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Like so many of my generation, I watched in awe and fascination the historic Apollo lunar landing on July 20, 1969.  This event paved the way for future space exploration and the mission symbolized how anything is possible. Since then, we have launched probes to Mars and one to Jupiter. Nearly 30 years later, the Hubble Space Telescope provided a glimpse at what is probably a planet outside our solar system. In 2018, Astronomers discovered evidence for thousands of black holes located near the center of our Milky Way galaxy. Our view of the universe has significantly evolved since Copernicus (1500’s) successfully formulated a heliocentric model of our solar system.  Every new piece of information building upon the next, providing a more detailed view.

Astronomers built on this model, adding more and more detail. Today if you look at the model of the universe, it is far more complex than the model first drawn by Copernicus. That’s how models work – as new data becomes available, models evolve to accommodate new insights.  All of us in business and Marketing can look to the heavens to navigate how we develop data models. A model helps us organize and understand how different data elements relate to one another and the properties of the real world and should be used to help make better business decisions.

We often talk with companies who are interested in creating data models. It’s common to hear the request for a model that looks a lot more like NASA’s than Copernicus’ model of the universe although many times, the data to create such as model does not yet exist. In situations such as these, we advise the following 5-step approach to the development of a data model.

  1. Start with a clear understanding of what you want to model. For example, a model of the process customers goes through to evaluate a solution or one for which of your customers are most likely to buy a new product or used to identify new market opportunities or one that helps you model the contribution of Marketing to the company’s revenue.
  2. You don’t have to start from scratch. You must start somewhere but you don’t have to start from scratch. Conduct a literature review to find out what models are out there that might be like what you need and consider using it as a base or starting point.  If the model has been well vetted, this will help you gain internal buy-in and accelerate your efforts.  Copernicus was able to leverage the work of others, including the work of Greek astronomer Aristarchus of Samos who suggested 1800 years before Copernicus that the Earth and planets revolved around a stationary Sun.
  3. Think like Copernicus. You’ll have to decide on your modeling methodology. Then create a high-level conceptual version of your model. Share it internally and make sure it will capture what is intended. While the model is being reviewed gain clarity around the data you will need to support the model. Remember to validate your data.  Bad data will result in a bad model. Consider Plato and Aristotle’s first models of our universe which placed Earth at the center and not the sun. The data wasn’t valid and as a result, neither was the model.
  4. Start simple. Use the initial data to vet and employ the model. As you begin to use it weave in your learnings and expand your level of detail. Build-in experiments that will help you gain more knowledge which you can then use to take the model to its next level. Make sure to gain buy-in at each stage.
  5. Be willing to change. It’s an iterative process. You will learn as you go. It’s important to recognize that models change as information becomes more available. As NASA gains more information, they can fill in more detail about space, stars, and planets. Maintain and scale your model to keep your model relevant.  This will most likely require you to invest in tools and processes.

Data modeling an essential key to success for supporting the business intelligence process. They are an integral part of capturing actionable intelligence to support effective decision making and planning. Want to start building your data model?

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