IoT & Automation

Nearly half of cloud services in the enterprise are outside corporate IT’s domain, while around 47 percent of corporate data stored in cloud environments are not managed by the IT department. Cloud computing is attractive to enterprises for cost efficiency as well as its flexibility in allowing employees and customers 24/7 access to information and services. However, the security challenges can be significant. IT managers are often uncertain of which measures are meant to secure what data. Effective security for cloud data demands a holistic approach and recognizes that not all data is vital.

Today, all organizations are digital by default. Digital business inherently means utilizing new technology, connecting devices and operating platforms, embracing different ways of working, building large-scale data silos, and so on. The convergence of Internet of Things networks with what were once separate and self-contained — and therefore more manageable — systems represents a fundamental change. Coping with digital challenges and mitigating risks still represents a major burden for organizations across the board. The World Economic Forum now rates a large-scale cybersecurity breach as one of the five most serious risks facing the world today. 

Joining IoT and healthcare and leveraging connectivity to deliver care is not as easy as it seems. How can we ensure the successful marriage of IoT and healthcare? Let’s expand the notion of “pathways” to appreciate how IoT can be leveraged in healthcare delivery. The expansion includes the notion that data needs to flow across the players involved in care delivery and money needs to flow to compensate the right parties. We are on our way to professionally managed healthcare using IoT, not just consumer-grade wearables for the fitness-conscious.

Enterprises are moving cyber security and resiliency to the top of their priority lists (if they were not there already). This is especially true for enterprises building or managing IoT applications. Attacks which used IoT devices to launch distributed denial of service attacks (DDoS) are raising both consumer and business customers’ fears regarding the security of the IoT. When it comes to cybersecurity, IoT devices come with their own unique security challenges. Here are five key best practices enterprises can adopt to significantly lower the risk of a successful attack on their IoT infrastructure. 

Each year, Earth Day provides an opportunity for people around the world to consider how they can take action to protect our environment. For decades, it has encouraged people to undertake individual actions and advocate for policies. Digital technologies – in particular the IoT – can help us address the climate change challenge by accelerating the transition to a more sustainable, renewable-energy-powered economy. In particular, the IoT is a key enabling technology for an emerging concept called, “The Internet of Energy.” 

When IoT developers consider blockchain, they should first sit back and ask themselves the basic question we ask about any new technology – “what problems will it solve for us?” If the answer is “nothing we can think of” then it is probably safe to put blockchain on the back burner, at least for now. However, blockchain could hold some potential for the IoT, particularly in relation to applications involving peer-to-peer transactions. If there are technological innovations that make blockchain more effective or increase its benefits, it might become more relevant to the IoT – but that day is not here yet.

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

IoT can be used not only to improve existing business operations but also to create new offerings and new business models. Business models require thinking through the consumption side of your offer — how it is bought and used, and the production side of the offer — how it is created and delivered. We need to think of the offer from the consumer’s lens, i.e. buyer, user, and operator. It is time to go beyond predictive maintenance and reimagining our offerings with IoT. Time to flip some tiles!

VPN evolution has transitioned the technology from point-to-point connectors that facilitate remote access to one that’s based on sophisticated security multipoint connectivity. VPN evolution has taken place over the years, adapting to the networks that have been shaped by broadband connectivity, the cloud, and mobility, as well as the endpoint devices themselves. Reflecting back on the early days of VPNs and how far we have come, the evolution and the history of VPN technology can be broken down into four phases. Secure communication is one of the most important foundations for our future, and it is imperative to protect data in motion with VPN evolution.

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.

Data clustering is the classification of data objects into different groups (clusters) such that data objects in one group are similar together and dissimilar from another group. Many of the real world data clustering problems arising in data mining applications are pair-wise heterogeneous in nature. Clustering problems of these kinds have two data types that need to be clustered together. In an industrial setting, despite collecting data from tens of thousands of sensors, less than 1% is actually utilized. We can move rapidly into Industry 4.0 by combining subject matter expertise, data collection methods and next-generation data science tools, beyond many of the “me too” products.