Meeting the Challenges of Multi Cloud Service Provider Deployments

| October 23, 2018

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As Service providers move Enterprise applications from centralized, monolithic architectures into distributed, micro service applications running in data centers, private/public clouds and uCPE assuring not only the network but the application level experience becomes paramount. Verifying that new 5G services are realizing higher bandwidth, increased reliability and (ultra) lower latency over 5G and IoT networks will be much harder. Operators need to think about how they will deliver application level experience. They will need real-time lightweight visibility up to layer 7 in uCPE, mobile edge, data center, SD-WAN and other cloud architectures. Dr. Vikram Saksena of NETSCOUT discusses how carriers can successfully assure the seamless migration of these services to multi-cloud environments.

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Nimble Storage

"The Nimble Storage Predictive Flash Platform combines flash performance with predictive analytics to predict and prevent barriers to data velocity. Customers experience absolute performance, non-stop availability and cloud-like agility that accelerate critical business processes. Over 7,500 enterprises, governments, and service providers have deployed the Predictive Flash Platform. "

OTHER ARTICLES

What’s new in Kubernetes 1.19?

Article | August 18, 2020

Kubernetes 1.19 is about to be released! And it comes packed with novelties. However, there’s something beyond the features that grabbed our attention this time. Where do we begin? Kubernetes as a project is maturing, support has been increased from nine to 12 months, and there’s a new protocol in place to ensure a steady progress on feature development. Also, many of its new features are meant to improve the quality of life of its users, like Generic ephemeral inline volumes, or the structured logging.

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The 6 Biggest AI Marketing Challenges & Their Solutions

Article | July 8, 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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Top 5 Data Visualization JavaScript Libraries in 2021

Article | January 20, 2021

When you work with data, the most prominent challenge is picking the right manner to represent data in a readable format. With proper visualization, it becomes easier to convey the message present in the analyzed data. The best choice, in this case, is to use Data Visualization. By definition, data visualization is the graphical representation of the data and information. Using elements like maps, charts, graphs, etc. gives an easier view to understanding the patterns, trends, outliers, and performance. But, choosing the right tool for visualization can become difficult with the number of choices made available in the market. To save you from the trial and error method for finding the tool that works amazingly, here is the list of the best JavaScript libraries that are about to simplify the way you used to interact with data. Apart from these libraries, there are some tools for javascript which you can learn about and get more advanced with data visualization. Best JavaScript Libraries For Data Visualization For assisting you in getting started with data visualization in any application, here is the list of the top 5 libraries of JavaScript that you can get started with. D3js D3 (Data-Driven Documents) is among the most popular javascript libraries. It is not only limited to the visualization of data but also helps in data analytics, data utilities, animations, and geo. It works regardless of the JavaScript frameworks being used. D3 uses SVG, CSS, and HTML to create visualization as per web standards. Some of the benefits of using D3js include large dataset support, flexibility, and transition functionality. C3js Combination Chart or C3 is based on D3 and provides reusable chart libraries for applications. C3 offers a class to each element to help in defining a custom style that can later extend structure from D3. One of the major benefits of using C3js is that you can even update the rendered chart. ReCharts Similar to C3, ReCharts also uses D3 while expressing declarative components. Being lightweight and rendering on SVG elements, it helps in creating stunning charts. The library consists of some beautiful chart examples. Moreover, the charts here can also be customized according to needs. It is great for static charts but can lag with multiple animations. With an intuitive API, it becomes highly powerful and responsive. CanvasJS Canvas is an HTML5 and JavaScript chart library that’s easy to use. It smoothly runs on different platforms with 100,000+ data points. It comes with four default themes that can be greatly suitable for different conditions. Canvas charts provide high performance, fast developer support, and do not depend on other libraries. Highcharts Based on SVG JS library charts, Highcharts is quite popular among large organizations. Highcharts comes with the entire ecosystem for various project templates. It is even compatible with old browsers. Even the non-developers can use it with ease because of its interactive chart editor. It is being used by popular brands like Microsoft. Conclusion With the help of these javascript libraries, you will find it easy to present a large amount of data visually. What’s more, these will work with any framework. If you are a non-developer and still want data visualization in your application, then you can easily hire developers with relevant experience out there.

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TOP 5 TIPS FOR ADOPTING ENTERPRISE MACHINE LEARNING

Article | June 18, 2020

When you first got your business off the ground, you may or may not have paid much attention to the technologies that would be available to you in the years to come—like machine learning. Machine learning was the stuff of science fiction just decades ago; now it’s practically everywhere. So, what is machine learning? Simply put, machine learning is a subset of artificial intelligence in which computer algorithms learn from large datasets in order to make more accurate predictions over time. Obviously, it’s a lot more complicated than that, but it poses numerous benefits to business owners—assuming it’s used the right way. Here are five tips for successfully adopting machine learning technologies in your day-to-day operations.

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Spotlight

Nimble Storage

"The Nimble Storage Predictive Flash Platform combines flash performance with predictive analytics to predict and prevent barriers to data velocity. Customers experience absolute performance, non-stop availability and cloud-like agility that accelerate critical business processes. Over 7,500 enterprises, governments, and service providers have deployed the Predictive Flash Platform. "

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