Businesses can obtain major benefits — including better customer experiences and operational excellence — from the internet of things (IoT) by extracting insights from connected objects and delivering feature-rich connected products.
The mobile mind shift requires businesses to proactively support these IoT benefits for nonstationary connected objects that exist as part of IoT solutions. In particular, the IoT forces businesses to acquaint themselves with the implications of mobility in the IoT context for connectivity, security, compliance with privacy and other regulations, and data management for mobility. This means that:
Mobile technologies are central to most IoT solutions. To date, technology managers have mostly focused on enterprise mobility management (EMM) as part of their mobile activities. This narrow focus is insufficient for IoT solutions.
Mobile IoT is not a technology revolution but a fundamental business process transformation. Mobility requires managers not only to deploy mobile technologies but also to exploit them to support specific business process requirements.
Mobile technologies set the framework for IoT solutions. Mobile has distinct implications for aspects like broadband availability, data management, security, and local data compliance. Ignoring these will undermine your IoT initiatives and return on investment.
My new report, Mobilize The Internet Of Things, provides advice and insights for businesses on addressing these mobile challenges in the context of planning for and implementing IoT solutions.
Cybersecurity requires a specialized skillset and a lot of manual work. We depend on the knowledge of our security analysts to recognize and stop threats. To do their work, they need information. Some of that information can be found internally in device logs, network metadata or scan results. Analysts may also look outside the organization at threat intelligence feeds, security blogs, social media sites, threat reports and other resources for information.
This takes a lot of time.
Security analysts are expensive resources. In many organizations, they are overwhelmed with work. Alerts are triaged, so that only the most serious get worked. Many alerts don’t get worked at all. That means that some security incidents are never investigated, leaving gaps in threat detection.
This is not new information for security pros. They get reminded of this every time they read an industry news article, attend a security conference or listen to a vendor presentation. We know there are not enough trained security professionals available to fill the open positions.
Since the start of the Industrial Revolution, we have strived to find technical answers to our labor problems. Much manual labor was replaced with machines, making production faster and more efficient.
Advances in artificial intelligence and robotics are now making it possible for humans and machines to work side-by-side. This is happening now on factory floors all over the world. Now, it’s coming to a new production facility, the security operations center (SOC).
Today, IBM announced a new initiative to use their cognitive computing technology, Watson, for cybersecurity. Watson for Cyber Security promises to give security analysts a new resource for detecting, investigating and responding to security threats.
Modern application delivery leaders realize that their primary goal is to deliver value to the business and its customers faster. Most of the modern successful change frameworks, like Agile (in its various instantiations), Lean, and Lean Startup, which inspire developers and development shops, put metrics and measurement at the center of improvement and feedback loops. The objective of controlling and governing projects to meet vaguely estimated efforts but precisely defined budgets as well as unrealistic deadlines is no longer on the agenda of leading BT organizations.
The new objective of BT organizations is to connect more linearly the work that app dev teams do and the results they produce to deliver business outcomes. In this context, application development and delivery (AD&D) leaders need a new set of metrics that help them monitor and improve the value they deliver, based on feedback from business partners and customers.
Preproduction metrics. Leading organizations capture preproduction data on activities and milestones through productivity metrics, but they place a growing emphasis on the predictability of the continuous delivery pipeline, quality, and value.
What’s taken artificial intelligence (AI) so long? We invented AI capabilities like first-order logical reasoning, natural-language processing, speech/voice/vision recognition, neural networks, machine-learning algorithms, and expert systems more than 30 years ago, but aside from a few marginal applications in business systems, AI hasn’t made much of a difference. The business doesn’t understand how or why it could make a difference; it thinks we can program anything, which is almost true. But there’s one thing we fail at programming: our own brain — we simply don’t know how it works.
What’s changed now? While some AI research still tries to simulate our brain or certain regions of it — and is frankly unlikely to deliver concrete results anytime soon — most of it now leverages a less human, but more effective, approach revolving around machine learning and smart integration with other AI capabilities.
What is machine learning? Simply put, sophisticated software algorithms that learn to do something on their own by repeated training using big data. In fact, big data is what’s making the difference in machine learning, along with great improvements in many of the above AI disciplines (see the AI market overview that I coauthored with Mike Gualtieri and Michele Goetz on why AI is better and consumable today). As a result, AI is undergoing a renaissance, developing new “cognitive” capabilities to help in our daily lives.