Business modeling for agile decision-making: Balancing complexity with agility

In conducting business analysis work, the challenge often isn't just in analyzing existing data but in tackling problems devoid of any prior data. How do you predict outcomes when starting from scratch?  This demands not only interpreting information but also making assumptions and hypothetical scenarios, which is accomplished by creating a decision model.

What is a model?

Simply put, a model is a representation of something, typically on a smaller scale.  

A plastic car model is a tangible example, in that pieces representing parts are assembled to create a small-scale representation of a real car.  The number of pieces reflects the detail of the model, and more pieces means greater details but requires more time.  

The complexity of a model dictates its scale and, by extension, its agility.

Similarly, a business model is a representation of a business initiative, composed of inputs such as investment, employees, inventory, equipment to result in sales and profit.  And the more inputs into the business model, the less agile it is to create and manage.

An illustration of complexity versus agility from Pixar’s Cars

As told in the book Creativity, Inc., Pixar’s approach in animating the character Lightning McQueen in "Cars" exemplifies the balance between complexity and agility.  

The initial digital 3D model of Lightning McQueen was in high detail to emote facial expressions.  Detailed digital models consume significant computational resources. So for long-shot racing scenes that did not require facial expressions, a less detailed model sufficed, conserving resources without compromising the storytelling. 

This illustrates how the context dictates the necessary scale of the model.

Key considerations for effective modeling

Effective modeling requires a fine balance. A detailed model provides depth but demands more time both to build and to manage. It requires stakeholders to accurately estimate input values, a process often fraught with inaccuracies that compound with the complexity of the model.   

The following are considerations when creating a decision model.

Purpose and Scale Understand the specific problem or question the model needs to address. A full-scale model may not always be necessary if only a quick high-level need.

Level of Detail Determine the essential details, which represent controllable inputs or assumptions required for decision-making. 

Relevance Do not include variables that you cannot measure if you intend to analyze actual performance against plan. Analysis of inputs against actions facilitates explaining variances.

Frequency of Use Decide whether the model will support ongoing decisions or a one-time analysis.  Institutionalized models tend to require detail and stability, whereas one-time models are disposable.

Timeliness Balance the need for detail against the urgency of the decision.

Conclusion

Effective modeling is more than just including numerous inputs; it's about striking the right balance between detail and agility. By starting with a simpler model and iterating based on feedback and needs, you can provide quicker, adaptable solutions that drive strategic decisions.

This approach not only conserves resources but also aligns with the dynamic needs of business.