Cloud-of-things analytics has a simple, powerful appeal. It offers the opportunity to know more about your business, faster. For industries ranging from retail to manufacturing, that means better operational visibility, more responsive customer service, more automated reaction to problems, and improved preventative maintenance. That’s not to mention the potential to increase revenue by launching innovative services based on new insights or event-triggered actions.

But there are challenges to moving analytics into the cloud and building a meaningful framework. Each company has different skills, capabilities, experiences, and needs when it comes to analytics. Some are highly proficient and looking to operationalize their deep learning models, while others are still introducing more contextual data sources. Here are some tips that can help companies at any stage move to the next analytics level.

1. Just build the first frame

There is nothing more daunting to a writer than a blank sheet, to a painter than an empty canvas, or to a data scientist than an empty pipeline. You have in your mind’s eye the types of insights you are looking for, but the task of transforming the data, building and testing the model, creating visualizations, and then turning the output into action causes analytical block.

I advise companies to just start. Build a basic data frame on a relatively manageable and familiar dataset, process some basic statistics against it like counts and baselines, and create some simple views. Then start to layer in new data, add more complex analytics, and build out new visualizations. These will be the foundation of the initial solution. The aim is to keep it simple, flexible, and understandable. It builds confidence as the process starts to unveil the direction of the project.