Shamayita Acharya

As time goes on, it seems that there’s very little left in the world that is purely “analog” anymore. The business world is digital nowadays, and there are many benefits attached to that reality. The caveat is that there are inherent risks that come along with the digitization of society, and cybercriminals know it. Now that the IoT has grown and the world is becoming increasingly wireless, we’ve finally reached the point where even small businesses and private citizens are falling victim to data breaches and cyberattacks. The problem is that as sophisticated as technology and IT security get, malicious actors, are always one step ahead, equally (if not slightly more) sophisticated themselves. Organizations are struggling to keep up and adapt to such a harsh digital landscape, while simultaneously recognizing that there’s no doing business outside of said landscape. This presents a catch-22 that becomes more visible within IT security every day. Fortunately, advanced analytics and

Now the retail sector is seeing a rapid transformation. Such technologies as IoT and Big Data have already started penetrating the industry. Coupled with mobile apps and machine learning, they enable retailers to take a deep look at customer needs and preferences. By using technological innovations, retailers can get various advantages and opportunities, for example, the collection of customer data, audience segmentation, improved marketing campaigns based on customer analysis, and much more. However, it’s not the end of retailers’ challenges. Today customers are especially concerned about fair trade practices, the truth about the claims that retailer companies make about their goods, as well as receiving high-quality services. Empowered by social media, the new age retailer’s customer is more informed, demanding, and conscious. Trust, security, and transparency are becoming critical for success, requiring the adoption of new business models. Blockchain technology, that is going to

In this age of data and convenience, customers across the globe are getting used to great customer experience from numerous companies. Big names such as Google, Apple, Amazon, and many others lead the way when it comes to ensuring a seamless customer experience. While these names lead the front, Telcos lag behind when it comes to their perception of great customer experience. In consideration of the fact that Telcos lag behind when it comes to their perception of great customer experience, I recently talked to Thomas Kinnman from Ericsson. Both of us discussed important factors related to customer experience in the eyes of Telcos, and what should be done in this regard. Telcos Lagging Perception of Great CX There are numerous negative customer experiences that often go unnoticed by Telcos. Telcos fail to deliver action at the right time and often end up losing the customer value that they would have wanted to provide. It is necessary for Telcos to understand what constitutes a ne

We recently spoke with Dan Cantorna, Director of Data Innovation at Collinson Group to give us his thoughts on how the landscape was looking Data Science and the customer journey and here are his thoughts... Over the past year we have seen advances in facial recognition and next-generation voice technologies which provide an ever-improving Artificial Intelligence-based (AI) toolkit for loyalty programmes. These newer services have been embraced by consumers, making them attractive to retailers as useful tools to help improve their customer relationships and increase loyalty. While no piece of technology is in and of itself the facilitator of loyalty, the combination of these solutions offers a new way to communicate a meaningful loyalty strategy in the modern era of retail. AI-reliant technologies can change the retailer-customer game significantly and be a real differentiator for the smart brands that are early adopters. Last year, smart, AI-powered speakers from Amazon and Google

By Lance Eliot, the AI Trends Insider When I get to work each morning, I often see a duel between car drivers when they reach the gate that goes into the workplace parking garage. Let me describe the situation for you. For those drivers such as me that tend to come into the parking garage from a major street that runs in front of the parking facility, we turn into a narrow alley that then leads to a gate arm. You need to then take out your pass card, wave it at the panel that will open the gate, and the gate arm then rises up (assuming you have a valid card). At this juncture, you are able to drive into the parking structure. This works just fine most of the time in the sense that once the gate arm is up, you zoom ahead into the parking structure. But, it turns out that there is a second gate that is inside the parking structure and it allows traffic that is already in the structure to get onto the same parking floor as the gate that connects to the major street. This other gate ar

It’s time to take a step back from the noise and hype surrounding artificial intelligence (AI). Businesses have been inundated with AI sales pitches promoting the technology’s potential to automate tasks, increase speed and accuracy and cut costs. But what’s the long-term plan? Most businesses lack a vision of how AI will transform their operations. Developing an overarching AI strategy Rather than the piecemeal adoption of AI systems, some believe businesses need to develop an overarching strategy for how to embed AI in their organisation over time. “The most important thing is having a comprehensive and holistic view of AI sourcing within the organisation,” says Mohammed Chaara, a former Lenovo strategist who is now “an evangelist” for AI. Working out what kind of AI is needed for different processes and whether these will be carried out in-house, outsourced or in partnership is an important step to developing a strategy, he says. Mr. Chaara believes businesses need to base thei

By Timothy Chou, IoT Lecturer, Stanford University If you work for a company that produces such items as heating, ventilation and air conditioners, 3D cardiac imaging machines, seed drills or submersible pumps, chances are that you’re already aware of the rise of the Internet of Things (IoT). All of us in the tech community are constantly excited about any opportunity to tell the rest of you about our cool technology that can run on your machine, vehicle or product. We see the IoT’s potential to not only improve services but also to connect each product to the internet, to collect data from it and, using advanced machine learning technology, to derive predictions from the ongoing analysis of that data. But if you’re the CEO of an already successful manufacturing company, maybe the questions you’re asking right now are “Why should I care? Isn’t the IoT just the stuff my geeky R&D staff care about? Don’t my products sell just fine without IoT connectivity? How can it possibly be m

By Matthew Mayo, KDnuggets Summer, summer, summertime. Time to sit back and unwind. Or get your hands on some free machine learning and data science books and get your learn on. Check out this selection to get you started. It’s time for another collection of free machine learning and data science books to kick off your summer learning season. Because that’s a thing. Right? If, after reading this list, you find yourself wanting more free quality, curated books, check the previous iteration of this series or the related posts below. 1.  Python Data Science Handbook By Jake VanderPlas The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages. Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, A Whirlwind Tour of Python : it’s a fast-paced introduction to the Python language aimed at resea

A new solar-powered weed killer robot that looks like a table on wheels, can scan rows and rows of crops with its camera and zap weeds with jets of blue liquid as soon as they are identified. The liquid will be replaced by a weed killer spray as soon as final tests are complete. This is a new breed of AI weeders that could disrupt the $100 billion pesticides and seeds industry by reducing the need for herbicides and modified crops. “Some of the profit pools that are now in the hands of the big agrochemical companies will shift, partly to the farmer and partly to the equipment manufacturers, ”  said Cedric Lecamp, who runs the $1 billion Pictet-Nutrition fund that invests in companies along the food supply chain. Even though the technology is still in the initial phase, ot marks a shift from the standard methods of crop production to plant-by-plant approach. ecoRobotix, the developer of the weed killer robot, believes that the design can reduce the amount of herbicide farmers use by

It seems that no matter how advanced cyber security systems get over time, the human factor always remains the greatest liability. When it comes to cyber attacks, most of the time the breach happened due to an employee’s misjudgment, carelessness, or simply lack of knowledge. According to a research conducted by BakerHostetler that observed the causes of major security breaches in 2016, 24% of them were caused by employees’ actions or mistakes. On top of that, 31% of issues happened because of malware, which could easily be counted among employees’ mistakes. So, in total, over half of all security problems come from human errors. That is something that shouldn’t be ignored, especially in today’s world where the threat of cyber attacks is at an all-time high. To keep this human factor at a manageable minimum, companies need to educate their employees and follow certain guidelines. Here is a list of things they should focus on. Phishing We’ve already mentioned that the staggering

With the technological advances in the past years, the mobile phone has become an inevitable part of our lives. It has transformed the way we live or work. Today, we rely on mobile apps present on our phones to perform multiple tasks like activity tracker, diet monitor, meeting planner and more. All these apps involve the tremendous use of data, which needs to be analyzed by a robust data management tool. Big Data management helps get insights from the generated information of the apps, which the users use every day. The rapid growth of the data is enabling the app developers to leverage the analytics to fuel the app development process and make it a success. Let’s have a look at how Big Data technology drives the mobile app development process for enhanced user experience. Role of Big Data in Mobile App Development Strategy The excellence of user engagement and mobile interactions depend on the app’s ability to analyze and deliver the results user wants. Big data has tremendou

A few years ago, mentioning the term big data, even within industry circles, was more than likely to elicit some quizzical stares. In 2018, it's hard to find a business that isn't already deeply immersed in the technology. That's emblematic of a field that has exploded in popularity worldwide, and that continues to grow exponentially with each passing day. It has been good news for industries of every kind, and a boon to qualified big data professionals, too. Although there's been a huge surge in demand for big data skills in the job market, there hasn't yet been a corresponding surge in training to meet that demand. That has led to a much-discussed "skills gap" in the big data field that has caused no small amount of hand-wringing by industry analysts. The disparity in the number of available positions compared to the number of qualified applicants has even spurred a massive surge in demand for contractors that have the skills to serve as a stopgap for

We are excited to make several product announcements including the general availability of : HDP 2.6.5 Apache Kafka 1.0 Apache Spark 2.3 Apache Ambari 2.6.2 SmartSense 1.4.5 HDP 2.6.5 is an important release for Hortonworks given it is the first release that enables Apache Kafka 1.0 and Apache Spark 2.3 Hortonworks Data Platform 2.6.5 With […] The post Announcing the General Availability of Hortonworks Data Platform (HDP) 2.6.5, Apache Ambari 2.6.2 and SmartSense 1.4.5 appeared first on Hortonworks .

After the advent of the GDPR, companies that are smart about protecting data will find themselves at a distinct competitive advantage. The post Protecting Data: How to Adapt to the GDPR appeared first on Hortonworks .

This is the 6th blog of the Hadoop Blog series (part 1, part 2, part 3, part 4, part 5). In this blog, we will explore how to leverage Docker for Apache Spark on YARN for faster time to insights for data intensive workloads at unprecedented scale. Apache Spark applications usually have a complex set […] The post Containerized Apache Spark on YARN in Apache Hadoop 3.1 appeared first on Hortonworks .

If data is the new bacon, data stewardship supplies its nutrition label! This is the first part of a two part blog introducing Data Steward Studio (DSS) and discusses the problems that DSS addresses in the enterprise data landscape. Part 2 of this blog will cover a detailed capability walkthrough. Data lakes, which promise to […] The post DISCOVER with Data Steward Studio (DSS): Understand your your hybrid data lakes to exploit their business value! appeared first on Hortonworks .

Last month we had an incredible DataWorks Summit Berlin! This marked our sixth year in Europe, and the event featured 1,200 attendees from 51 different countries. The theme was “Ideas. Insights. Innovation.” which focused on how leading enterprises are using advanced analytics, data science, and artificial intelligence to transform the way they deliver customer and […] The post Ideas to Implementation – Identifying the Right Data Strategy to Find Success appeared first on Hortonworks .

By Lance Eliot, AI Trends Insider Are you a Yanny or a Laurel? Unless you’ve been living in a cave, you likely know about the recent craze over an audio clip that has created a social media frenzy and sparked a debate among both friends and foes alike. A short audio clip that was posted on Twitter had asked listeners to report whether they heard the word “Yanny” or the word “Laurel” when hearing the clip. Thousands upon thousands of replies seemed to suggest that the world is split into Yanny believers versus Laurel believers. At times, it has been an even split, while at other times the tide starts to go toward the Yanny side and the next moment it slides over to the Laurel side. I’ve overheard people talking about this curious and mind-puzzling audio phenomenon while I’ve been in the line at Starbucks, while at the coffee machine in the office, while at the grocery store getting my weekly foodstuffs, and even while camping in the middle of the woods. If a tree were to fall, would

Alibaba  is teaming up with SenseTime,  the world’s highest-valued AI startup, to launch a not-for-profit artificial intelligence lab in Hong Kong in a bid to make the city a global hub for artificial intelligence. Alibaba, which is SenseTime’s largest single investor thanks to a recent $600 million round at a valuation of $4.5 billion, is providing financing for the “HKAI Lab” through its Hong Kong entrepreneurship fund. SenseTime said it will contribute too, although the total amount of capital backing the initiative hasn’t been revealed. The partners of the project — which also includes the Hong Kong Science and Technology Parks Corporation (HKSTP) — said the aim is to “advance the frontiers of AI,” which includes helping startups commercialize their technology, develop ideas and promote knowledge sharing in the AI field. That’s all fairly general — Alibaba has a track record of politicking through technology investment schemes in Greater China and Southeast Asia — but one tangi

By Bernardo Lustosa, Partner, cofounder, and COO at ClearSale In the early days of machine learning, hiring good statisticians was the key challenge for AI projects. Now, machine learning has evolved from its early focus on statistics to more emphasis on computation. As the process of building algorithms has become simpler and the applications for AI technology have grown, human resources professionals in AI face a new challenge. Not only are data scientists in short supply, but what makes a successful data scientist has changed. Divergence between statistical models and neural networks As recently as six years ago, there were minimal differences between statistical models (usually logistic regressions) and neural networks. The neural network had a slightly larger separation capacity (statistical performance) at the cost of being a black box. Since they had similar potential, the choice of whether to use a neural network or a statistical model was determined by the requirements of

Open data sharing within the pharmaceutical industry is crucial for opening the floodgates of innovation. Successful innovation depends on having a rich supply of high-quality data because, without the right kind and quantity of data, there wouldn’t be a foundation for the research required to innovate. The best way to achieve this critical mass of high-quality data is by improving data sharing practices. Why Open Data is Needed Productivity in the pharmaceutical industry has been steadily declining over the past 20 years, mainly because of increasing costs coupled with a meagre output of new medicines. As explained by R. Mullin in “Cost to Develop New Pharmaceutical Drug Now Exceeds $2.5B,”  it costs an estimated $2.6 billion and takes more than ten years to develop and test a drug candidate. To make matters even more complex, healthcare providers are starting to require better value on spending and clear evidence that new medicines are indeed more effective than the current stand

Cybercriminals are a constant thorn in the side of every IT professional. That said, it’s hard not to admire the inventiveness and determination that goes into many hacking campaigns. The emergence of malvertising as a mainstream “industry” is a prime example. In 2017, a single group of hackers managed to spread malware-infected adverts to 62% of the web’s “ad-monetised websites on a weekly basis.” They did so using a network of fake advertising agencies, complete with bogus executive LinkedIn profiles and phoney social media presences. What’s more, they did it all without really having to get their hands dirty. The eventual payload of a malvertising campaign isn’t particularly new or sophisticated; It’s generally all about infecting computers with malware using things like fake Adobe Flash updates and dishonest “scareware” internet security programs. The clever part is how the hackers now spread the malware via legitimate advertising networks, giving themselves a reach across mill

Not very long ago, any firm would need to go through rigorous operations to be able to operate in a country. Things have changed with globalisation and the advent of the blockchain era, in which startups are based out of anywhere in the world and raising money in millions and some, in billions. How different is it for cryptocurrency-based startups as opposed to others on the market? Here’s the reveal: The Growth Can be Painstakingly Slow A few years back, in 2015, Magister Advisors reported about getting to see five blockchain/bitcoin-based unicorns in 2016. It wasn’t until August 2017, when Coinbase was coined as the first-ever cryptocurrency unicorn startup. The firm had a $1.6 billion valuation at that time because of a $108 million funding round that was announced. This gap of two years clearly depicts that the growth for blockchain-based startups is anything but rapid.  You Don’t Always Need a Product  If you take a look at unicorns like Airbnb, Uber, Lyft etc., you’ll f

An article published by the US Bureau of Labor Statistics claims that big data will play a decisive role in the labour markets by 2040. In reality, though, that has already started happening on a large scale. Big data tools are already mainstream in a lot of administrative tasks including recruitment, productivity management, corporate strategy and marketing. One segment that has relatively been untouched by big data is corporate training. The past decade has seen a dramatic transformation in the way workers are onboarded and trained at corporate workplaces. Classroom training has been overwhelmingly replaced by learning management systems and remote training tools. Despite the ubiquity of technology, big data has not been extensively used. That may, however, be changing. Experimental use of big data analytics in universities like Georgia State University has helped increase the overall graduation rate by over 22 points. Predictive analytics based on big data have been used by educ

By Lance Eliot, the AI Trends Insider Machine Learning (ML) is essential for the advent and further progress of AI self-driving cars. The nature of how Machine Learning is being undertaken today for AI self-driving cars will undoubtedly evolve and become more sophisticated over time. One crucial aspect for Machine Learning in the context of AI self-driving cars is whether or not to distribute out the Machine Learning aspects, and if so to what degree the ML should be distributed. This aspect of distributing ML is often referred to as Federated Machine Learning (FML). You can think of the word “federated” in the same sense that it is used for the governmental arrangement of the United States. The United States is a collection of distributed States that are collectively part of an overarching federation. We are continually striving in the United States to ascertain what is the appropriate balance of States rights versus Federal, and there are ongoing debates about how much autonomy th