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.
Are you new to building Machine Learning Product initiatives and struggles to find where to start, and looking for tips and guides on how you can iterate and learn quickly from your customers when it comes to ML products. Here is kick-starter materials online which you can use and follow
Artificial intelligence is such a vast, nascent, complex area of computer science that even the world’s richest companies are struggling to recruit at the required pace. Within this article, we will first define what the role of AI scientist entails, before delving into the causes of — and potential solutions to — the global AI talent shortage.
Many organizations are still thinking of IoT as simple consumer toys that do not impact them, but that is not the case. Early on I circled my wagons around explaining IoT from a security perspective and describing how to better examine and address the security of an IoT products ecosystem, no matter what we determined “IoT” to be. The main focus of this concept is that the security of any part of an IoT ecosystem can, and will, affect the security of all other parts of that ecosystem.
The promise of IoT remains unchanged, from subtle efficiencies, to disrupting industries, to global economic impact, but it remains early. The technology and know how will improve and every company will need to determine how and when to engage.
Regardless of where you stand on the matter of Data Science sexiness, it’s simply impossible to ignore the continuing importance of data, and our ability to analyze, organize, and contextualize it. The role is here to stay, but unquestionably, the specifics of what a Data Scientist does will evolve. With technologies like Machine Learning becoming ever-more common place, and emerging fields like Deep Learning gaining significant traction amongst researchers and engineers, Data Scientists continue to ride the crest of an incredible wave of innovation and technological progress.
Two big trends in the customer interaction and engagement space make Robotic Automation very relevant. First, the imperative to better enable agents. In the past years, customer service departments have invested in self-service. The second trend is to improve the customer experience. It entails streamlining and digitizing customer-facing processes.
There’s a lot of talk about the applicability of artificial intelligence (AI) and deep learning to taming the vast quantities of data that modern Operations teams and their tools deal with. Analyst reports frequently tout AI capabilities, no matter how minor, as a strength of a product, and the lack of them as a weakness.
The intent of this blog was to provide some clarification of how machine learning can be readily applied to solve real world problems. This simplistic Value prediction function was just one example where machine learning can be used to avoid building a potentially rigid and complex manual model and ultimately save time and effort.
There is no one, single killer app for IoT. Instead, any company can create the right killer app that solves the need of their specific customer, in their specific industry, and vertical. In this post, I shared a handful of examples, but we are seeing applications in all industries, from Healthcare, to Energy, to Smart Agriculture, Smart Buildings, Transportation, etc. Companies in all industries are looking to adopt IoT as the catalyst to improve their existing solutions.
New technologies and new processes are at the heart of any Internet of Things (IoT) deployment. They lead to new business models, new organizational structures, and—inevitably—new work roles. But rarely does this transformation happen as quickly and completely
Bitcoin transactions are irreversible. Once initiated, there’s no way to retract. Bitcoin transactions bear no costs, and can be done with absolute anonymity. Gradually, it’s becoming as regular as other monetary values in the market. However, with no government backing, Bitcoin holds relevance as long as online merchants accept it.
The booming growth of machine learning and artificial intelligence (AI), like most transformational technologies, is both exciting and scary. It’s exciting to consider all the ways our lives may improve, from managing our calendars to making medical diagnoses, but it’s scary to consider the social and personal implications.
Spark can handle pretty much any data store you throw at it and data scientists can use a common way of thinking about data (SQL) for handling it. You don’t have to use the SQL-like interface, but it’s there, and many take advantage of it. Don’t care for the SQL/HQL aproach? That’s fine, you can use Spark like many use bash for data wrangling. Spark spans many skill levels.
Your business needs should dictate what your IoT platform is. Not vendor definitions. They come in all shapes and sizes. Hoards of them. They are called IoT Platforms. They are hard to differentiate. They combine two words that we wax eloquent trying to describe. IoT and Platform.
The complexity of building out an IoT solution from scratch can be daunting, indeed. Devices must be designed and built, or suitable COTS devices identified, networks created, security — both physical and cyber — put into place, databases and software architected, connectivity purchased. That kind of a complex build-out is costly.
Today, there is a new gold rush, sparked by the Internet of Things (IoT). The news is filled with stories of self driving cars, smart solutions, and smart cities. Everyone has a disruptive idea that is going to change the world. Thousands of companies, new and established, are planning “smart” solutions. Marketing, hype and confusion are one and the same. And we’re just getting started.
In the burgeoning IoT space, a number of company’s are creating and acquiring their way to a full stack offering – lets call them the ‘Full Stacks.’ The other end of the spectrum represents best of breed integrations that we’ll call the ‘Custom Stacks.’ Clients, such as OEM’s, seeking to develop an IoT solution are faced with a strategic decision of either buying a ‘Full Stack’ or assembling a ‘Custom Stack.’ Both approaches, and in between, require integration, a separate topic, but are viable options and all come with pros and cons.