Whether you are learning about Lean People Analytics or just people analytics or analytics more broadly… you either know or you will eventually know that models are central to your success. Any method of people analytics I propose is going to come back in some way to models.
So what do I mean when I say model?
A model is an abstract representation of an object to help people understand or simulate reality. (By object I can mean a physical object or an abstract object like a system, theory or concept.)
Some models are physical objects. For example, a model of building. In this way a model can be used by an architect to convey the ideas and test them before they are applied at scale. Everyone can look at the model and make a decision if they like it or not. If they don’t like it the architect can ask why and change it. (By “at scale” I mean proportionally increased from where it is to its intended real world size). The model removes non-essential detail and material. Those can be added back if the decision is made to proceed.
Not to get too metaphysical on you but the example of an architect is also a model of sorts. It is a metaphor. The architect is an abstract imperfect depiction of the job that is to be performed by people analytics. That is provide the architecture for great companies. Architects don’t hammer the nails but they have an important job.
In people analytics we don’t construct physical objects. We work in abstract concepts and mathematics. However, architects also don’t just draw pictures of buildings. Architects need to understand abstract engineering concepts and mathematics. Mathematics becomes increasingly important to architecture as the scope of the project increases: tall buildings, bridges and other objects that people depend on for their lives. People depend on their jobs for their lives and the things people do at work impact the lives of others. The returns of the companies they work for fuel economic growth, which produces societal benefits. If the companies are not successful it has the opposite effect and we all bear those costs. All of this is why mathematics is important for Human Resources too.
Conceptual models are abstractions that connect or organize ideas serving the same purpose as a physical model but for things you can’t hold it in your hand. Most of what we work with in people analytics is conceptual so we have to get comfortable with conceptual models.
A model’s primary objective is to convey the fundamental relationship and functions of the system of elements that it represents without unnecessary detail. When implemented properly a model should satisfy four primary objectives:
- enhance understanding of the system of parts through their organization
- facilitate efficient communication between stakeholders
- provide a reference for analysts to make predictions, test ideas, extract meaning, while simultaneously offering grist for practitioners to formulate ideas on how to solve problems.
- document the system of objects for future reference and provide a means for collaboration
It is important to understand the different types of models and how they fit together.
Different Models For Different Things
No I’m not talking about runway models. Come on!
The word model is vague. There are a lot of different models.There are mathematical models, scientific models, business models, business process models, data models and other types of models. I could be talking about any of these or all of these.
In any given context I’m usually talking about one type of model at a time, but to form a complete blueprint for people analytics I am talking about combining five different types of models.
It is important to understand what they are, how they are used, how they work together and how to get them out onto the runway in the right order.
Once we have defined each relevant type of model then I will compare and contrast how a Lean People Analytics method uses the five models and how a traditional method of People Analytics uses the five models.
Five Models of People Analytics:
Business models are frameworks that describe how a business creates value — or, as management theorist Peter Drucker has said, business models are simply “a theory of a business.”
A business model is a conceptual model that describes and represents the elemental structure of how a business will earn profit. These conceptual models describe the elements of a business that include: problem focus, target customer focus (market), unique value proposition, channels, methods of generating revenue, total addressable market (projected target customer market estimates), projected costs, projected revenues, and any believed or real defendable business differentiation advantages.
Business models have changed over the years through innovation. Companies such as Ford (mass production), McDonald’s (fast food), Amazon (e-commerce) and Netflix (digital streaming) have all helped introduce new models for business.
Some modern theorists separate all business models into two large categories: Pipes (firms that create goods or services then sell them to consumers) and Platforms (creators of scalable digital networks to facilitate exchanges between groups).
People are in these business models somewhere, the question is where are they?
A scientific model is the conceptual model that describes and represents the component structure, relationship, behavior, and other views of a scientific theory for a physical object or process. A scientific model is a simplified abstract view of a complex reality. Sometimes these are represented in mathematical expression and other times they remain strictly conceptual diagrams for more accessible expression of theory.
E=MC^2 is a mathematical expression of a fundamental principle of how our universe works from the very large to the very small. It was developed through theory, refined with mathematics and tested through experiments. It abstract but it has helped us do everything from go to the moon, to creating large explosions, to harnessing the energy of the universe. Of course we don’t always like the side effects of those things but never-the-less the model works.
The quality of a scientific field can be assessed by how well the mathematical models developed on the theoretical side agree with mathematical results of repeatable experiments. Lack of agreement between theoretical mathematical models and experimental measurements often leads to important advances as better theories are developed based on the nuances of the findings.
Mathematical / Statistical Models
A mathematical model is a conceptual model that describes and represents the mathematical structure, relationships, behaviors, and other views of real world situations, represented as equations, diagrams, graphs, scatter plots, tree diagrams, etc.. A mathematical model is a simplified abstract view of a more complex real-world phenomena. Mathematical models can take many forms, including dynamical systems, statistical models, differential equations, or game theoretic models.
Simple Example: When a population doubles each year, the function P(n) = I x 2^n represents the population P after n years, where I is the initial population.
The National Council of Teachers of Mathematics (NCTM) says:
“Modeling involves identifying and selecting relevant features of a real-world situation, representing those features symbolically, analyzing and reasoning about the model and the characteristics of the situation, and considering the accuracy and limitations of the model.” (Principles and Standards for School Mathematics, p. 302)
Mathematical models are useful for a variety of reasons.
- Models allow condensed communication of the numerical expression of a real-life situation without non-essential detail.
- Mathematical models allow for logical testing of ideas.
- A model may help to explain a system and to study the effects of different components, and to make predictions about behavior.
For example – a rocket manufacturer should begin by designing a mathematical model and conduct simulations on a computer, rather than incur the costs of building million-dollar rockets and blowing them up for testing purposes. That might get expensive.
Eventually you have to test your rockets in the real world, but only after you believe you have worked out the mathematical model. As you launch real rockets, you collect data to see if the rocket performs as predicted and to adapt your model when things go off track.
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