They say necessity is the mother of invention, and the accelerating pace of the mobile revolution is no exception. The proliferation of mobile devices affected countless industries, creating a powerful need for businesses to manage those devices. And so, mobile device management (MDM) was born. While these solutions have evolved considerably over the years to keep pace with new technologies and innovations, their core function remains enabling IT personnel to remotely manage, track, troubleshoot and secure devices – mostly smartphones and tablets these days.It’s easy to see the parallels between the factors driving the development and adoption of MDM – namely device control and security – and those that brought about IoT device management. Similarities in concepts and terminologies further cloud the distinction between the two. But, as any company that has attempted to repurpose an MDM solution for the management of IoT devices can attest, the resemblance is only superficial.To read this article in full, please click here
Being hired as the resident IoT data scientist can come with a lot of pressure. Oftentimes the only one on the team with the unique ability to turn data into business intelligence, data scientists are responsible for making key IoT decisions, setting plans, ensuring execution and meeting deliverables. On top of this, there can be a number of stumbling blocks out of the gate that make it hard to reach goals. Being aware of these challenges not only helps put a data scientist on the shortest route to success, it makes it easier to identify where and when more help will be needed.Here are some of the most challenging requirements data scientists face when starting an IoT project:To read this article in full, please click here
Data scientists are an essential part of an IoT deployment. They fill a critical need to interpret data and provide valuable context around machine learning. However, as IoT initiatives expand and mature in a business, in-house data science resources can become thinly stretched. This creates a data pile-up that is a surefire way to set your deployment back.Hiring more data scientists is typically not an option either as there is a significant shortage in the market. Demand is only going up too: Gartner predicts that a shortage of data scientists will hinder 75% of organizations from reaching their full potential with IoT through 2020. Because hiring is difficult, time consuming and expensive, many organizations are turning to data science services to fill in resource gaps. Outsourcing data scientists has the dual benefit of helping keep IoT initiatives moving forward while freeing up internal resources to focus on other areas of the business.To read this article in full, please click here
Across a variety of industries, corporate IT and operations teams are rapidly deploying IoT to meet core business objectives. The aim of these deployments can vary greatly, from monitoring device health, to reducing operating costs, and increasing production volume. Yet there are a number of other areas throughout an organization, with initiatives of equal importance, where stakeholders have yet to leverage the value of connected device data to achieve their goals. One such example is the C-level. While generally not designed with executives in mind, IoT technology can provide value to the C-level that’s on par with the advantages their IT and operations counterparts stand to gain.To read this article in full, please click here
Businesses of all kinds are embarking on IoT initiatives. And no matter the business type or scale of project, stakeholders always have one burning question: “How soon until we start seeing some ROI?” For businesses that have a concrete plan in place for measuring ROI, the answer is typically within the first year. That’s because companies that take the time to make these plans have the clearest objectives, which make them easier to attain and in a faster amount of time. The ROI planning process is essentially the same for a company just starting out and for one that is having trouble finding profitability from IoT investments and needs to revisit their approach.To read this article in full, please click here
Adopting IoT technology is a significant, company-wide undertaking. It requires a large dedication of resources and budget across multiple departments. Rightfully, the C-level has expectations for their investments. But before you can get to the payoff, there is an inordinate amount of decisions to be made and changes to endure. Not the least of which will be to learn new technology, establish new processes and define new job descriptions.But, because the result is so valuable to an organization’s bottom line – for example, greater yields of manufactured goods, extended life of decade’s old equipment, and more – tech leaders often compare the adoption of IoT technology to the early days of computers in business. To read this article in full, please click here
Organizations that are leveraging IoT to drive better business outcomes are increasingly using digital twin technology. In fact, Gartner predicts half of large industrial companies will be using them by 2021.A digital representation of a physical object, digital twins allow businesses to create a crystal-ball-like-view into the future. They enable simulation, analysis and control to test and explore scenarios in a practice setting before initiating changes in the real world.While digital twins have historically been associated with more complex technology environments, its impressive ability to both eliminate problems and deliver next-level operational performance is making these models a must-have technology in every IoT team’s toolkit.To read this article in full, please click here
When most people think about the adoption of the IoT, they think about smart cities, autonomous vehicles, or the latest consumer tech and wearables. However, some of the most amazing technology applications are taking place within industrial verticals such as manufacturing, oil and gas (O&G), and transportation. Unfortunately, when asked about the state of IoT adoption within these markets, we’re often left relying on basic information about connected endpoints, instead of truly understanding how businesses are progressing through IoT maturity within the industrial field. To help answer these questions (and get a bit more in the weeds on the topic) my company, Bsquare, recently conducted its first Annual Industrial IoT (IIoT) Maturity Study. We polled 300 respondents at companies with annual revenues in excess of $250 million. Participants were evenly divided among three industry groups (manufacturing, transportation and O&G) and titles covered a wide spectrum of senior-level personnel with operational responsibilities, most of whom had spent an average of six years in their organizations.To read this article in full, please click here
Dave McCarthy