Through Oath, Verizon leverages big data technologies like Hadoop, Spark, and Kafka for enhancing mobile advertising and generating new revenues and furthering customer engagement. But advertising is just one of the verticals in which Verizon is building tailored apps through IBM Big Data and Analytics.

AT&T has also been active in the area of IoT analytics, combining its IoT solutions with IBM Cloud and Watson to create AT&T IoT analytics capabilities, a collaboration that aims to help AT&T’s enterprise customers transform their industrial IoT data into analytic insights so they can take immediate action to improve business operations. By using AT&T’s IoT network and the IBM Watson Data Platform, AT&T’s IoT analytics solutions will ingest data from hundreds of “wells,” creating the models necessary with appropriate machine learning libraries and open-source technology to help predict potential failures or machine malfunctions. This will help enterprises detect anomalies in less time and with more accuracy.

T-Mobile is another that is looking at furthering its capabilities in the IoT. It introduced two IoT Access packs that make it easier and faster for enterprise customers to get IoT devices online. T-Mobile also continues to work on Category M and Narrowband IoT, and has partnered with Twilio, Sequans, and Novatel Wireless to expand its IoT capabilities.

Change of Mindset Necessary for CSPs in the IoT

The hope with each of these IoT-analytics driven approaches is to offset competition and declining growth in fixed and consumer-mobile services with IoT-enablement capabilities. If CSPs can build reliable and secure single-user networks that facilitate edge-to-cloud communications with minimal latency and optimal security, they can help enterprises in all verticals meet the expectations of their big data and analytics projects. The need is there, as evidenced by Gartner predictions that 60 percent of big data projects fail, namely because of a lack of skills and expertise to successfully deploy and run projects.

For telecom executives, this is a critical opportunity to redefine their roles as enablers in the IoT. They can go beyond just offering enterprises software-driven, cloud-based platforms that help collect, manage and analyze large volumes of data at Web scale. If they modernize their operations, they can go to the next level and build entirely new business models around IoT analytics for their enterprise customers.

The metaphor Gartner’s Sicular uses is that of a stealth aircraft "considered ‘invisible’ to radar, even though it is 'invisible' only to the most common frequencies of radar. In the IoT, CSPs have to modulate the frequency up and down, looking outside their normal range so they can be warned of the impending arrival of the next bomber,” relates Sicular, referring to the next wave of disruptors in terms of business models. “In the IoT, new business models and innovations are occurring at lightning speed, so it’s quite likely a bomber will appear out of nowhere and blow you to smithereens if you don’t tune into a frequency other than that which you are comfortable.”

It means CSPs should consider how big data and analytics in the IoT can transform what they can do for not only enterprise customers, but also other CSPs that are otherwise competitors. “Our CTO says ‘data without analytics is value not yet realized,” says Walker. “Data in and of itself has value, but CSPs can help companies determine where to invest in terms of adding value or adding cost. CSPs have a lot of potential ‘value’ flowing through their pipes, so helping enterprise customers turn data into revenue or into cost reductions would be an amazing opportunity.” 

As a manufacturing expert, Walker sees similarities between what manufacturing has gone through and what CSPs can now consider in the IoT. “We have large utility customers providing energy to large manufacturers, and for the utility the focus was all about uptime, reliability and keeping transmission lines open, much like CSPs that focus on their networks’ speed and reliability." She notes that utilities have started looking at new ways to leverage sensors, IoT data and analytics to help their manufacturing customers automatically "shed load" in terms of equipment needs — even going so far as to work with competing utilities to tailor production schedules to peak and off-peak requirements of large manufacturers, accommodating big orders energy wise, and without having to buy more equipment. CSPs can accomplish the same if they educate themselves about the esoteric needs of different industries and customers and the ways in which using IoT data and analytics can enable them to solve problems for their customers, and the new ways in which they can monetize new
capabilities.