Dominoes are cousins of playing cards and one of the oldest tools for game play. Whether domino is used in professional game competition or simply set up and then knocked over, it requires skill and patience to master. Dominoes also have a storied history. They began in China around the 13th or 14th century, and the markings on their surfaces—known as pips—originally represented the results of throwing two six-sided dice.
Dominos aren’t just for games—they can also be used to illustrate physics principles. For example, the way a domino falls has a lot to do with gravity. When a domino is standing upright, it has potential energy (energy stored in its position), but when it’s pushed down, its potential energy is converted to kinetic energy (energy of movement). This change in energy is what causes the chain reaction that leads to the fall of the next domino.
The same principle applies to plotting a novel. Think of every scene as a domino, and each scene can trigger reactions that influence the story in surprising ways. In fact, if your scene has enough impact, it can become its own chapter of the book.
In the book Domino Effect, author Nick Elprin describes how a single person can have a huge impact on the lives of everyone they come into contact with. This is an apt analogy for the way that data science can be deployed across the enterprise. With the right software, an organization can enable its data scientists to do their best work by enabling them to access the data they need when and where they need it.
For this reason, many organizations are adopting a modern analytics platform, such as Domino, that provides the framework and tools to support data science best practices. Domino’s catalog of integrated tools helps data scientists to quickly identify and connect the right languages, IDEs, and data sources.
Using Domino, data scientists can create a fully functional analytics ecosystem, with their choice of coding languages, IDEs, and tools integrated directly into one user interface. This is a significant improvement over the traditional, fragmented workflows that are still in use today.
For example, when a data scientist is working on a project with multiple collaborators, they often have to toggle between multiple applications to find the tools and information they need to complete their work. By integrating all these tools and data sources into a single workflow, Domino makes it easier for users to focus on the problem at hand—not switching between applications or navigating multiple logins. This can significantly reduce the time to complete an analytical project and ensure that the highest quality work is delivered on time. It also can increase the speed at which projects are completed and help to avoid costly errors. This is particularly true for complex problems with a high degree of uncertainty, such as those involved in machine learning or predictive modeling. The platform’s ability to automate the creation of reproducible, reproducible models is another key feature for ensuring data science best practices are followed.