Developing in React Native using Atom and Flow

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One drawback of writing in React Native is the lack of type-checking that is common in Javascript. While using a separate language like TypeScript is a possibility, it also requires full commitment to the build process and for developers familiar with Javascript to learn a new technology. Flow is a popular alternative that actually ships with React Native. This post intends to show you how to get the flow errors and warnings in your Atom editor as well as how to set up pre-commit to restrict committing code that has Flow errors.

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Mascon Global

Mascon Global Limited (MGL) is a global provider of IT and ITES services with offices and software development centers across USA, Mexico, Europe, and Asia. MGL has been assisting companies accomplishing their goals by providing a wide range of technology solutions by leveraging its domain and business expertise and strategic alliances with leading technology providers.

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DIGITAL TRANSFORMATION

The Holy Trinity of Transformation - Culture, Leadership and Sustainability

Article | April 12, 2021

Digital Transformation is not a magic wand; it is a complex yet essential enterprise commitment to change. Companies that have succeeded have reaped significant benefits. The Deloitte Digital Transformation Survey 2020 found that greater digital maturity is associated with better financial performance. The higher-maturity companies in this year’s sample were about 3X more likely than lower-maturity companies to report annual net revenue growth and net profit margins — a pattern that was consistent across industries. Unfortunately, most enterprises do not fully appreciate what it entails. Some see it as a technology or a budget problem; others believe it is an optional strategy — they are both wrong. To truly succeed, transformation needs to be led from the top by setting the strategy and allocating resources. Antonio Neri of HPE hits the spot when he says, “Digital transformation is no longer an option for enterprises, but a strategic imperative.” For me, one of the most significant examples of top-driven organisational change is Jeff Bezos’ call to “Rearchitecting the Firm” in 2002. It is a seminal work. The principles of this mandate went on to form the backbone of Amazon in the modern cloud world. It was clear, direct, and backed up by management action. More than 75% of CEOs agreed that the pandemic sped up their companies’ transformation plans COVID is a catalyst for change The flurry of digital technology solutions spurred by COVID-19 presents a unique opportunity for enterprises to rethink how technology decisions are made and apply them in new and meaningful ways. Covid-19 dramatically accelerated technology adoption across all industries. According to a Fortune-Deloitte CEO survey and the KPMG 2020 CEO Outlook Survey, more than 75% of CEOs agreed that the pandemic sped up their companies’ transformation plans. As Microsoft CEO Satya Nadella noted, “We’ve seen two years’ worth of digital transformation in two months.” 80% of companies plan to accelerate their companies’ digital transformation plans, primarily incentivized by the global pandemic implications. The same study also concludes that only 30% of digital transformations have achieved their objectives which is troubling. 80% of companies plan to accelerate their companies’ digital transformation plans, however only 30% of digital transformations have achieved their objectives - BCG Research Most people forget that digital transformation is less about technology and much more about the organization’s culture and business shift. Key stakeholders need to rethink customer experience, business models, and operations fundamentally. It is all about finding new ways to deliver value, generate revenue, improve efficiency, and, most importantly driving sustainable innovation. Bear in mind, just moving to the cloud is not Digital Transformation. Crises Breed Innovation I am of the firm belief that uncertainty drives creativity. Crises are the breeding ground for innovation. You must make decisions quickly, and you never have enough time or information to weigh difficult choices thoroughly. McKinsey’s analysis shows that bold innovators emerge from crises substantially ahead of peers — and maintain this advantage for years to come. Innovators not only outperformed the market during the financial crisis but continued to widen the gap during and after the recovery. Analysis of the performance of approximately 2,000 companies between 2007 and 2017 against the S&P 500 reinforces those conclusions: staying focused on growth and innovation through a downturn helped the top-performing companies to generate higher returns to shareholders. Staying focused on growth and innovation through a downturn helped the top-performing companies to generate higher returns to shareholders Antonio Neri and other leaders confirm that as the pace of technology disruption continues to accelerate, digital-native and digitally transformed companies are outpacing their competitors. The McKinsey study shows that roughly one in ten companies in their sample achieved higher revenue growth, innovation, digital adoption, and profitability than the others over the entire 2007–17 economic cycle and during the downturn years. The outperformers also delivered excess returns of roughly 8%, while the rest hovered around zero throughout the period. So, what does it take to succeed? Do existing leadership teams have the skills to undertake true digital transformation? I thought it would be a good idea to look at how companies are hiring critical resources. A study by professors from Harvard and Darden and executives from Spencer Stuart published in the Harvard Business Review addressed this specific question. The team looked at more than 100 search criteria for C-suite positions in Fortune 1000 companies across a broad range of industries, and the results were very suggestive. There has been a rise in tech and digital expertise search even before the pandemic: 59% of executive searches included technological or digital knowledge. Company boards were asking for these skills across a wide variety of roles. This fact also suggests that people with the right skill sets are already in leadership positions. Not surprisingly, 100% of the specs for CIOs, CMOs, and CTOs sought technical or digital skills. However, the functions that got neglected in the search for technological and digital expertise were more revealing. Less than a third of the job specs for CHROs and chief accounting officers mentioned these required skills. Even more worrying — only 40–60% of searches for roles such as CEO, board director, and CFOs required digital know-how. At the very minimum, we need all leaders to understand how to build digital businesses. This shift alone could be the difference between success and failure. But is that enough for now? Almost every organization has stepped up its digital transformation efforts in 2020–21. Success is as much about the right technology platform choice as it is about leadership, agility, talent, and a clear vision. A new and emerging factor is consumers wanting the brands they use to focus on sustainability issues. So do employees and prospective employees. The driver for this shift largely springs from realizing that human activities’ ecological footprint is a probable cause for the crisis we face today. While we keep talking about the usual polluters like utilities, transportation, agriculture, and climate change causes, some lesser-discussed and more exciting facts would make the issue more relatable. Did you know that in processing 3.5 billion searches a day, Google accounts for about 40% of the internet’s carbon footprint? They have been carbon neutral since 2007, but their infrastructure still emits a considerable volume of CO2. Did you know that Bitcoin currently uses enough power (121 terawatt-hours) to run Cambridge University for almost 700 years? To address sustainability in a meaningful manner, we need to take a holistic view of the players, their impact and then push for a mutually beneficial solution . Else, it is bound to fail. As a first step, 26 CEOs of Europe-based companies have signed a Declaration to support Green and Digital Transformation of the EU. They formed a European Green Digital Coalition, committing on behalf of their companies to not only make the tech sector to become more sustainable, circular, and a zero polluter but also to support sustainability goals of other priority sectors such as energy, transport, agriculture, and construction while contributing to an innovative, inclusive and resilient society. Like these CEOs, Accenture also believes that there is great value at the intersection of digital technologies and sustainability — they call it Twin Transformers. Companies leveraging both are 2.5X more likely to be among tomorrow’s strongest-performing businesses than others. BigTech is conscious of its responsibilities to the climate. Almost all majors players have made pledges to reverse CO2 emission. Since they are all profit-driven, I am sure they have also figured out this also means good business by the numbers too (a counter-intuitive rationalisation but better than getting caught in the justification game) In the future, a company’s commitment to ESG-related programs will drive the ability to attract investors and retain talent. Companies also realize that ESG factors, when integrated into strategic digital transformation decisions, may offer potential long-term performance advantages. One of the critical levers for moving to sustainable systems will be technology, a lot of technology, and a lot of investment. But how do we make it accessible to all and profitable to the providers at the same time? HPE is one company that has made significant strides in this regard by embracing the twin doctrine of digital transformation and sustainability. Their customers can reduce their energy costs by more than 30% by eliminating overprovisioning through HPE GreenLake. In fact, their consumption-based offerings have reduced customer carbon footprint by 50% in one case. Minimizing e-waste is another area of focus for them too. So what have we learned from all this? As an ancient Chinese proverb states, “When the winds of change blow, some people build walls, others build windmills.” What will you build?

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The 6 Biggest AI Marketing Challenges & Their Solutions

Article | April 12, 2021

More and more businesses are utilizing modern-day opportunities that Artificial Intelligence (AI) brings to the digital world. Perhaps, it is the most necessary step for the companies to stay competitive in 2021 and beyond. With the rise of technology, AI-powered marketing platforms are becoming more common and simpler to use. However, this does not mean that they do not have any challenges. A survey conducted by Teradata, a data analytics firm, reports that around 80% of enterprise-level organizations have already embraced some form of AI. Out of them, approximately 32% of businesses use AI algorithms for marketing purposes. However, more than 90% of these companies have already anticipated significant barriers to adopt and integrate AI. In this article, we shed light on the six biggest challenges in AI marketing. It will help you act and avoid common problems if you encounter such roadblocks when integrating AI into your marketing strategy. Here are some highlights of this article: Many popular media sources have created hype around AI. Therefore, people, in general, don’t trust it. There isn’t enough skilled workforce to fill AI-related positions in organizations. AI software needs high-quality data. Unfortunately, maintaining such data quality is not that easy. AI software needs significant investment. Many small businesses lack IT infrastructure resources. Cloud services help them overcome this problem. As you can now understand, most challenges in AI marketing revolve around business alignment, data, or people. While every organization varies and will face the AI adoption process differently, there are a few common challenges in AI marketing you should be aware of. So, without further ado, let’s take a look at the most common AI challenges that digital marketers face. Lack of Knowledge of AI Systems When it's about total AI implementation, your company’s management must have a deeper understanding of the role of AI in Digital Marketing, the latest AI trends, data challenges, and all other essential aspects. However, many marketers lack a proper understanding of the use of AI technologies in marketing. On top of this, unfortunately, AI comes with a variety of fears and myths. While some people think they need an in-house data science team for complete AI adoption, others believe in those sci-fi fantasies showing how smart robots can end humanity. Insufficient knowledge of AI is one of the biggest challenges in AI marketing. It hinders the AI implementation in several ways and ultimately delays the success. How to get rid of this? First things first — start by acquiring knowledge. It might sound a bit demotivating, but we do not mean you have to be a data scientist for this. You can look at other giants in the industry, carefully analyze how they are deploying AI into their business, and act accordingly. Next, know more about the current AI technologies for marketing — you can either DIY or get help from an expert. Once you have adequate knowledge about it, you know what to expect from AI and what not. Challenges in Integration Deployment and integration of new technology requires skills. Integrating Artificial Intelligence into your business is not an easy task. It is a complicated job and requires proper knowledge. You first have to set up interfaces and other elements to address all your business needs. Such steps may require complex coding. Developers must consider feeding the data into the system, labeling, data storage, data infrastructure needs, and much more while setting up the elements. Then comes the model training and testing part. It is necessary for the following reasons: To check the effectiveness of your AI Develop a feedback loop for constant improvement Data sampling for reducing the stored data and run models even faster The biggest challenge here is — how to confirm if it's working correctly? And, is it worth the money you are investing? Arguably, the only and the most effective way to overcome this hurdle is to work closely with your vendor to ensure that everyone is well aware of the process. Plus, there should not be any limitations in the vendor’s expertise. They should be capable of guiding you beyond building the AI models. When you implement Artificial Intelligence with the right strategy, you indirectly reduce the risk of failure. And, once you successfully implement AI into your system, you will still have to educate your marketers to use it efficiently. In this way, your people can understand how to interpret the results they receive by proper implementation and effective use of the AI model. Poor Data Quality or Lack of Data High-quality data is essential for Artificial Intelligence. Any AI system will come up with poor results if you provide it with insufficient or poor-quality data. As the Big Data world is evolving every day, businesses are gathering vast amounts of data. However, this data is not always up to the mark. It's either insufficient or not good enough to drive a profitable AI marketing strategy. Such data-related challenges in AI marketing prevent companies from capitalizing on Big Data. For this reason, as a business, you should always make sure the data you get is clean and rich in quality. Otherwise, you will experience unsatisfactory results from the AI, and it will negatively influence the overall success of your AI-powered marketing campaigns. Budget Constraints for AI Implementation Many companies lack the necessary budget for implementing AI into the system. Even though AI has the power to provide impressive Returns of Investment (ROI), hefty investments are still one of the biggest challenges in AI marketing, especially for smaller and mid-size companies where the budgets are already stretched. AI-powered platforms come with high-performance hardware and complex software. And, the deployment and maintenance of such components are costly. Such budgeting challenges in AI marketing can limit the opportunities for businesses to utilize AI technology to the fullest. Thankfully, this is now becoming a thing of the past as many affordable AI vendors are coming ahead for the rescue. With them, you do not have to invest in developing in-house solutions. Moreover, they allow you to implement AI tech in a relatively cheaper and faster way. Privacy and Regulations Artificial intelligence is still new to this world, and it's growing at an incredible pace. Chances are that the rules and regulations surrounding AI will change and tighten up over the coming days. The data collection and use of data policies already impact businesses that collect and use data from the customers based in the European Union and drive their Artificial Intelligence systems. The EU implemented GDPR in 2018, and it has made the data collection, and data usage rules even stricter for companies. Ultimately, companies now have to be extra careful while collecting and using customer data. Furthermore, several businesses are restricted from storing the data offsite for regulatory purposes. This means that they can no longer utilize cloud-based AI marketing services. Constantly Changing Marketing Landscape AI is a new marketing tool and can bring disruption to traditional marketing operations. For this reason, marketers evaluate how AI can create new jobs and, at the same time, replace older jobs. One survey suggests that AI marketing tools are more likely to replace the jobs of around 6 out of 10 marketing analysts and marketing specialists over the coming years. Overcoming The Challenges in AI Marketing Yes, such challenges in AI marketing can sometimes slow down your campaigns and affect the outcomes of your AI-driven software. But fortunately, there are a variety of alternative solutions. You need to consider the following steps to rule out the common challenges in AI marketing we discussed earlier. Develop a target oriented marketing strategy Get the money before you roll out AI in marketing Train your marketers Recruit the right talent Developing business cases, recruiting talented marketers, measuring the ROI, and getting the required investment — probably, none of these steps sound interesting. But, when it is about the reality check of your AI marketing strategies, they are absolute methods that can open the door to actual Artificial Intelligence payoffs. In the end, every company's responsibility is to make sure that they are using the AI system responsibly so that they can benefit their customers in the best way possible. Frequently Asked Questions How does AI affect marketing? AI helps marketers to spot the latest internet trends and predict them for the future. Such trends are necessary to learn the current marketing facts and eventually help with significant tasks such as budget allocation and setting up the target audience. Plus, AI effectively reduces the money and time usually spent by companies on digital advertising. Simultaneously, it leads businesses towards smarter and more targeted advertising campaigns. As a result, many companies have implemented AI into their digital marketing strategies as it can increase sales and save money at the same time. On a bigger scale, AI has an impact on global trends, sustainability, and scalability. Even government issues, major public concerns, and major cities around the globe have seen positive effects of AI. AI can make the world a better place if used in the right way! How is AI used in digital marketing? Companies are utilizing some stand-out developments for improving the customer experience with the proper use of AI. For example: Image recognition technology Predictive and targeted content Content creation Chatbots With these, AI enhances customer support, and provides more relevant and targeted content to the customers. Why is artificial intelligence critical in marketing? With the correct use of Artificial intelligence, businesses can collect, analyze and store a large amount of data. As a result, AI is the best way to learn the latest marketing trends and incorporate them into your marketing strategy. In general, Artificial Intelligence has the power to help your company reach potential customers and provide them with easy access to make purchases. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "How does AI affect marketing?", "acceptedAnswer": { "@type": "Answer", "text": "AI helps marketers to spot the latest internet trends and predict them for the future. Such trends are necessary to learn the current marketing facts and eventually help with significant tasks such as budget allocation and setting up the target audience. Plus, AI effectively reduces the money and time usually spent by companies on digital advertising. Simultaneously, it leads businesses towards smarter and more targeted advertising campaigns. As a result, many companies have implemented AI into their digital marketing strategies as it can increase sales and save money at the same time. On a bigger scale, AI has an impact on global trends, sustainability, and scalability. Even government issues, major public concerns, and major cities around the globe have seen positive effects of AI. AI can make the world a better place if used in the right way!" } },{ "@type": "Question", "name": "How is AI used in digital marketing?", "acceptedAnswer": { "@type": "Answer", "text": "Companies are utilizing some stand-out developments for improving the customer experience with the proper use of AI. For example: ● Image recognition technology ● Predictive and targeted content ● Content creation ● Chatbots With these, AI enhances customer support and provides more relevant and targeted content to the customers." } },{ "@type": "Question", "name": "Why is artificial intelligence critical in marketing?", "acceptedAnswer": { "@type": "Answer", "text": "With the correct use of Artificial intelligence, businesses can collect, analyze and store a large amount of data. As a result, AI is the best way to learn the latest marketing trends and incorporate them into your marketing strategy. In general, Artificial Intelligence has the power to help your company reach potential customers and provide them with easy access to make purchases." } }] }

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What Is an Intelligent Virtual Assistant and How Can It Help Your Business?

Article | April 12, 2021

This article was originally published in Smart Selling Tools and is reprinted with permission by the author. The piece has since been updated to match advances in our technology. The fourth industrial revolution is upon us. Connectivity, data, and processing power have come together to make disruptive technologies like Artificial Intelligence (AI) possible. While we are just scratching the surface on what we can do with AI, we’re seeing more and more companies – from fledgling startups to large corporations – begin to offer specialized AI applications.

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AI TECH

AI Adoption: an advanced digital transformation process

Article | April 12, 2021

Common view is that AI software adoption is 'on its way' and it will soon replace many jobs (example self-driving cars with drivers etc.) and the majority of companies are starting to embrace the efficiencies that AI brings now. Being a practitioner of AI software development and being involved in many projects in my company AI Technologies, I always found my direct experience in the field in contrast with what the media generally portraits about AI adoption. In this article I want to give my view on how AI projects affect the work dynamics into clients work processes and compare that with the studies available on the impact of AI and new technologies on work. This should help the reader, especially if he is an executive, to set the right expectations and mentality when he is assessing the potential investment into a new AI project and if his company is ready for it. To start with, any software development project, including AI, can be summarized into 3 stages: proof of concept (POC) when the prototype has been built, product development when the software is actually engineered at scale, live support/continuous improvements. It occurs often that projects in AI will not go pass the POC stage and this is often due to 1) not right IT/data infrastructure in place 2) not specialist people have been hired to handle the new software or digital transformation process has not been planned yet. Regarding point 2, the most difficult issue is around hiring data scientists or data/machine learning engineers because many companies struggle with that. In fact, in a March 2021 O’Reilly survey of enterprise AI adoption, it has been found that “the most significant barrier to AI adoption is the lack of skilled people and the difficulty of hiring.” And in 2019 software it has been estimated that there were around 144,000 AI- related job openings, but only around 26,000 developers and specialists seeking work. Of course hiring an internal data scientist, it is not the only problem in restructuring the workforce. Often a corporation has to be able to re-train entire teams to be able to fully benefit from a new AI software. I can give an example. As many readers know a sales process involves 3 stages: lead generation, q&a call/mails with potential clients and deal closing. Now, a couple of years ago AI Technologies had been engaged to automatize the q&a call stage and we build a ai bot to manage the 'standard questions' a potential client may ask (without getting into the details, using AI and technically word3vec encoding, it is very possible to automate mails/chatbot for 'standardized questions' like 'how much it cost?' 'how long is the warranty for' etc.). Using this new internal solution, it meant the team responsible for the q&a would have been retrained either to increase the number of leads or the number of closing. The company simply decided to not embark into the transformation process required to benefit the new AI adoption. This example, in various forms, it is actually quite common: companies unless they are really innovative prefer to continue with their corroborated internal procedures unless some competitors threat their profitability. This bring to the fact that actually AI is not an out of the shelves solution which can be plugged in with no effort. As the moment a POC is under development it should be a good norm to plan a digital transformation process within the company. Also it is worth mentioning that, it is unlikely that the workforce has to be dismissed or made redundant as many expected following AI adoption. Just following the example above, what the AI bot does actually is to get over the repetitive tasks (q&a) so people can do more creative work engaging more clients (lead generation) or convincing to buy ( deal closing). Of course, it means that some people have to be retrained but also means that with the same people, you can close/generate more sales. It is a misconception to think that AI solutions will make human work redundant , we just need to adapt to new jobs. My example resembles a classical example on adoption of ATMs. When ATMs were introduced in 1969, conventional wisdom expected the number of banking locations to shrink, but instead, it actually made it possible to set up many more of them, it became cost-effective. There were under 200,000 bank tellers in 1970, but over 400,000 a decade later. The other common problem to face when companies want to embrace AI adoption (point 1), it is their current infrastructure: databases, servers, and crm systems have to be already in place. To put it simply, any AI system requires data to work with so it naturally sits on top of data infrastructure in day to day business operations. In the last two years AI Technologies has been engaged to work with a large public organization (70,000 employees) to build a solution to automatically detect malicious behavior of its employees manipulating their data. To build the AI software we had also designed a system to stream data from each employee terminal into a central database for processing. This infrastructure was not present at the beginning of the project since before the need for malicious detection was arised, the organization never really realized the necessity to gather certain data: a simple login and logout time was all the needed to monitor the activity of their employees (which company folder/file they accessed etc. was not important). This is a common situation and most of the companies' infrastructure are usually not ready to be used directly with AI solutions: their current infrastructure was simply designed with other objectives in mind. For sake of completeness, most companies decide to invest their internal resources in other areas of the business rather than crm or expensive data structures. There is no blame on this choice, at the end any business has to be profitable and investing in infrastructure is not always easy to quantify the return of investment. If anything, this article should have given an idea of the major pitfalls approaching AI projects which can be summarized as follows: • AI solutions are not out of the shelves , ready made software that can be immediately put in use: they often require new skilled hires within the client organization and potentially a plan how to re-utilized part of the workforce. • It is often a myth that AI solutions will necessarily replace the employees although it is possible that they have to be retrained. • Any AI project works on data and infrastructure which are necessary to benefit the new solutions. Before embarking on AI projects an organization has to either budget in a new infrastructure or at the very least an upgrade of the one in use. In essence, due to the implication on both employees and infrastructure, AI adoption should be considered as a digital transformation process more than a software development project. After the overwhelming hype of attention of the recent years, I would expect that in the next 2-3 years more companies will start to realize what AI projects really are and how to best use them.

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Spotlight

Mascon Global

Mascon Global Limited (MGL) is a global provider of IT and ITES services with offices and software development centers across USA, Mexico, Europe, and Asia. MGL has been assisting companies accomplishing their goals by providing a wide range of technology solutions by leveraging its domain and business expertise and strategic alliances with leading technology providers.

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