Data-Driven, Goal Driven, and Model Driven Decisions

We live in a world where data is collected globally, twenty-four hours a day. In the past, we relied on human experience to make business and personal decisions. For example, corporate managers used expert opinions to forecast sales, a method known as the Delphi approach. This was the best practice of that time. In the digital age, the data-driven approach focuses on analytics instead of guesswork and emphasizes collecting data related to the sales forecasting problem. This data can vary, including economic indicators, customer preferences, and various factors influencing sales generation. The above illustrates a data-driven decision regarding forecasted sales. The strength of the data-driven approach lies in its ability to continuously provide new information, allowing managers to make changes and improve forecasts. The weakness of a strictly data-driven system is that it sometimes stifles innovation and creativity in forecasting.

The goal-driven approach differs from the data-driven approach, even though data is essential for making goal-driven decisions. In the goal-driven approach, there are clearly defined goals. For instance, a manager sets goals to achieve five million in sales. In this process, the manager searches for data that will help achieve those goals, analyzes it, and formulates a strategy to achieve the goals. In the goal-driven approach, specific data needed to achieve the goals is pulled to decide how to generate five million in sales next year. The success of the goal-driven data approach depends on access to needed data and clear goals and objectives.

Model-driven approaches are used when historical evidence indicates that the model helps solve problems. The model-driven approach comprises three modules: input, process, and output. The input collects the data required for the model, while the processing module includes established analytics. The output delivers solutions to the problem based on the input. For instance, it is demonstrated that there is a correlation between speed and fuel consumption; a model could be employed to determine a specific speed to use an allocated amount of gas. Although the model presented above is simple for personal use, we have a complex business decision-making model involving hundreds of variables.

A data-driven, goal-oriented, or model-based approach can be applied in business, education, and personal life. For example, consider an educator in a history class who wants to discover which areas of history most students are interested in exploring. This educator can implement a data-driven strategy in a digital classroom by asking open-ended questions and using analytics as data is collected and integrated into the lesson plan. Similarly, a math educator may adopt a goal-driven approach to teach fundamental addition and multiplication concepts to achieve the learners' educational objectives. In personal life, an individual can apply a model-driven approach for self-improvement by gaining knowledge in psychology, sociology, math, and history. These subjects are essential components of general education in universities and colleges across the country. The combined knowledge of these fields will enrich individual lives. Psychology helps us understand human behavior, ourselves, children, and others. Math plays a role in everyday life, whether calculating the speed you drive, determining if you have enough cash to buy coffee, or figuring out how many hours you need to work to pay bills. History reveals the successes and failures of the past and shapes our present lives. Sociology aids in understanding family structure, function, roles, relationships, culture, and more. Embracing a model-driven approach can enhance life and channel personal energy toward positive emotions and outcomes.