Tableau Software : How to start unlocking value from your IoT data with Tableau

There is lots of hype around more advanced analytic applications such as predictive maintenance with IoT sensors, but you need to walk before you can run. Based on our experience, the first, critical requirement before starting an IoT journey is creating a data-driven environment.

Let's remember the true value that the Internet provides. It allows people to get immediate access to information to make faster, better decisions. If we wanted to take a new product to market 25 years ago, it would take us months, if not years, to study the market, understand customer requirements, research competitors and competing products-and the list goes on and on. Today, we have all of this information at our fingertips.

The concept of the Internet of Things is very similar. It aims to help organizations make better decisions faster. 'Faster' is a key word here. These 'things'-devices or sensors-collect more data than was ever possible before and allow us to collect more relevant data and at incredible speeds. But the real value of the Internet of Things comes when you can interpret the data and take action. This starts with solving the last-mile problem -the reality that IoT solutions often fail because people ultimately can't see or understand the data they mine.

Companies are addressing this problem through self-service analytics software, building dashboards to detect trends and anomalies. They can also set alerts to notify stakeholders when things go wrong. For example, a large car manufacturer is using Tableau to analyze operational quality data to understand the source of degraded factory performance, including defect types, affected parts, production locations, people, and other variables. The company now has immediate access to this information, allowing them to quickly understand the state of affairs and adjust the business accordingly.

The foundation of the company's success lies in their dedication to identify and standardize key operational metrics all the way up to the executive board level. After establishing this foundational work, the company decided to embark on their first IoT initiative. They added IoT sensors to collect more frequent data points from their production line, allowing them to automate preventive maintenance tasks, minimize number of defects, and significantly increase car production output at a much lower cost.

Sensor data presents opportunities to solve problems and address question after question. But before this can happen, organizations need to make analytics a priority-a coordinated effort, backed and fully vetted by top management. Only then can you expand IoT across the whole enterprise.