Every day, more IoT devices come online and begin broadcasting data. Need proof? Global spending for IoT is projected to reach $1.1 trillion by 2023. That means all those devices and all their data is only going to grow over the next few years. The question for businesses then becomes, what are you doing with your IoT data? If the answer is nothing, then you’re in the minority. The IoT analytics market is expected to reach $14 billion U.S. by the end of 2021 and $18 billion the following year. Up from $7.4 billion in just 2018. Why? Leveraging that data can help companies streamline operations and cut cost dramatically.
But IoT analytics is more involved than simply collecting data from connected devices. You need a coordinated IT strategy that allows data to be gathered and processed at scale before you can extract anything meaningful from it. With that in mind, here are 4 strategies for getting the most out of IoT analytics use cases.
#1 – Define your goals
What do you hope to achieve with IoT analytics? While this answer will certainly evolve over time, it’s important to ask yourself what your goals are before you get started. Maybe you’re looking for true contextual awareness of equipment and systems, a way to improve decision-making, better administrative control over resources, a way to reduce data management costs or a way to effectively comply with environmental regulations. IoT analytics can help with all that and more, but it’s important to have an idea of your goals before you craft the strategy to achieve them.
#2 – Create an IoT analytics infrastructure
Once you know your goals, it’s time to focus on your means. First you need to figure out who the key IoT stakeholders should be within your company and if they need to add any additional skills to manage analytics projects. Selecting a chief data officer (CDO) is advisable, someone responsible for not just leading data analytics strategy but also being an ambassador for their division’s efforts to the rest of the company. Reevaluating your current data infrastructure and repurposing it to serve IoT analytics projects is also advisable since IoT falls under the big data umbrella. Using infrastructure in this way can help prevent data silos and make cross-functional data analysis much easier.
#3 – Leverage AI
AI is quickly becoming an indispensable part of a successful data infrastructure, particularly for edge networks. Smarter applications can be created when AI is deployed as an analytical tool at the edge of networks. Smarter apps like this can help with everything including SCADA (supervisory control and data acquisition) or even video surveillance. Leveraging AI at the network edge like this is actually becoming more of a necessity than a luxury. And that’s because the growing number of IoT devices requires a smarter infrastructure in order to handle both the current amount of data being generated today and even more data to be generated in the months and years ahead.
#4 – Live in the cloud
And where is all that data going to be stored? Hopefully not on your own servers. The cost of creating and managing a dynamic on-prem storage solution to house the data generated by your IoT infrastructure would be astronomical. Offloading the storage of this data to the cloud makes sense at every level, from eliminating hardware upkeep costs to having lightning-fast agility and complete flexibility. It also helps that some of the world’s largest cloud vendors now offer IoT analytic suites that can run hyper-complex operations on a massive scale, which means you enjoy all the benefits of cutting-edge analytics without the cost of having to create a platform from scratch.
For more information on how IoT analytics or IoT analytics use cases can help your customers, visit our IoT Marketplace at https://iot.ingrammicro.com
or contact us at firstname.lastname@example.org