The Evolving Role of IoT in the AI Revolution

Operational use cases in B2B contexts lead consumer applications in IoT spending and deployment, and they have been around for years. So what's the next big thing in the IoT space?

In many ways, it’s not surprising that the industrial IoT (IIoT) revolution commanded the lead in deploying IoT innovation and yielding returns from that investment, even if consumer IoT markets garner more media attention. The IoT extends the traditional operational benefits of telematics, enabling companies to use wireless technology and the internet to connect a far broader spectrum of “things.”

Yet it takes more than a huge investment in IoT devices to make the initiative a success. To succeed and remain a step ahead of traditional competitors as well as IoT pure-players seeking entry into the market (especially from information systems pure players), enterprises need to cultivate data as a core competency. 

This is in contrast to the strategy enterprises often employ with data, which is to keep it siloed on certain teams or with certain people instead of democratizing it across teams. So often the challenge is incorporating IoT data strategy into the data strategy as a whole. And indeed, developing data as a core competency means:

 

  1. Organizational change from the ground up, leveraging a combination of technology, people, and processes.
  2. Developing expertise in gathering, cleansing, integrating, and analyzing huge amounts of data. This must take place not only with data scientists, but by leveraging and upleveling the skills of data analysts and those in the business sector as well.
  3. Understanding the importance of not just IoT data alone, but the power of combining sensor data with other sources like enterprise data (e.g., sales, CRM), third-party data (e.g., behavioral, demographic), and open public data (e.g., Census, weather), etc. 

 

What’s Next for IoT

Edge analytics and edge AI: Extreme volumes of IoT data raise several obstacles, including high cost of moving and storing data, difficulty in detecting anomalies and creating visualization from enormous data sets, and in using conventional training sets sufficiently large to provide reliable models on extreme sets. Edge AI means that machine learning algorithms are processed locally on a hardware device, and it’s quickly becoming the hottest topic in the IoT space.

IoT security and governance as part of a larger AI governance strategy: Poor security and governance practices only get worse and widen the potential for disaster, as the volume of data increases. Having a stable platform from which data teams work that keeps track of what data is being used where (and how) as well as making sure individual data isn’t being stored on local machines is vital to a successful security and governance strategy, especially in the context of IoT.

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What Dataiku Brings to the Picture

Dataiku is the platform democratizing access to data and enabling enterprises to build their own path to AI. By making AI accessible to a wider population within the enterprise, facilitating and accelerating the design of machine learning models, and by providing a centralized, controlled, and governable environment, Dataiku allows businesses leveraging IoT as well as other more traditional data sources to massively scale AI efforts.

Data scientists as well as analysts or business users across transportation, the public sector, manufacturing, pharmaceuticals, and more use Dataiku to power self-service analytics while also ensuring the operationalization of machine learning models in production. 

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