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.
You can't turn anywhere without bumping into artificial intelligence, machine learning, or cognitive computing jumping out at you. Our cars brake for us, park for us, and some are even driving us. Our movie lists are filled with Ex Machina, Her, and Lucy. The news tells about the latest vendor and cool use of technology, minute by minute. Vendors are filling our voicemail and email with enticements. It's all so very cool!
But cool doesn't build a business. Results do.
Which brings me to the biggest barrier companies have in adopting artificial intelligence. Companies are asking the wrong questions:
What is artificial intelligence (or insert: machine learning or cognitive computing)?
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.
You can't bring up semantics without someone inserting an apology for the geekiness of the discussion. If you're a data person like me, geek away! But for everyone else, it's a topic best left alone. Well, like every geek, the semantic geeks now have their day — and may just rule the data world.
It begins with a seemingly innocent set of questions:
"Is there a better way to master my data?"
"Is there a better way to understand the data I have?"
"Is there a better way to bring data and content together?"
"Is there a better way to personalize data and insight to be relevant?"
Semantics discussions today are born out of the data chaos that our traditional data management and governance capabilities are struggling under. They're born out of the fact that even with the best big data technology and analytics being adopted, business stakeholder satisfaction with analytics has decreased by 21% from 2014 to 2015, according to Forrester's Global Business Technographics® Data And Analytics Survey, 2015. Innovative data architects and vendors realize that semantics is the key to bringing context and meaning to our information so we can extract those much-needed business insights, at scale, and more importantly, personalized.
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.
It looks like the beginning of a new technology hype for artificial intelligence (AI). The media has started flooding the news with product announcements, acquisitions, and investments. The story is how AI is capturing the attention of tech firm and investor giants such as Google, Microsoft, IBM. Add to that the release of the movie ‘Her’, about a man falling for his virtual assistant modeled after Apple’s Siri (think they got the idea from Big Bang Theory when Raj falls in love with Siri), and you know we have begun the journey of geek-dom going mainstream and cool. The buzz words are great too: cognitive computing, deep learning, AI2.
For those who started their careers in AI and left in disillusionment (Andrew Ng confessed to this, yet jumped back in) or data scientists today, the consensus is often that artificial intelligence is just a new fancy marketing term for good old predictive analytics. They point to the reality of Apple’s Siri to listen and respond to requests as adequate but more often frustrating. Or, IBM Watson’s win on Jeopardy as data loading and brute force programming. Their perspective, real value is the pragmatic logic of the predictive analytics we have.
But, is this fair? No.
First, let’s set aside what you heard about financial puts and takes. Don’t try to decipher the geek speak of what new AI is compared to old AI. Let’s talk about what is on the horizon that will impact your business.
New AI breaks the current rule that machines must be better than humans: they must be smarter, faster analysts, or they manufacturing things better and cheaper.