Experfy Insights

Internet of Things, or IoT, is defined as the network of physical objects, or “things” embedded with electronics, software, sensors, and connectivity to enable objects to collect and exchange data. Many Organizations today show interest in and demand for applying business intelligence (BI) to IoT data, systems, and processes. R&D and Marketing & Sales departments assign the highest levels of IoT importance, as do larger manufacturing, financial services/insurance, and technology organizations. One of the most valuable insights is how critical the role IoT champions or IoT Advocates are to the successful adoption of IoT technologies today. 

Most of us know the value of customer management—the problem is that we’re using less-than-stellar tools or using them in a way that is less-than optimal. At the end of the day, customer management is about knowing what data to gather about your leads, keeping it up to date, and gaining insights from it in the fastest way possible. AI is a clear partner for CRMs and companies looking to build a more loving relationship with customer management and their customers both.

Can RPA be implemented without expensive and extensive modifications to existing systems? The answer lies in the evolution of RPA to what we know as ‘Connected RPA’, a wholly new approach that is transformational because it is quick to implement and doesn’t require any coding. In essence, Connected RPA brings forward a new generation of digital workers who can access and read the user interface of legacy systems to interoperate and orchestrate any third-party application.

Intelligent automation can help HR professionals make smarter decisions, help them get more done with less, and help HR shift its focus from manual, repetitive tasks to take on a more strategic role in the business – by innovating. Automation, learning systems, and artificial intelligence are all becoming key elements of HR practices and HR leaders and practitioners need to embrace these as ways to help your business achieve its goals. Intelligent automation can improve HR management in many ways.

AI has become so pervasive we often don’t even recognize it anymore. Besides enabling us to speak to our phones and get answers back, intelligent algorithms are often working in the background, providing things like predictive maintenance for machinery and automating basic software tasks. As the technology becomes more powerful, it’s also forcing us to ask some uncomfortable questions that were once more in the realm of science fiction or late-night dorm room discussions.

Businesses now require regular and sustained innovation, but this is problematic for many organizations more used to operating in safe mode. The situation is recoverable when leadership commits to building culture of innovation. This is especially important in IT, because it’s here where innovation now starts and ends. To this end, organizations are rushing to embrace Agile and DevOps – the practices of choice for rapidly delivering high quality software. But when it comes to culture nothing is easy.  Here are five tips to help ensure success.

Data breaches are becoming more frequent and damaging. This failure to solve the growing security crisis is crippling the confidence of large enterprises in their ambition to move to the cloud, which can be a risky, but necessary venture. Why is it necessary? The legacy implications of not moving to the cloud are affecting data. A data-centric approach to security and homomorphic encryption is required to solve this problem and give companies the confidence to move to the cloud.

An important step while creating our machine learning pipeline is evaluating our different models against each other. A bad choice of an evaluation metric could wreak havoc to your whole system. So, always be watchful of what you are predicting and how the choice of evaluation metric might affect/alter your final predictions. Also, the choice of an evaluation metric should be well aligned with the business objective and hence it is a bit subjective. And you can come up with your own evaluation metric as well.

Machine Learning on Code (MLonCode) is a new interdisciplinary field of research related to Natural Language Processing, Programming Language Structure, and Social and History analysis such contributions graphs and commit time series. MLonCode aims to learn from large scale source code datasets in order to automatically perform software engineering tasks such as assisted code reviews, code deduplication, software expertise assessment, etc. Some MLonCode problems require zero error rate, such as those related to code generation. A tiny, single misprediction may lead to the whole program's compilation failure.

Blockchain technology can serve as the digital foundation for the smart cities of the future helping governments raise their attractiveness and amplify their competitiveness in the new digital economy where human capital and industry are vital to success. Governments that embrace blockchain and distributed ledger technologies to reform civil services will be rewarded with a robust and agile digital infrastructure built for the hyper-connected and digitally-based economy that enables the cultivation of productive ecosystems, better public services, lower costs, and improves sustainable outcomes for all.

AI systems and algorithms are created by people with their own experiences, backgrounds and blind spots which can unfortunately lead to the development of fundamentally biased systems. If we allow this incredible technology to continue to advance but fail to address questions around biases, our society will undoubtedly face a variety of serious moral, legal, practical and social consequences. It’s important we act now to mitigate the spread of biased or inaccurate technologies. Then what can be done?

Whether you’re a tech enthusiast or an industry leader looking to innovate, it’s impossible to miss the growing role of AI, machine learning, and deep learning. You probably know, for instance, that the data generated by the Internet of Things (IoT) is somehow connected to artificial intelligence, and that AI can work on that data to provide answers and insights. But we’ll bet that the concepts get pretty blurry at that point. AI represents a decisive break with the past, and it’s something everyone needs to understand.

The augmented reality (AR) will become the key interface between humans and machines that’ll help bridge the gap between the digital and physical worlds. AR comprises a set of technologies that superimposes computer generated digital data, images and animation on real world objects.  The technology is still in its early stages. Today, most AR applications are focused on entertainment and delivered through smartphone and tablet apps. But, they’re being increasingly applied to commercial and industrial applications. 

The Platform business model is clearly today’s economic winner. Rather than seeking to control the means of production, platform companies focus on the means of connection,  connecting and facilitating the interactions between buyers and sellers, suppliers and consumers, or even just friends and families. And they generate revenue by collecting a “toll” from each interaction on their platform. Platforms gather data and use to feed artificial intelligence that helps manage the platform, an essential step due to the scale of due to scale of their organization.

Till now quality management was trying to justify investments in testing via the ROI metric, it is now time for a change. When implementing continuous testing, and running test automation cycles multiple times a day, in different environment, but different personas, ROI becomes an obsolete term since the measures are much different than before. The key term for the next 5-10 years when trying to measure and justify investment in testing should be VALUE.

It is important to understand DevOps is not an island. Enterprises implementing DevOps should be aware that DevOps interoperates with other IT systems and practices. Enterprises are well-advised to put in place governance policies that encourage the selection of tool-agnostic IT partners with solutions that best suit the needs of each unique enterprise and can integrate and evolve DevOps together with all their IT systems. One of the essential best practices areas that make up successful DevOps is continuous testing (CT).

It’s essential that the technology skills development program be aligned in purpose and design with the intended business objective. That may sound obvious, but organizations have a way of accelerating beyond the original scope and intentions. Successfully navigating digital transformation requires a team with well-aligned IT roles and skill sets. People are the make-or-break element of a high-performing IT organization. There is simply no replacement for people with the right skills, attitudes and traits.

A poorly handled Docker integration can lead to a number of issues that require extensive troubleshooting. It is important to make the integration is handled properly to minimize the risk of other problems down the road. Steps that you take during the Docker integration process can make a big difference in avoiding future bugs. You should follow these tips to avoid the need to troubleshoot time-consuming bugs after your Docker registries are up and running.

Blockchain technology encourages the building of ecosystems with many participants, who share similar interests but don’t trust each other because of the competition in the market. The shared blockchain ecosystem enables the cooperation of all parties and therefore leads to increasing efficiencies. With focus on the prolonging value chain of connected cars, a radical change is to be expected due to new emerging information technologies such as blockchain. This article provides a proposal for the evaluation and categorization of blockchain use cases, as well as a brief overview over promising use cases in the mobility sector and their challenges.

Organizations going through digital transformation need to put their business users first, unfiltered by an Organizational change management (OCM) team and processes to ensure they are moving ahead to improve their operations. This article isn’t about throwing OCM out; rather, it’s about putting your business users on the front lines of digital transformation and reducing the bureaucracy that comes with that transformation. When you involve your business user community in your change efforts at a deeper level, you have additional insurance that your efforts are on target.

With the increasing adoption and business applications of AI and ML, many IT professionals are choosing AI and ML as their career. As an aspiring AI professional, you should ensure that you are always aware of any current and upcoming advancements in artificial intelligence. However, considering the competition to enter the domain, it may be difficult to stand out in a pool of applicants. This article discusses the skills and certifications you need to get hired as an AI developer, machine learning engineer, or a research scientist.

Despite the rapidly evolving market of AI hardware, of the three key parts of hardware infrastructure - computing, storage, and networking -  it is computing that has been the focus and has made significant progress in the last couple of years. The industry is going with the fastest available option and promoting that as a solution for deep learning. The other two areas, storage and networking, are still to be seen in the near future.

The global IoT market is estimated to grow to over 212 billion U.S.D by 2019 end. While the consumer based adoption of IoT is well talked about, the future will see IoT being used for not just personal or consumer based use but also industrial use. As of now, the future of IoT lies in worldwide adoption.  In this article, we are going to look into the IoT trends 2020 and beyond, which would act as a torchbearer for businesses looking for an area to expand into. 

A growing number of cities are investing in smart technology. The innovation is enabling municipalities to finally do something about mitigating the damage that modern living inflicts on the environment and the population. Data analytics can potentially offer insights into nearly every aspect of public service and municipal activities. They already play a vital role in enabling cities, utility agencies and other municipal entities to optimize resources and move closer to zero-carbon objectives.