentation and extrapolation.

Validation and monitoring

Assumptions and hypotheses developed during simulation modeling often require scrutiny. How accurate are the results, and can the insights be trusted? This is where verification and validation come into play.

Verification ensures that a simulation model follows the original specifications. During this process, a data professional should test the model for errors and resolve them by double-checking that all specifications match the intended design.

Validation assesses whether the simulation model accurately represents the real-world system it is meant to reflect. With this goal in mind, data professionals create a value range that represents how accurately the model simulates that system. If the model falls outside of that range after testing, they revise it until it meets validation standards.

Various verification and validation techniques provide credible evidence of a simulation model's accuracy and determine the quality of predictive analytics results. However, verification and validation don't have to be one-and-done tests. Ongoing monitoring of simulation models and predictive analytics ensures that output remains accurate and trustworthy within a designated range.

Feature engineering

Feature engineering is a data preprocessing technique that focuses on transforming raw data for use specifically by machine learning models. This targeted focus is what distinguishes it from other data preprocessing techniques and makes it particularly important for data professionals working in advanced modeling.

In feature engineering, a feature refers to the data variables a machine learning model requires to deliver the desired results. Data professionals select or create features from existing data, then transform and extract them to power a model. This might require data preprocessing tasks such as cleaning, organizing, reformatting or enriching data.

Feature engineering is central to building high-quality machine learning models. Learning the steps of the process helps data professionals enhance the predictive accuracy of simulation models. Because it's highly context-dependent, there is no standard feature engineering process. This skill might require some hands-on work to learn effectively, because it usually involves combining various data preprocessing techniques to execute.

Predictive analytics strengthens simulation modeling by grounding it in data-driven insights, but applying both techniques effectively requires data professionals to brush up on their skills. That said, these skills are also worth learning on their own, because they're essential to many other data science techniques. With a comprehensive skill set to pull from, professionals can explore new ways to enhance results and fuel data-driven decision-making.

Jacob Roundy is a freelance writer and editor with more than a decade of experience with specializing in a variety of technology topics, such as data centers, business intelligence, AI/ML, climate change and sustainability. His writing focuses on demystifying tech, tracking trends in the industry, and providing practical guidance to IT leaders and administrators.