Data is the lifeblood of the Internet of Things. However, there’s a huge difference between having data and using data. That’s why this post explores the value of smarter data. Previously, we talked about the fundamentals of data management. Analytics is the next step in the journey.
Not too long ago, IoT meant “connect things because you can – and it’s cool, too.” Now, as the IoT market matures, there’s more focus on questions like, why am I hooking this up? Is the data useful to me? What will I do with it?
Many organizations believe they’re building an IoT-focused business because they’ve added sensors to various things. Things like presses, refrigeration units, drills, pumps and robot arms. Data rolls off all these things, and the business gets a graph as a snapshot of performance.
Imagine you have a factory …
Let’s imagine you have a factory, and your equipment is outfitted with IoT sensors. When a machine malfunctions, your sensors record the status that this machine has stopped running. Depending upon how long you store your data, you could see how many other machines have had problems, too.
In this very simple scenario, you have IoT data, and it’s data you obviously need to keep your business up and running. But the true power of the Internet of Things is when you begin to use that data.
Now let’s make your factory even smarter
Let’s take our scenario to the next level. You still learn that your machine isn’t working. But now, that data immediately triggers an action, and generates a work order that then schedules someone to come and repair the machine.
Next, this information is combined with all other machine information. It is then analyzed against historical data to spot potential trends. You ask your data questions like:
- How old is this machine?
- When was the last service call?
- How does its performance compare to other machines?
- How long has it been running without incident?
- What anomalies indicate potential problems?
Using data for predictive maintenance
By asking these questions, you can begin to spot trends and also begin to make predictions. For example, you might find that with machines two years or older, one-third of them have problems a month before their annual service inspection. You can then reevaluate when scheduled maintenance is done, and shorten the inspection cycle to 10 months. You’ll reduce machine downtime, reduce operations cost and help protect revenue, too.
When you consider your IoT data as a long-term asset rather than a perishable one, you’ll add much more value. You’ll derive insights and spot trends. And you’ll be able to take proactive and preventative actions.
Consider the Watson IoT Platform
There are four great reasons to consider the Watson IoT Platform:
- We take the worry out of data storage, since it’s already included. That means you can derive the maximum value from your information.
- Out of the box, you’ll find both general analytics formulas to quickly get started, along with the flexibility to add your own unique functions, specific to your business.
- There’s a low-cost option that lets you start small – and quickly — to test your concepts before moving into a full-production instance.
- Others think we’re a good choice, too. IBM was named a leader in The Forrester Wave: Industrial IoT Software Platforms, Q3 2018. We invite you to download the report.
Learn more about the Watson IoT Platform today.
About the author: An engineer by training and a lifelong technology enthusiast, Jiani Zhang is the Program Director for Offering Management for the IBM Watson IoT Platform. In this role, she helps lead customer engagements and guides the development of the Platform technology, both of which help clients realize business results. Previous to this role, Jiani led an offering strategy and management team focused on Industrial IoT. And to round out her IoT expertise, she also served as an original member of the IBM IoT leadership team. Her technology expertise runs from product design and development, to management and consulting.
Jiani holds a B.S. in Electrical Engineering and Computer Science from University of California, Berkeley and an M.B.A. from UCLA Anderson with emphasis in Technology Management.