A part of our customer understanding comes from what we think is valuable to our customers, average deal size, repeat purchases, etc. But usually, these insights are narrow and not in real time. While it is one thing to capture data, ingest and get insights – the accuracy of data becomes more important. To create a long-term customer isn’t only about catering to their needs but also being able to pick up their buying signals, understand their behavioral patterns and more importantly, being able to use the insights to personalize their customer experience (CX).
One of the domains where the General Data Protection Regulation (GDPR)will leave its mark prominently is the artificial intelligence industry. Data is the bread and butter of contemporary AI, and under previously lax regulations, tech companies had been helping themselves to users’ data without fearing the consequences. That will change on May 25, when GDPR comes into effect. GDPR requires all companies that collect and handle user data in the European Union to be more transparent about their practices and more responsible for the security and privacy of their users.
Realizing the growing security risks in the legally complex and increasingly regulated global economy, software development outsourcing companies put a lot more emphasis on complying with industry regulations, policies, methodologies, and technologies used to protect data. They conclude well-thought-out service-level agreements (SLAs) with their clients and look for more efficient solutions for responding to potential vulnerabilities in the development process and tackling the security challenges. We understand the importance of information security when working with international clients. Therefore, we’d like to share our knowledge and experience in the most effective information security procedures when outsourcing software development.
The digital revolution can be brutal, and companies that fail to keep pace with it will quickly find themselves replaced by one of their more technology-savvy competitors. That means companies need to step up their IT activities to avoid a competitive disadvantage — and they need to find the right technologies and ensure they remain secure along the way. Organizations must be prepared for worst-case scenarios while pursuing their digital transformation objectives. Cloud technologies offer a golden opportunity for any business that aims to fuel its digital transformation process.
Specific knowledge about fintech operations is a must for efficient fintech app development that includes compliance aspects, understanding how different types of fintechs operate, background in finance and banking, and more. Also to ensure growth, fintechs need a reliable and easily scalable platform built in compliance with the best industry practices. Here are 15+ rules for fintech app developers and grouped them according to 4 underlying principles. In Part 1, we focused on security and compliance, and API-led connectivity. In part 2, we dwell on the rules related to software infrastructure scalability, and specific domain expertise.
Edge computing is a means of processing data physically close to where the data is being produced, i.e. where the things and humans are — in the field area, homes, and remote offices. Since they don’t live in the cloud, we need to complement cloud computing with many forms of computing at the edge to architect IoT solutions. Since we’re referring to computing close to the source of things, data, and action, a more generic term for this type of data processing is: proximity computing. In the next year, we will see reference architectures evolve to support new application patterns for IoT that incorporate proximity computing.
One of the most exciting things about cryptocurrency is that the future hasn’t been written yet. And one of the things that strongly distinguish people in the cryptocurrency movement is how they deal with consent. If you care about consent, the safest thing to do is to ask every time. We are all creating this movement together. Let’s all do our best to build the culture of consent. The future of cryptocurrency, the future of open source money, and therefore the future of our financial infrastructure depends on it.
Fintechs currently face the challenges of growing and scaling. Yet most will likely fail because: they could not find the right product-market fit, the high cost of scaling up, inability to find the right partner, and the struggle to create, launch, and quickly gain market share for a differentiated product that cannot be replicated. And to overcome those hurdles, they, first of all, need a reliable and robust platform built in compliance with the best industry practices. We outline15+ time-tested rules of fintech app development.
As enterprises delve more deeply into IoT, there will be a growing need for an operational intelligence-oriented data analyst as many IoT use-cases demand near real-time operational insight. So you can expect to see a huge uptick in demand for people who have technology and business skills related to the Internet of Things (IoT), as organizations continue to ramp up their IoT projects in a big way. As enterprises delve more deeply into IoT, there will be a growing need for an operational intelligence-oriented data analyst who is also an “AI/ML data engineer.”
Deep learning, the spearhead of artificial intelligence, is perhaps one of the most exciting technologies of the decade. There’s no doubt that machine learning and deep learning are super-efficient for many tasks. Deep learning is often compared to the mechanisms that underlie the human mind. However, they’re not a silver bullet that will solve all problems and override all previous technologies. Beyond the hype surrounding deep learning, in many cases, its distinct limits and challenges prevent it from competing with the mind of a human child.
Today’s marketers and salespeople are harnessing technology advancements to unearth insights in real-time, which was unimaginable few years ago. However, the key question is – are we looking at the right data? If our intent is to collect and understand customer feedback, we are looking at only partial information. A lot of information is usually in hidden in what is usually referred to as “dark data” or in simple terms unstructured data. You need to go beyond structured data and demystify the dark data that can give you relevant information.
The advent of IIoT (Industrial IoT) has completely changed the global IoT scenario. On one hand, IIoT drives the fourth wave of industrial revolution, and on the other hand, it contributes significantly to the unprecedented surge in the number of connected devices worldwide. Companies have started utilizing the IIoT to collect, aggregate, and analyze data to maximize efficiency and enhance productivity. The IIoT not only bring automation through machine learning but also establishes better synergy among machines to optimize output. However, implementation of IoT at your workplace requires utmost care and precision.
Product management has a major identify crisis. Product management is both art and science. You can teach it. You can learn it. But you also need to experience it. Product management seems to be an amorphous, fuzzy thing. Buzzword, acronyms and new memes seem to appear daily. Agile, lean, design thinking are popular terms. But how do they relate to product management? They all attempt to improve delivery quality and reduce delivery time. In the end, product management ensures the delivery and launch based on the requirements. If they add great value, why is their role so misunderstood?
Nearly half of cloud services in the enterprise are outside corporate IT’s domain, while around 47 percent of corporate data stored in cloud environments are not managed by the IT department. Cloud computing is attractive to enterprises for cost efficiency as well as its flexibility in allowing employees and customers 24/7 access to information and services. However, the security challenges can be significant. IT managers are often uncertain of which measures are meant to secure what data. Effective security for cloud data demands a holistic approach and recognizes that not all data is vital.
Today, all organizations are digital by default. Digital business inherently means utilizing new technology, connecting devices and operating platforms, embracing different ways of working, building large-scale data silos, and so on. The convergence of Internet of Things networks with what were once separate and self-contained — and therefore more manageable — systems represents a fundamental change. Coping with digital challenges and mitigating risks still represents a major burden for organizations across the board. The World Economic Forum now rates a large-scale cybersecurity breach as one of the five most serious risks facing the world today.
When it comes to emerging technologies, many talk about the importance of incorporating them in day to day processes - especially when it comes to businesses. But there only a few that have hands on experience with them. So we decided to interview some experts who have spent years working on different technologies, experimenting with their use cases and guiding others who wanted to grow in that field.
Behind the revolution in digital assistants and other conversational interfaces are natural language processing and generation (NLP/NLG), two branches of machine learning that involve converting human language to computer commands and vice versa. NLP and NLG have removed many of the barriers between humans and computers, not only enabling them to understand and interact with each other, but also creating new opportunities to augment human intelligence and accomplish tasks that were impossible before. Maybe NLP and NLG will remain focused on fulfilling more and more utilitarian use cases.
Machine learning is known for its difficulties with interpretability, or rather its absence. This is an issue if your users have to work with the numeric output, like in the systems used in sales, trading or marketing. If the user’s interpretation of the ML output is wrong the actual metrics won’t matter and you end up with the bad user experience. The problem is even bigger if you try switching users from an old transparent algorithm to ML. Here I outline the recipes for overcoming the user’s push-back once you start switching your system to ML.
Data is one of the most important assets that any company has Today, there are new and changing uses of data in the digital economy. The big questions however are, who is winning with data, where is this data being kept, what makes new data different, when should data be kept, moved, deleted or transformed, how should data be valued, and why data is so much more important than it used to be? Once we accept the premise that data value should be measured, what would we do with this measure?
Joining IoT and healthcare and leveraging connectivity to deliver care is not as easy as it seems. How can we ensure the successful marriage of IoT and healthcare? Let’s expand the notion of “pathways” to appreciate how IoT can be leveraged in healthcare delivery. The expansion includes the notion that data needs to flow across the players involved in care delivery and money needs to flow to compensate the right parties. We are on our way to professionally managed healthcare using IoT, not just consumer-grade wearables for the fitness-conscious.
The views on AI software development are polarized and controversial. Still, many experts see AI as an opportunity rather than a threat. AI technology may act as a method of augmenting human workforce and enabling us to work in newer and smarter environments rather than disrupting every single aspect of our lives. But like with any powerful technology, we need to use it prudently. Here are the most common illusions and myths about AI software development and shed some light on the possibilities of this disruptive technology.
Enterprises are moving cyber security and resiliency to the top of their priority lists (if they were not there already). This is especially true for enterprises building or managing IoT applications. Attacks which used IoT devices to launch distributed denial of service attacks (DDoS) are raising both consumer and business customers’ fears regarding the security of the IoT. When it comes to cybersecurity, IoT devices come with their own unique security challenges. Here are five key best practices enterprises can adopt to significantly lower the risk of a successful attack on their IoT infrastructure.
Experienced teams know when to back up seeing a piling debt, but technical debt in machine learning piles extremely fast. You can create months worth of debt in a matter of one working day and even the most experienced teams can miss a moment when the debt is so huge that it sets them back for half a year, which is often enough to kill a fast-pacing project. You end up with the project where the metrics randomly jump up or down, do not reflect the actual quality, and you are not able to improve them.