Today’s IoT technologies are still immature point solutions that address emerging use cases with evolving technology standards. Buyers are concerned that what they buy today may become functionally or technologically obsolete tomorrow. Faced with this dilemma, many defer buying even if the IoT solutions they buy today offer tremendous value to their organizations. This post discusses a planning strategy called “future-proofing” that helps managers, buyers, and planners deal with obsolescence.
Look, you could say AI is already teaching us. You ask questions to search engines all day, and get answers in return. If you train them right, algorithms can feed you valuable knowledge. However, you cannot ask it WHY it gives you those answers and articles. Good teaching is an open two way process.
Tooling is probably the least exciting topic in data science at the moment. People seem to be more interested in speaking about the latest chatbot technology or deep learning framework. This just does not make sense. Why would you not dedicate enough time to pick your tools carefully?
Suddenly, everyone has an “innovative” IoT platform, “smart” connected devices, machine learning and “disruptive” pricing models. But don’t be fooled by the hype. While IoT may be built with innovative technologies, the real IoT innovation is what they allow organizations to become — intelligent, agile, and adaptive.
Robotic process automation (RPA) seems to promise the Holy Grail to operations. Lower cost, fewer errors, better compliance with procedures – the benefits seem real and achievable to COOs and operations leaders. The fact that RPA tools promise to pay for themselves from the operational savings (with short payback periods) makes the business case even more attractive.
Driving sales takes a lot of hard work. It’s a very time-consuming process, where you have to research potential clients, pitch to prospects, and keep following up with them before you close a deal. Anything that can automate this process will certainly help salespeople close more deals and grow their business faster. The recent developments in artificial intelligence (AI), coupled with the availability of big data and deep learning, have given rise to a few interesting ways that can make it easier to grow your sales.
Hospitals are starting to see gains from predictive analytics, especially on the operational side. OR block scheduling is an area where the ROI can be significant. Now, with the explosion of smart devices, computational power in the cloud, and the growing pervasiveness of data science and machine learning algorithms, an entirely different realm of operational optimization is suddenly possible.
Some benefits of Bitcoin and other currencies based on distributed ledgers (the concept behind blockchain tech) include cheaper global transactions; low risk of money laundering; and growing market demand. The major risks that are often cited about cryptocurrency are that it is very volatile; prone to scammers and hackers, and could be a bubble.
Effective digital architects build their technology capabilities into their strategic plans and roadmaps where it is continuously reviewed and updated. They invest in AV, OT and IoT and develop them so that it becomes a sustainable competitive differentiator for the enterprise.
Today, machine learning networks require the scale of computing resources found only in large data centres, but the scale of the IoT will require that these capabilities are pushed out from the cloud to the edge, and into mobile devices themselves. This will require challenging advances in energy-efficient computing, the area of business where Arm is world-leading. IoT devices will make a great deal of information available, with associated issues of data protection and cybersecurity. Where the potential exists to use the internet to disrupt the control of critical equipment, such as an autonomous vehicle, security matters must be paramount.
A lot has been said about the negative connotations of artificial intelligence but the hard evidence continues to tell us something different. For starters, the National Association of Software and Services Companies has stated that robotic process automation (RPA) can already reduce operational costs by as much as 65% - with ROI within as little as half a year. With ever more sophisticated decisioning as part of this, AI is becoming an incredible asset to the human workforce and is increasingly becoming an inevitable addition to the business process. In the area of customer service, in particular, artificial intelligence is already proving its worth in ways that cannot be ignored or played down.
Most healthcare providers are waking up to the fact that their operations need a data-driven, scientific overhaul much the same way as auto manufacturing, semiconductor manufacturing and all other asset-intensive, “flow”-based systems have experienced. The good news is that there are tools, software and resources that can be used to bring about this transformation.
Are you ready for the Internet of Things (IoT)? Despite its transformational potential, most organizations are not. In an era of rapid disruption and digital transformation, IT executives and managers must lead the charge. You must bridge the gap between technology, business, engineering and operations. You must be evangelists, teachers, facilitators and innovators. To succeed, I’ve listed six things you must do to accelerate IoT adoption within your organization.
Although the GSMA describes eSIM as being a vital enabler of the growth of the machine-to-machine (M2M) ecosystem, the applications of this nascent technology are in fact even wider-reaching than pure M2M applications and that not only opens up broad opportunities for IoT/M2M companies to define and own their category, it also creates a literal, built-in differentiator that will set them apart from competitive solutions and devices that are not eSIM-enabled.
In recent years, as the sultans of Silicon Valley have pressed both computation speeds and data storage capacities to dizzying heights, researchers and analysts working at the intersection of statistics and computer science have leveraged new tools to chase increasingly sophisticated modeling techniques. This dramatic expansion in both software tools and, especially, the quantity and quality of data available led to the emergence of data science as a discipline, and most important the assets created by a data science teams: predictive analytic models.
IoT protocols aren’t limited to internet protocols or the ones that IT domain created. A plethora of standards exist in OT and are tailored for specialized OT applications from production floor control to subsystem connectivity in cars. OT and IT protocols evolved separately with some borrowing from each other.
The number of network-connected and intercommunicating devices is expected to climb well into the tens of billions within a few years. As the world grows ever more connected, how can we trust these devices? Such a network of intercommunicating objects requires a secure and efficient way to track all interactions, transactions, and activities of every “thing” in the network. Interoperability, security, compliance, privacy, and reliability are all barriers to IoT growth, but the paramount challenge for ubiquitous connectivity, never mind device autonomy, is a lack of trust.
With all the articles being published about robots taking over repetitive tasks and transforming the operations in businesses across sectors I find it hard believe that any IT leader is unaware of the robotic process automation (RPA) technology, yet it seems to be the case.
Many of you have likely experienced the same situation that I have encountered where you have received a report and thought “what am I looking at?”, “whats the point?”. The pack is full of tables and it's difficult to see the wood for the trees! How can you, as the data expert, show your business partners that you have a clear and comprehensive understanding of their business, without burying them in data?
For years, we have been hearing about the virtues of IoT and specifically how Industrial IoT (IIoT) is the cure for inefficient machine maintenance and unplanned downtime. While all that is true if zero-downtime ("ZDT") was such a pain, why isn't everyone rushing to get connected and drive maintenance costs down and uptime up?
When evaluating innovative ideas, it is crucial to gain a good understanding of the value drivers. The value propositions for target customer groups and key partners must be clear and compelling. Given the complexity of connected solutions and the associated ecosystem, this is easier said than done. However, there are several tools that can support this process and help reduce the risks and complexity of IoT solutions. Below we present a selection of our favorites, which evolved from the IoT Business Model Builder.
IoT may be the next big thing in enterprise technology, but the various types of connected devices that seem useful for a business have the potential to cause more harm than they help. If these risks are controlled by some of the best practices, IoT can be engaged with in a healthier and safer fashion. If not, IoT has the potential to evolve into a minefield for cyber-risk in the enterprise.
Improving hospital operational efficiency through data science boils down to applying predictive analytics to improve planning and execution of key care-delivery processes, chief among them resource utilization (including infusion chairs, operating rooms, imaging equipment, and inpatient beds), staff schedules, and patient admittance and discharge. When this is done right, providers see an increase in patient access (accommodation of more patients, sooner) and revenue, lower cost, increased asset utilization, and an improved patient experience.
The field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix’s algorithms to make movie suggestions based on movies you have watched in the past or Amazon’s algorithms that recommend books based on books you have bought before.