The best way to bring groups together and bridge the cultural divide is to identify a common benefit for the company, Streenstrup said. Potential benefits include reducing technology costs (number of software licenses, support staff requirements) reducing risk (exposure to cybersecurity attacks), agility and speed (moving more quickly than competitors) and unlocking the benefits of equipment data (“the big holy grail”). “If you can unlock the value in that equipment data, safely and reliably, now you get much better visibility into the plant or the machinery is doing,” Streenstrup said. “The two highlights there are operational efficiency, how much material energy or materials do you put in to get a benefit and also reliability – how do we know what the machine is doing so we can get in there before it fails?”
IT faces challenges in monitoring admin accounts as well: 57% of professionals said they only monitored some privileged accounts, or did not monitor privileged access at all. And 21% said they are unable to monitor or record activity performed with admin credentials at all. Gaining access to privileged accounts is the easiest way for cybercriminals to steal an organization's critical data and systems, One Identity noted in the report. "By not adhering to these best practices, privileged accounts are vulnerable to open the door to data exfiltration or worse, if compromised," according to a press release. "When an organization doesn't implement the very basic processes for security and management around privileged accounts, they are exposing themselves to significant risk," John Milburn, president and general manager of One Identity, said in a press release.
For businesses, chatbots have the potential to be an incredibly financially-efficient solution. Take a use case that is commonly explored today: customer service. At their best, a chatbot can solve a customer’s query on its own, reducing the human resource required by a brand and satisfying the customer’s need – which we know is the best way to have customers coming back for more. However, the mysticism and confusion around chatbots means that, too often, businesses that are building and deploying AI and chatbot solutions focus on the wrong thing – trying to make their chatbots good at chat. This is not to say a natural conversation flow isn’t important to a customer experience – it is. However, brands should remember their bots are not going to be used in the same way Siri and Alexa are – they are there to fulfil a business function, not answer questions on the weather in Barcelona tomorrow.
The power of Blockchain can essentially eliminate the “middle man” in financial transactions like loans, wire transfers, and other services that require often exorbitant transaction service fees. And I don’t just mean removing the need for bank tellers. I’m talking about the ability to turn all currency digital so that it no longer needs to stored or secured at all. Even beyond Bitcoin, Blockchain could be used to develop local currency or internationally accepted money — depending on the needs of the industry or user. Although you’d think Big Business in the form of the country’s major financial institutions would be pushing hard against Blockchain because of its potential to put them out of business, you’d be wrong. Research shows 65% of banks plan to implement some form of Blockchain in the next few years. That’s how powerful this technology has become.
For developers to take responsibility for the systems they create, they need support from operations to understand how to build reliable software that can be continuous deployed to an unreliable platform that scales horizontally. They need to be able to self-service environments and deployments. They need to understand how to write testable, maintainable code. They need to know how to do packaging, deployment, and post-deployment support. Somebody needs to support the developers in this, and if you want to call the people who do that the "DevOps team", then I'm OK with that. ... Dedicated DevOps teams are often made up of experienced operations people with a mix of skills including using version control, writing infrastructure as code, and continuous delivery. These teams typically start by addressing the things that are most painful, such as deployment automation, and if they're successful, can evolve to providing shared services for the rest of the organization.
"Data is the feedstock of AI, especially unstructured data, giving insights into customer intent, employee behavior. However, as consumers realise quite how much data is being collected on them to fuel these models and algorithms, there will be pushback as more stringent privacy controls are demanded. "There is the danger of bias being baked into machine learning applications at any stage, be it the data, the training of models and or the programming of algorithms. Developers and owners of those applications need to guard against this but also make the applications sufficiently transparent so biases can be detected and fixed at whatever stage they occur." ... Jane Zavalishina, CEO of Yandex Data Factory, argues that firms will likely struggle to integrate AI systems into existing business operations and humans will still be more capable in other areas, such as common sense and compassion.
"At the same time as these technology developments are happening companies are also globalising, innovating their business models while having to deal with different regulatory regimes around the world. "Enterprise architects are therefore required to ensure that IT landscapes are optimised: cost-effective, open and collaborative yet secure and private, scalable and flexible," Carpenter explains. Roland Woldt, director of KPMG's Enterprise Architecture Practice, says EA has evolved substantially from its early days when it was seen strictly as a technical way to wire up an organisation's infrastructure. Today's EA is more focused on business outcomes – what KMPG calls "capability-centric architecture": the capabilities needed to make digital transformation happen. According to Woldt, with its many moving parts and a myriad of direct and indirect relationships with partners, customers and vendors, EA has become incredibly complex.
"It may not be true that you can solve it with machine learning," Wilder-James said. "This is one important difference from other technical rollouts. You don't know if you'll be successful or not. You have to enter into this on the pilot, proof-of-concept ladder." The most time-consuming step in deploying a machine learning model is feature engineering, or finding features in the data that will help the algorithms self-tune. Deep learning models skip the tedious feature engineering step and go right to the training step. To tune a deep learning model correctly requires immense data sets, graphic processing units or tensor processing units, and time. Wilder-James said it could take weeks and even months to train a deep learning model.
According to 451 Research analyst Carl Brooks, for a technology solution to qualify as "as a Service," it has to meet the National Institute of Standards and Technology (NIST) definition parameters, which he paraphrased as "self-service, paid on-demand, elastic, scalable, programmatically accessible (APIs), and available over the network." In a general sense, the cloud is divided into three distinct layers: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). The fundamental model of cloud computing that underpins all three of these layers is a service rental model, according to Forrester Research principal analyst Dave Bartoletti. "You are renting infrastructure, or you are renting development platforms and tools, or you are renting software. That's IaaS, PaaS, and SaaS," Bartoletti said.
As threats have become increasingly dynamic and automated, DDoS detection and mitigation solutions are rising to the challenge with their own increase in automation and adaptability. According to Radware’s Cyber-Security Perceptions and Realities: A View from the C-Suite report, 38% of IT executives throughout the United States and Europe indicate that automated security systems – such as machine learning and AI – will be the primary resource for maintaining cyber security within the next two years. But it presents a catch-22 for the next-generation security professional. As a security professional, when you’re increasingly relying on automation to defend the network, you’re not “practicing” or fine tuning your skill sets. The DDoS mitigation solution is doing a lot of the heavy lifting and the network security professional is receiving and digesting reports. This can create a void in skill sets due to lack of “practice.”
Quote for the day:
"Hiding from yourself is the surest path to self hatred, self pity and a whole lot of missed potential." -- Jon Westernberg