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What the growth of AIops solutions means for the enterprise

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Without exaggeration, digital transformation is moving at breakneck speed, and the verdict is that it will only move faster. More organizations will migrate to the cloud, adopt edge computing and leverage artificial intelligence (AI) for business processes, according to Gartner.

Fueling this fast, wild ride is data, and this is why for many enterprises, data — in its various forms — is one of its most valuable assets. As businesses now have more data than ever before, managing and leveraging it for efficiency has become a top concern. Primary among those concerns is the inadequacy of traditional data management frameworks to handle the increasing complexities of a digital-forward business climate.

The priorities have changed: Customers are no longer satisfied with immobile traditional data centers and are now migrating to high-powered, on-demand and multicloud ones. According to Forrester’s survey of 1,039 international application development and delivery professionals, 60% of technology practitioners and decision-makers are using multicloud — a number expected to rise to 81% in the next 12 months. But perhaps most important from the survey is that “90% of responding multicloud users say that it’s helping them achieve their business goals.”

Managing the complexities of multicloud data centers

Gartner also reports that enterprise multicloud deployment has become so pervasive that until at least 2023, “the 10 biggest public cloud providers will command more than half of the total public cloud market.”

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But that’s not where it ends — customers are also on the hunt for edge, private or hybrid multicloud data centers that offer full visibility of enterprise-wide technology stack and cross-domain correlation of IT infrastructure components. While justified, these functionalities come with great complexities. 

Typically, layers upon layers of cross-domain configurations characterize the multicloud environment. However, as newer cloud computing functionalities enter into the mainstream, new layers are required — thus complicating an already-complex system.

This is made even more intricate with the rollout of the 5G network and edge data centers to support the increasing cloud-based demands of a global post-pandemic climate. Ushering in what many have called “a new wave of data centers,” this reconstruction creates even greater complexities that place enormous pressure on traditional operational models. 

Change is necessary, but considering that the slightest change in one of the infrastructure, security, networking or application layers could result in large-scale butterfly effects, enterprise IT teams must come to terms with the fact that they cannot do it alone.

AIops as a solution to multicloud complexity

Andy Thurai, VP and principal analyst at Constellation Research Inc., also confirmed this. For him, the siloed nature of multicloud operations management has resulted in the increasing complexity of IT operations. His solution? AI for IT operations (AIops), an AI industry category coined by tech research firm Gartner in 2016.

Officially defined by Gartner as “the combination of big data and ML [machine learning] in the automation and improvement of IT operation processes,” the detection, monitoring and analytic capabilities of AIops allow it to intelligently comb through countless disparate components of data centers to provide a holistic transformation of its operations. 

By 2030, the rise in data volumes and its resulting increase in cloud adoption will have contributed to a projected $644.96 billion global AIops market size. What this means is that enterprises that expect to meet the speed and scale requirements of growing customer expectations must resort to AIops. Else, they run the risk of poor data management and a consequent fall in business performance. 

This need creates a demand for comprehensive and holistic operating models for the deployment of AIops — and that is where Cloudfabrix comes in.

AIops as a composable analytics solution

Inspired to help enterprises ease their adoption of a data-first, AI-first and automate-everywhere strategy, Cloudfabrix today announced the availability of its new AIops operating model. It is equipped with persona-based composable analytics, data and AI/ML observability pipelines and incident-remediation workflow capabilities. The announcement comes on the heels of its recent release of what it describes as “the world-first robotic data automation fabric (RDAF) technology that unifies AIops, automation and observability.”

Identified as key to scaling AI, composable analytics give enterprises the opportunity to organize their IT infrastructure by creating subcomponents that can be accessed and delivered to remote machines at will. Featured in Cloudfabrix’s new AIops operating model is a composable analytics integration with composable dashboards and pipelines.

Offering a 360-degree visualization of disparate data sources and types, Cloudfabrix’s composable dashboards feature field-configurable persona-based dashboards, centralized visibility for platform teams and KPI dashboards for business-development operations. 

Shailesh Manjrekar, VP of AI and marketing at Cloudfabrix, noted in an article published on Forbes that the only way AIops could process all data types to improve their quality and glean unique insights is through real-time observability pipelines. This stance is reiterated in Cloudfabrix’s adoption of not just composable pipelines, but also observability pipeline synthetics in its incident-remediation workflows.

In this synthesis, likely malfunctions are simulated to monitor the behavior of the pipeline and understand the probable causes and their solutions. Also included in the incident-remediation workflow of the model is the recommendation engine, which leverages learned behavior from the operational metastore and NLP analysis to recommend clear remediation actions for prioritized alerts. 

To give a sense of the scope, Cloudfabrix’s CEO, Raju Datla, said the launch of its composable analytics is “solely focused on the BizDevOps personas in mind and transforming their user experience and trust in AI operations.”

He added that the launch also “focuses on automation, by seamlessly integrating AIops workflows in your operating model and building trust in data automation and observability pipelines through simulating synthetic errors before launching in production.” Some of those operational personas for whom this model has been designed include cloudops, bizops, GitOps, finops, devops, DevSecOps, Exec, ITops and serviceops.

Founded in 2015, Cloudfabrix specializes in enabling businesses to build autonomous enterprises with AI-powered IT solutions. Although the California-based software company markets itself as a foremost data-centric AIops platform vendor, it’s not without competition — especially with contenders like IBM’s Watson AIops, Moogsoft, Splunk and others.

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AI

AI-powered personalization: The key to unlocking ecommerce growth

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The ecommerce scene has evolved. Every other business is progressing online and trying to scoop some internet-driven profit. 

While certain enterprise tools can help digital-first businesses, increasing competition means that barely existing online or running half-hearted campaigns isn’t enough for brands to make it big in ecommerce. They need to up their marketing game and reinvent how they deal with their customers. Modern businesses need to predict their customers’ requirements and be proactive with solutions. They need to tailor their messages to be relevant. 

Fortunately, technology has not left them to do all of this alone. 

Artificial intelligence, more popularly known as AI, helps businesses revolutionize how they interact with their customers and allows them to carve out their portion of online success. This revolutionizing technology empowers brands to step up their game by offering what customers demand: personalization. 

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Introducing AI-powered personalization

AI grew beyond fantasy writers’ brains and sci-fi movies a long time ago. Yet many brands still fail to leverage its true potential, which restricts their growth. 

We are not living in just the era of AI. The world has successfully entered the era of advanced AI. 

Today, brands use artificial intelligence to tailor their offerings, personalize experiences and increase revenue. With the rapid adoption of AI-powered personalization, many ecommerce businesses are looking forward to shaping a brighter, more successful online future.

AI-driven personalization uses machine learning, deep learning, natural language processing, etc. to personalize a brand’s marketing messages, content, products and services. This technology reshapes how brands interact with their customers and sets them up for more profitable customer journeys. 

Personalization, of all kinds, is inherently rooted in data. 

The point where AI hits home is its ability to mine, read and draw conclusions from huge volumes of data. Brands with deeper access to data can personalize more accurately. And by doing so, they can deliver optimal customer experiences, the crux of all modern business-customer interactions. 

How AI-powered personalization helps ecommerce businesses

AI-powered personalization has multiple use cases within the ecommerce industry. And when used to its full potential, it leads to numerous benefits that we will discuss below. 

Targeted marketing: Lower marketing spend, higher revenue

Forty percent of customers prefer targeted ads aligned to what they are looking for to make the buying process easier. This could be why behaviorally targeted ads are twice as effective as non-targeted ads. AI helps marketers run more targeted brand campaigns, which eventually helps them decrease their marketing spend and increase their revenue. 

How does targeting increase revenue? With AI working behind the scenes, your ad reaches people who are most likely to take action somewhere down the line. This makes the ad impressions worth the money spent on acquiring them. 

AI digs through significant volumes of data and offers better customer segmentation. It also helps create more impactful ads based on previous ad performances. With these conclusions, marketers can reduce their ad spend by deliberately avoiding the ads that are nothing but money-draining pits. Additionally, AI helps to create and deliver tailored marketing content, like blogs, ad messages, CTAs, videos and much more. 

There are numerous AI-powered reporting tools available that collect, sort and draw valuable insights from audience data, helping you understand your customer better. 

Once you know that your audience is more receptive and responsive to a certain type of content — for example, video tutorials over written blogs — you will spend on creating the former and save your marketing budget from being spent on the latter. 

An SaaS startup deployed AI to tailor its marketing messages. The company personalized the images sent via emails to their subscribers, and as a result, the CTR from their opened emails increased four times

That’s why personalization matters. 

Despite all the benefits of targeted marketing, 76% of marketers fail to use behavioral data to run targeted ads campaigns. This statistic points toward the opportunity for brands to adopt AI-powered ad personalization and gain a competitive edge. 

Personalized product recommendations increase order value

When you are an ecommerce merchant, you want your existing customers to keep buying from you because we know upselling to existing customers is more profitable than acquiring new customers. 

AI-powered personalized product recommendation helps you do just that. When your customers visit your website, the AI algorithm picks up their buying behavior, previous transactions, demographic, interests and other such data. With this information, the website presents relevant and unique suggestions to the visitor, increasing their chances of buying more than what they came for. 

Put yourself in your customers’ shoes to grasp this concept. 

Say you have been looking for new clothes and finally on an apparel ecommerce store you find some shirts you like. As you are considering one of these shirts, the web page starts recommending some nice pants to go with them. You suddenly feel these pants are a good match for the shirts you like. This whole process brings the convenience of ordering both items from the same store, and hence you end up adding the pants to your cart. This is how personalized product recommendations increase order value, which eventually reflects in your revenue. 

A shoe retailer from London experienced an 8.6% increase in add-to-cart rate after serving personalized recommendations that appeared when a customer added one item to their cart. 

Personal touch: Excellent customer service via chatbots 

Personalization is just one part of building a pleasant customer experience. Offering excellent customer service is the other side of the coin. 

The modern customer is spoiled. Seventy-five percent of customers expect to be taken care of within five minutes. 

AI-powered chatbots come to the rescue here. And before you dismiss this, saying robotic, monotonic responses have become a thing of the past, know that the chatbots we are talking about today run on advanced AI. These chatbots are programmed to imitate humans as much as possible and are not limited to generic, scripted, rule-based conversations. 

Modern AI chatbots deploy NLP, sentiment analysis and other AI techniques to understand not just the meaning but the context, emotion and nuance behind each query. By doing so, they can carry a more resounding and contextually-accurate conversation with the customer and solve their problems. 

When a chatbot offers a tailored response for each unique query, this one chatbot becomes enough to compensate for an army of customer support representatives. Apart from offering a pleasant user experience, these chatbots also allow customers to enjoy the “self-service” they prefer. It solves their problems without making them wait for human representatives and may increase lead generations and conversions. 

A public transportation company that caters to thousands of customers every day increased its bookings by 25% and saved $1M in customer service within a year of deploying a chatbot designed to be its customer support representative. 

Optimized search leads to better customer experience

Optimized search learns from customers’ buying behavior and previous searches to customize search results in real time. 

Optimized search also deploys NLP to understand the nuances of human language and display accurate results. For example, it understands that “running shoes” and “runner’s shoes” both mean the same thing and hence would display similar results. When customers come across the results they want to see; they are more likely to be happy with your service. And it’s the customer’s experience that we are pursuing, aren’t we?

Final words

Customers these days switch loyalties based on the experiences brands offer. AI-driven personalization helps you offer tailored services, serve relevant content to customers and enjoy more profitable business relationships. But along with all its benefits, advanced AI adoption has its complexities. It can be expensive and may demand training. 

However, these complexities might be worth the results that you are likely to enjoy after the successful deployment of AI. 

Atul Jindal is a web design and marketing specialist.

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AMD CEO: Growth opportunities still big in spite of ‘flattish’ PC market

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Advanced Micro Devices CEO Lisa Su said that reported its growth opportunities and chances to take market share from rival Intel remain strong in spite of a slowing PC client market.

She made the remarks in an earnings call as Santa Clara, California-based AMD reported revenues and earnings for the third quarter ended September 30 exceeded expectations, with revenue growing 54% to a record $4.3 billion.

“We delivered our fifth straight quarter of greater than 50 percent year-over-year revenue growth with each of our businesses growing significantly year-over-year and data center sales more than doubling,” Su said.

She said the PC market is strong in terms of end-user demand, but component shortages mean that the market will likely be “flattish” in the fourth quarter. Su said supply constraints will likely last into the first half of the year, and that the PC market will be flat as a result.

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But fourth-quarter demand will be helped by semi-custom chip sales, which are higher with stronger demand for the Microsoft and Sony game consoles. AMD makes the main chips for those consoles.

Lisa Su of AMD gets the Noyce Award.

Above: Lisa Su of AMD gets the Noyce Award.

Image Credit: SIA

AMD Lisa Su said that Ryzen 5000 processor shipments increased by a double-digit percentage sequentially, as Acer, Asus, HP and Lenovo all increased their mobile laptop offerings.

Su said that cryptocurrency mining-related sales generated negligible revenue.

“It’s not a segment we have been servicing. We have tried very much to keep our gaming graphics focused on gamers,” Su said.

Demand has exceeded supply in year two of the game consoles shipping, Su said. She mentioned that year four is usually the peak year for console sales. She said there were multiple growth drivers across PC, datacenter, and consoles. During the quarter, datacenter and graphics sales more than doubled from a year ago. On top of that, datacenter graphics chip sales grew significantly.

AMD’s Epyc server chips are getting into cloud computing deployments at Cloudflare, Vimeo, and Netflix, and servers targeting the enterprise are coming from Dell, HPE, Lenovo, Supermicro, Cisco, and others. AMD has a datacenter event coming on November 8. Workstation chip sales are also strong.

More threatening to rival Intel, Su said, “We expanded our wins in the quarter with Fortune 1000 financial services, automotive and
aerospace companies and see significant ongoing growth opportunities as our enterprise server pipeline.”

AMD is also getting supercomputer wins. But Su said the PC client market looks flat in terms of revenues in the fourth quarter.

AMD Radeon RX 6800M

Above: AMD Radeon RX 6800M

Image Credit: AMD

AMD said it has gained revenue for six consecutive quarters in client PC chps, and it has had record results in servers for six quarters as well. That implies that AMD is gaining market share, though the company did not claim that this quarter.

Su said that the $35 billion Xilinx acquisition is on track to close by the end of the year. Su said she expects strong cloud and enterprise demand for AMD’s Epyc chips in 2022. AMD has Zen4 chips coming in that time frame.

“We always expect the competition to be strong, but our focus is always consistent execution of our roadmap,” Su said.

Overall, AMD has had a good run on momentum behind its Zen and Zen 2 architectures for processors, which can generate 50% or more better performance per clock cycle than the previous generation. This architecture put AMD ahead of Intel in performance for the first time in a decade, and it has helped the perennial No. 2 PC chip maker into a fast-growing contender against Intel.

In the past couple of years, Intel has had stumbles not only on the chip design side but also in manufacturing, where it has lost its technological advantage to rivals such as TSMC, which makes both processors and graphics chips for AMD. As a result, AMD has been making historic market share gains for the past three years. What’s interesting is AMD has been making these gains amid a historic chip shortage driven by the supply whipsaw from the pandemic and unprecedented demand for electronic goods.

Intel, by comparison, reported it could see a slowdown in Q4 amid problems such as slowing demand in China. Su said she saw a normal environment for demand in places like China.

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AI Weekly: AI adoption is driving cloud growth

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The adoption of cloud technologies continues to accelerate. According to the newest report from Canalys, in Q2 2021, companies spent $5 billion more on cloud infrastructure services compared to the previous quarter. While a number of factors are responsible, including an increased focus on business resiliency planning, the uptick illustrates the effect AI’s embracement has had — and continues to have — on enterprise IT budgets.

In a recent survey, 80% of U.S. enterprises said they accelerated their AI adoption over the past two years. A majority consider AI to be important in their digital transformation efforts and intend to set aside between $500,000 to $5 million per year for deployment efforts. Organizations were projected to invest more than $50 billion in AI systems globally in 2020, according to IDC, up from $37.5 billion in 2019. And by 2024, investment is expected to reach $110 billion.

The cloud is playing a role in this due to its potential to improve AI training and inferencing performance, lowering costs and in some cases providing enhanced protection against attacks. Most companies lack the infrastructure and expertise to implement AI applications themselves. As TierPoint highlights, outside of corporate datacenters, only public cloud infrastructure can support massive data storage as well as the scalable computing capability needed to crunch large amounts of data and AI algorithms. Even companies that have private datacenters often opt to avoid ramping up the hardware, networking, and data storage required to host big data and AI applications. According to Accenture global lead of applied intelligence Sanjeev Vohra, who spoke during VentureBeat’s Transform 2021 conference, the cloud and data have come together to give companies a higher level of compute, power, and flexibility.

Cloud vendor boost

Meanwhile, cloud vendors are further stoking the demand for AI by offering a number of tools and services that make it easier to develop, test, enhance, and operate AI systems without big upfront investments. These include hardware optimized for machine learning, APIs that automate speech recognition and text analysis, productivity-boosting automated machine learning modeling systems, and AI development workflow platforms. In a 2019 whitepaper, Deloitte analysts gave the example of Walgreens, which sought to use Microsoft’s Azure AI platform to develop new health care delivery models. One of the world’s largest shipbuilders is using Amazon Web Services to develop and manage autonomous cargo vessels, the analysts also noted. And the American Cancer Society uses Google’s machine learning cloud services for automated tissue image analysis.

“The symbiosis between cloud and AI is accelerating the adoption of both,” the analysts wrote. “Indeed, Gartner predicts that through 2023, AI will be one of the top workloads that drive IT infrastructure decisions. Technology market research firm Tractica forecasts that AI will account for as much as 50% of total public cloud services revenue by 2025: AI adoption means that, ‘essentially, another public cloud services market will be added on top of the current market.’”

With the global public cloud computing market set to exceed $362 billion in 2022 and the average cloud budget reaching $2.2 million today, it appears clear that investments in the cloud aren’t about to slow down anytime soon. As long as AI’s trajectory remains bright — and it should — the cloud industry will have an enormous boom from which to benefit.

For AI coverage, send news tips to Kyle Wiggers — and be sure to subscribe to the AI Weekly newsletter and bookmark our AI channel, The Machine.

Thanks for reading,

Kyle Wiggers

AI Staff Writer

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Go read this story on how Facebook’s focus on growth stopped its AI team from fighting misinformation

Facebook has always been a company focused on growth above all else. More users and more engagement equals more revenue. The cost of that single-mindedness is spelled out clearly in this brilliant story from MIT Technology Review. It details how attempts to tackle misinformation by the company’s AI team using machine learning were apparently stymied by Facebook’s unwillingness to limit user engagement.

“If a model reduces engagement too much, it’s discarded. Otherwise, it’s deployed and continually monitored,” writes author Karen Hao of Facebook’s machine learning models. “But this approach soon caused issues. The models that maximize engagement also favor controversy, misinformation, and extremism: put simply, people just like outrageous stuff.”

On Twitter, Hao noted that the article is not about “corrupt people [doing] corrupt things.” Instead, she says, “It’s about good people genuinely trying to do the right thing. But they’re trapped in a rotten system, trying their best to push the status quo that won’t budge.”

The story also adds more evidence to the accusation that Facebook’s desire to placate conservatives during Donald Trump’s presidency led to it turning a blind eye to right-wing misinformation. This seems to have happened at least in part due to the influence of Joel Kaplan, a former member of George W. Bush’s administration who is now Facebook’s vice president of global public policy and “its highest-ranking Republican.” As Hao writes:

All Facebook users have some 200 “traits” attached to their profile. These include various dimensions submitted by users or estimated by machine-learning models, such as race, political and religious leanings, socioeconomic class, and level of education. Kaplan’s team began using the traits to assemble custom user segments that reflected largely conservative interests: users who engaged with conservative content, groups, and pages, for example. Then they’d run special analyses to see how content-moderation decisions would affect posts from those segments, according to a former researcher whose work was subject to those reviews.

The Fairness Flow documentation, which the Responsible AI team wrote later, includes a case study on how to use the tool in such a situation. When deciding whether a misinformation model is fair with respect to political ideology, the team wrote, “fairness” does not mean the model should affect conservative and liberal users equally. If conservatives are posting a greater fraction of misinformation, as judged by public consensus, then the model should flag a greater fraction of conservative content. If liberals are posting more misinformation, it should flag their content more often too.

But members of Kaplan’s team followed exactly the opposite approach: they took “fairness” to mean that these models should not affect conservatives more than liberals. When a model did so, they would stop its deployment and demand a change. Once, they blocked a medical-misinformation detector that had noticeably reduced the reach of anti-vaccine campaigns, the former researcher told me. They told the researchers that the model could not be deployed until the team fixed this discrepancy. But that effectively made the model meaningless. “There’s no point, then,” the researcher says. A model modified in that way “would have literally no impact on the actual problem” of misinformation.

The story also says that the work by Facebook’s AI researchers on the problem of algorithmic bias, in which machine learning models unintentionally discriminate against certain groups of users, has been undertaken, at least in part to preempt these same accusations of anti-conservative sentiment and forestall potential regulation by the US government. But pouring more resources into bias has meant ignoring problems involving misinformation and hate speech. Despite the company’s lip service to AI fairness, the guiding principle, says Hao, is still the same as ever: growth, growth, growth.

[T]esting algorithms for fairness is still largely optional at Facebook. None of the teams that work directly on Facebook’s news feed, ad service, or other products are required to do it. Pay incentives are still tied to engagement and growth metrics. And while there are guidelines about which fairness definition to use in any given situation, they aren’t enforced.

You can read Hao’s full story at MIT Technology Review here.



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AI

Sinequa: Third-party marketplaces boost hyperscale cloud growth

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Cloud enterprise professionals are drawn to the “big three” hyperscale clouds — Amazon Web Services, Google Cloud, and Microsoft Azure — because of the third-party products and services available. In a survey of cloud professionals in France, United Kingdom, and United States, 93% of respondents reported the strength and breadth of third-party marketplaces make hyperscale clouds “more attractive as a platform,” according to research from Sinequa, an intelligent search and analytics platform provider.

Above: Respondents expect to increase their use of blockchain, search and analytics, and AI and machine learning.

Image Credit: Sinequa

This new research highlights the importance of third-party marketplaces and demonstrates the competitive advantage that third-party offerings bring to the “big three” hyperscale clouds: Microsoft Azure, Amazon Web Services, and Google Cloud. On average, the survey found enterprise organisations are running eight different services bought from a hyperscale marketplace, and 90% of respondents plan to purchase more products and services from these marketplaces in the future.

When asked about the main benefits of using a hyperscale marketplace, 70% of cloud professionals reported that convenience was a key advantage. The ability to easily try new products and services was also an important feature for 50% of respondents, while 39% reported that the choice of products available was a top reason for using a marketplace. These findings point to the advantage hyperscale cloud providers have in being able to offer more products and services than smaller competitors.

As well as highlighting the benefits of hyperscale marketplaces, the research revealed which product or service categories are seeing the greatest increase in demand. Blockchain tops the list, with companies expecting to increase their use of blockchain by 112% in the future. Search and Information Gathering or Insights solutions are also in demand, with companies expected to increase their use of these tools by 43% going forwards. This trend reflects the need for high performance solutions that integrate and improve the accessibility of information across multiple applications, as companies shift towards a hybrid working model.

Sinequa ran a survey of 250 UK, US, and French cloud professionals who reported they worked at enterprises or large businesses. The survey, which consisted of 10 questions, ran on the 19th April 2021.

Read more about Sinequa’s research.

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Tech News

How Apple reportedly gave up control of iCloud for business growth in China

China is one of the biggest markets in the world for Apple’s products. In its recent quarterly results, the company registered a whopping $17.7 billion in iPhone sales in the region.

However, this stellar business performance comes at a cost of user privacy and ceding control over its own ecosystem. According to a new report from The New York Times, Apple gave in to China’s multiple demands, including custom hardware for iCloud and app removals.

The report noted that Tim Cook caved in to China’s demand of storing iCloud data of China-based customers in the country —Apple wanted to keep that data in the US. While storing user data locally is a common practice across the globe, Apple allegedly handed over iCloud’s encryption key to China and made it easier to retrieve user data.

This is unlike Apple in the US, where it has constantly battled with authorities to keep their hands off iPhone users’ data. The NYT report noted that the iPhone maker created a special loophole to give the government access to data: it partnered with a government-affiliated Guizhou-Cloud Big Data as a service provider. Plus, it made changes to the iCloud service agreement that included the clause, “Apple and GCBD will have access to all data that you store on this service.”

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These growth marketing courses can boost your Instagram brand to over 10K followers

This is the training to boost your brand to over 10,000 Instagram followers fast.

TLDR: The 2021 Instagram Growth Marketing Bundle can help novice Instagram brands become online marketing powerhouses, growing followers and profits from the social platform.

If you thought teenage girls, celebrity influencers, and overly enthusiastic grandparents were the only ones on Instagram, you haven’t been paying attention. Sure, pictures of celebrities doing their thing and every meal ever served fill typical Instagram feeds. But brands that aren’t paying attention to the Insta-revolution are missing out big time.

With more than 1 billion monthly active users, Instagram has become a marketing bonanza for brands ready to engage and reap the benefits. Over 80 percent of Instagram users say they use the site to research products and services, while over 90 percent follow at least one brand on Instagram.

With that many potential dollars in play, The 2021 Instagram Growth Marketing Bundle ($19.99, over 90 percent off, from TNW Deals) is a focused primer for businesses who want to take advantage of Instagram’s reach and boost brand awareness, engagement, and ultimately, sales.

Across six courses, learners can get an up-close look at how to navigate the Instagram platform, create compelling content, increase the number of followers, and hopefully turn interested passers-by into long-term customers.

For those struggling to even get off the ground, the Instagram Marketing for Instagram Business Beginners course is a prime launching pad to learn the absolute Insta basics. The step-by-step training can help brands identify the audience they want to find, communicate visually to those users, and build your reputation as an authoritative voice in your feed, all via Instagram posting.

Meanwhile, courses like Complete Instagram Marketing Course: From 0 to 10,000 Followers lay out a concrete gameplan for helping any Instagram account grow its following both organically and quickly. Then How to Turn Instagram into a Business and Monetize a Following explains how to translate that growing fanbase into profits, as well as how to turn that Instagram account into its own money-making operation.

There’s also the Instagram Marketing 2021 Master Class, which expands this training into next steps, including how to use special Instagram features only available to users with over 10,000 followers.

Valued at $1,200, the training in The 2021 Instagram Growth Marketing Bundle is now on sale at an incredibly low price, just $19.99 while this offer lasts.

Prices are subject to change.



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Gartner says low-code, RPA, and AI driving growth in ‘hyperautomation’

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Research firm Gartner estimates the market for hyperautomation-enabling technologies will reach $596 billion in 2022, up nearly 24% from the $481.6 billion in 2020.

Gartner is expecting significant growth for technology that enables organizations to rapidly identify, vet, and automate as many processes as possible and says it will become a “condition of survival” for enterprises. Hyperautomation-enabling technologies include robotic process automation (RPA), low-code application platforms (LCAP), AI, and virtual assistants.

As organizations look for ways to automate the digitization and structuring of data and content, technologies that automate content ingestion, such as signature verification tools, optical character recognition, document ingestion, conversational AI, and natural language technology (NLT), will be in high demand. For example, these tools could be used to automate the process of digitizing and sorting paper records.

Gartner currently anticipates the hyperautomation market reaching $532.4 billion this year.

Drivers of growth

Gartner said process-agnostic tools such as RPA, LCAP, and AI will drive the hyperautomation trend because organizations can use them across multiple use cases. Even though they constitute a small part of the overall market, their impact will be significant, with Gartner projecting 54% growth in these process-agnostic tools.

Through 2024, the drive toward hyperautomation will lead organizations to adopt at least three out of the 20 process-agonistic types of software that enable hyperautomation, Gartner said.

The demand for low-code tools is already high as skills-strapped IT organizations look for ways to move simple development projects over to business users. Last year, Gartner forecast that three-quarters of large enterprises would use at least four low-code development tools by 2024 and that low-code would make up more than 65% of application development activity.

Software automating specific tasks, such as enterprise resource planning (ERP), supply chain management, and customer relationship management (CRM), will also contribute to the market’s growth, Gartner said.

Lots of potential use cases

Hyperautomation extends the idea of intelligent automation, as it promises end-to-end process automation with minimal human intervention required. The convergence of intelligent process automation technologies and cloud computing, along with the need to process unstructured content, helps make the case for hyperautomation across several industries, including shared services, hospitality, logistics, and real estate.

Some day-to-day examples of automation include self-driving cars, self-checkouts at grocery stores, smart home assistants, and appliances. Business use cases include applying data and machine learning to build predictive analytics that react to consumer behavior changes and implementing RPA to streamline operations on a manufacturing floor.

Gartner earlier included hyperautomation in its Top 10 Strategic Technology Trends for 2021.

Benefits of hyperautomation

Gartner said tools that provide visibility to map business activities, automate and manage content ingestion, orchestrate work across multiple systems, and provide complex rule engines make up the fastest-growing category of hyperautomation-enabling software. Organizations will be able to lower operational costs 30% by 2024 through combining hyperautomation technologies with redesigned operational processes, Garner projected.

“Hyperautomation has shifted from an option to a condition of survival,” Gartner VP Fabrizio Biscotti said in a statement. “Organizations will require more IT and business process automation as they are forced to accelerate digital transformation plans in a post-COVID-19, digital-first world.”

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