DZone Research: IoT Futures

To understand the current and future state of IoT, we spoke to more than a dozen IT executives active in the space. Here's what they told us when we asked, "What do you see as the biggest opportunities in the continued evolution of IoT?:"

• Cognitive services will play a massive role in IoT as it evolves, allowing teams to build in intelligence into their IoT apps. Intelligence is basically machine learning, decision-making, and data crunching to derive insights at scale. Using cognitive services allow you to analyze and process massive amounts of data and derive insights and make predictions on it. It allows you to program your deployment to make decisions based on collected data (like a smart irrigation system changing how much water it’s emitting). And cognitive services are now accessible and affordable through services like Watson, AWS, and Azure, so teams can easily R&D and scale the technology to their needs.
• Enterprise B2B and B2E as in the general app space.
• We think decreasing the cost of sensors is important. Sensors are very useful for readings. But they’re very expensive. CO2 is $15, and a PM sensor is $50. Actively looking to reinvent the sensor in the air quality space to get the price down to one or two dollars and form factor down to the size of an accelerometer on a phone. Enable air quality intelligence in cars, homes, and businesses — the key is reducing the cost. Across the IoT space, prices have to get cheaper – Wi-Fi light bulbs still $20. Make integration cheaper.
• Developing models that help us use the data productively. Ingest without insight is waste. What makes a good ML project? What are the right methodologies? What are the best way to surface the results? How do we integrate with humans? How to make data consumable and actionable.
• Longer-term government, smart cities, manufacturing cut cost, and increase revenue with consumers more visible. Additionally, implement industrial process controla and OCF scales across all opportunities.
• More research and development will connect and automate tasks between devices. Securing these devices will be very important so that they only carry out pre-programmed tasks. And, of course, data privacy must be addressed somehow.
• Getting access to different kinds of data you haven’t had access to in the past. Ability to deploy sensors and change business models based on real-time data. Also getting humans out of the way, build more ML tools, use data science to make decisions in real time. Provide action based on the data in an automated fashion.
• I think there’s still so much low hanging fruit in this space, especially around waste reduction. These are opportunities where more accurate information can help the environment and save companies money. For example, 1.5T gallons of water is wasted annually due to inefficient irrigation practice—a number that could be reduced with sensors detecting leaks and soil moisture more accurately.
• IoT is becoming a mainstream component of many customer-facing and internal business applications. As companies expand their IoT initiatives, rolling out new IoT use cases that leverage their existing IoT platform investments are likely to result in the most cost-effective benefits for many organizations.

Here’s who we spoke to:

• Mike Donovan, V.P. of Product, Aquicore
• Adam Fingerman, CEO, ArcTouch
• Dave Schuman, Mobility Leader, Cloudera
• OJ Ngo, CTO and Co-founder, DH2i
• Nikita Ivanov, Founder and CTO, GridGain Systems
• Suzy Visvanathan, Director of Product Management, MapR
• Uri Sarid, CTO, MuleSoft
• David McCall, President, and Clarke Stevens, Chair, Data Model Tools Task Group and Vice Chair, Data Modeling Work Group, Open Connectivity Foundation
• Zach Supalla, Founder and CEO, Particle
• Stephen Blum, CTO, PubNub
• David Bericat, Global Technical Lead, Industrial IoT and Edge Computing, Red Hat
• Vaughn Shinall, Head of Product Outreach, Temboo
• Ray Wu, CEO, Wynd