As companies in a variety of industries plan and execute their digital transformation strategies, powered by the Internet of Things (IoT), they should be designing everything—products, applications, sensors, networks, services, etc.—with one all encompassing goal: to maximize the value of the data they create or ingest.
If your organization is planning to leverage the Internet of Things (IoT) to gather data from products and systems, see how goods are performing in the field, enhance factory production, or any other reason, it needs to become familiar with the concept of the “digital twin.” A digital twin is a digital replica of a physical asset, process, or system that can be used for a variety of purposes. The digital twin is intended to be an up-to-date and accurate replica of all elements of a physical object for which sensor data is available.
Most IoT ecosystem projects will involve multiple contributing application partners. They will also involve complex, evolving functional and non-functional requirements. To address these challenges and reduce complexity, IoT developers are now starting to embrace collaborative lifecycle management (CLM) technologies combined with the latest continuous engineering (CE) technologies. Organizations that want to ensure the success of complex IoT initiatives will need to mitigate the adverse potential of complexity-related development risk. Thankfully, these risks can be managed effectively using the latest CE/CLM tools to ensure that your IoT vision becomes a reality. Collaborative lifecycle management is a key to IoT success
Few industries will see as big an impact from the internet of things as the insurance sector. Indeed, IoT has the potential to touch nearly every facet of insurance, with the promises of both benefits and risks for carriers as well as their customers. IoT will impact how insurance underwriting and pricing are done for markets including transportation, home, life, healthcare, workers’ compensation and commercial. And it will transform the way insurers gather information about customers and their environments to process claims, determine risks and calculate costs.
Helping to fuel interest in data lakes are the digital transformation efforts underway at many enterprises, spurred by the emergence of the Internet of Things (IoT). The connected objects in the IoT will generate huge volumes of data. As more products, assets, vehicles and other “things” are instrumented and data ingested, it’s important that IoT data sets be aggregated in a single place, where they can be easily analyzed and correlated with other relevant data sets using big data processing capabilities. Doing so is critical to generating the most leverage and insight from IoT data.
As enterprises delve more deeply into IoT, there will be a growing need for an operational intelligence-oriented data analyst as many IoT use-cases demand near real-time operational insight. So you can expect to see a huge uptick in demand for people who have technology and business skills related to the Internet of Things (IoT), as organizations continue to ramp up their IoT projects in a big way. As enterprises delve more deeply into IoT, there will be a growing need for an operational intelligence-oriented data analyst who is also an “AI/ML data engineer.”
Although technology is quickly changing, your goals as a manufacturer likely haven’t. You still aim to please your customers by delivering quality products, while increasing productivity and profitability. Yet, new and unprecedented innovations will potentially impact all aspects of the execution of those goals at the operational level. Smarter connected devices that use open IoT protocols are rapidly penetrating factories. At the same time, the Industry 4.0 trend is showing how people, connected devices and artificial intelligence can work together to make factory automation more efficient and effective. To remain competitive, you must quickly adapt.
Industrial enterprises typically look to systems integrators to bridge the gaps with custom software development. A few IoT vendors are now beginning to build more fully-integrated IoT service creation and enrichment platforms (SCEPs), designed to support an AFML IIoT architecture. SCEPs allow complex IoT architectures, applications and orchestrations to be efficiently created and evolved with minimal programming and administrative effort. These next-generation IoT platforms will help companies eliminate IoT data exhaust and harness IIoT data for use as a strategic business asset.
Before launching an IoT initiative, organizations need to have a comprehensive strategy in place. Otherwise, there’s a risk of overspending, exposing data to security and privacy threats, limiting the payback from IoT technologies, as well as other negative outcomes. Without three key elements — strong leadership, a sensible business plan, and a commitment to culture change — all the sensor, networking and data analytics technology in the world is not going to deliver optimum results. Your IoT initiative will likely face many challenges before you can proclaim it a success. Here are some important considerations to keep in mind.
The current consumer IoT device landscape is still immature. For consumer IoT devices to thrive, device management capabilities need to evolve in a few ways. Effective device management is critical to establishing and maintaining the health, connectivity, and security of IoT devices. Effective device management is critical to establishing and maintaining the health, connectivity, and security of IoT devices. What consumer IoT needs is a truly open IoT device management ecosystem.