Data analytics in the IoT opens up new opportunities for MSPs

Partners have the opportunity to expand their IoT services in the emerging field of analytics.

Indeed, CompTIA, an industry association, has identified three areas in which IT service providers can engage in the IoT market: the sale of IoT devices, their management and monitoring, and data analysis. . However, providing data analytics in the IoT requires specialized skills.

Carolyn Avril

“This requires analytical skills and training, as well as the ability to apply data findings to business opportunities, goals and challenges that a client might have,” said Carolyn April, senior director of analytics. industry at CompTIA. Providing such a service “will require investments from distribution companies in terms of human resources, technical and commercial skills,” she noted.

The channel is “not quite there yet,” April added, but said data analysis is an area of ​​interest that has grown in importance over the past three years of studies on the channel. status of the CompTIA channel.

First successes

Vivek Kaushik calls IoT analytics “IoT version 2.5,” explaining that the chain is still looking to monetize the sale of IoT sensors and devices. But Kaushik, senior vice president of client services and account management at CSS Corp., a provider of technology consulting services, called IoT analytics “an exciting space.”

The company, based in San Jose, Calif., Has started capturing data from IoT devices “and we are able to infer and create predictive models to help [clients meet] the business challenge, ”he said.

Vivek KaushikVivek Kaushik

For example, CSS Corp. works with a client to extract data from devices on farm tractors to predict a diagnostic problem and when a tractor would need service.

Likewise, the company helps a customer of locomotive components and operations perform predictive maintenance on railway equipment to anticipate device failures, optimize services and inventory, and educate field engineers and purchasing managers on when to order spare parts.

In the railway example, CSS Corp. has developed a six-step methodology that begins with identifying the most critical data associated with the equipment for accurate predictions. Then, the data is trained and tested in the TensorFlow machine learning platform to create prediction models. Next, sensor data is merged with acoustic and image data to identify patterns, and then data from breaks, bearings, and wheels is analyzed to identify wear.

In this way, those responsible for CSS Corp. can identify unusual system behavior and resolve issues before they occur, as well as identify the best maintenance schedule and make recommendations to customers to optimize processes.

The next step is to see if the company can bring in another layer of intelligence to see why some equipment is performing better than others, Kaushik said. Beyond analyzing historical data to predict a maintenance failure, officials at CSS Corp. now want to see if they can use a locomotive company’s sensors and IoT data to suggest whether a different maintenance process would extend the life of a piece of equipment like a wheelbase.

This brings the analysis to the augmented level, he said. Augmented analysis includes information entering a centralized platform from different sources, including competitors of a locomotive company or other trains of the same company. “Maybe they oil [the wheelbase] twice a day and they have to do it three times a day, ”he said. The idea is to take inspiration from the best practices of what other trains are doing and go from predictive to suggestive, which is the augmented component.

AI will play a big role in making this possible, as a system gets smarter and smarter consuming more data and doing more repetitive tasks, Kaushik said. “This is the journey that we will see more and more of.”

Data Analytics in IoT: From Gyms to Retailers

Managed IT service provider Logically, headquartered in Portland, Maine, not only monitors IoT devices for a gym business, but also analyzes how much bandwidth its customers are using, said Joshua Skeens, CTO at Cerdant, an MSP belonging to Logically.

“We can actually tell you how many times these devices have connected in the last day or month so that you can see if there are repeat customers,” as well as when they are close to reaching. their bandwidth limit. In many cases, this helps customers determine if they need a new internet connection, he said.

Joshua SkeensJoshua Skeens

Besides gyms with chains, Logically also does IoT analytics work for sub-stores, gas stations and malls, Skeens said.

“It’s pretty scary the kind of data we can get from this stuff,” he said. “We are doing this to help businesses be proactive.”

Some customers are asking for this type of information, while others are not, and Skeens estimated that IoT analytics probably only represent about 5-10% of Logically’s business at the time. actual hour.

“Some [customers] don’t understand what their business case is, ”he said. “They know the data is there, but they don’t know what to do with it. “

For larger customers, Logically will tell them what kind of data is available, “and that’s telling,” according to Skeens. “They are starting to use it to make business decisions about whether to increase wireless capacity or whether to add more treadmills because there aren’t enough of them” for a period of specific time, he noted.

Retailers can use the data from the sensors to determine whether store displays are working or whether they need to change them, Skeens said. “You can track how long [customers’] The devices were in a particular location so we could tell if someone was standing in front of shoes, “he said.” It helps them rethink the layout of the stores. “

Obstacles to Analysis

Distribution companies face certain challenges when implementing IoT analytics, ranging from the right in-house skills to handling complex customer needs.

The tricky part for Logically is figuring out how to charge for IoT analytics services, Skeens said. “Is this a complementary service to what we offer or should it be stand-alone?” He said, adding that “this is something that MSPs deal with all the time.”

It’s an evolving industry – there is a long way to go.

Vivek KaushikSenior Vice President of Client Services and Account Management, CSS Corp.

For CSS Corp., one of the challenges is the fact that the industry does not yet use common IoT standards, Kaushik said. This results in a skills mismatch as IoT sensors use different programming languages ​​and platforms. “We are struggling to train, retrain and hire people with the specific analytical skills required for this job,” he said.

Another problem is that “customer expectations are quite high and sometimes the results are not there,” Kaushik said. “They think [IoT analytics] solve their problems ”, but he estimates that it may take two years for the partners to reach their expectations.

“It’s an evolving industry – there’s a long way to go,” Kaushik said. CSS Corp. also struggles to create more use cases. Right now, the company performs IoT analytics for around 30% of its customers when it comes to data and analytics.

“We work in a very limited set of use cases and PSMs like us don’t have a lot of money to invest in these innovation programs,” he said.

There is a lot of buzz, and every CIO and CXO wants to do more with AI, IoT, and intelligent intelligence, Kaushik said, “but the actual investments that go into its development are not as commensurate. “.

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