Cloud is a technology that has risen exponentially in recent years and which has been buoyed by the switch to remote working and increased need for off-site storage and support during the pandemic. However, with increased adoption, executive leaders are wanting cloud computing adoption to proceed at a faster pace than their organisations can staff internally.

To meet this challenge, business leaders must consider the following five steps if they are to successfully manage their cloud projects effectively.

To handle the skills gap, first support, monitor and measure the progress of the management team against cloud skills initiatives. To facilitate this progress, ensure roles across the business are divided by expertise, allowing for achievable hiring goals. For example, for such a cloud project, a programme or portfolio management approach will be needed.

By dividing responsibilities, the organisation will need a programme management office (PMO) that works with the cloud centre of excellence (CCOE) to drive the approach effectively, rather than demanding that cloud architects also possess project management skills.

Upskill existing staff with cloud skills, using relationship-based and experiential learning from experts. For more technical staff members, their skillset must span different IT domains.

Also, ensure that the technical leaders overseeing your organisations cloud initiatives are strategic thinkers with business acumen, big-picture perspectives and team-player mindsets who can communicate with diverse audiences and be agile in thought and action.

To reinforce the project, consider permanently hiring new employees with the necessary skills for the cloud project. New employees will also have to take time to learn the business and IT environment, but recruitment can be streamlined toward key experienced hires who accelerate such cloud computing initiatives.

In supplementing internal processes, look to bring on contractors from staffing agencies or hire independent contractors. This can be a useful way to acquire junior and mid-level people to perform cloud-related tasks and staff cloud projects.

Senior-level contractors can also be excellent partners and one of the swiftest and most effective ways to acquire the necessary skills. A key consideration, however, is to avoid allowing such senior-level contractors to make strategy or policy decisions.

The final step is to procure external service provider (ESP) assistance, usually in the form of a cloud managed service provider (MSP). This can be taken as a project-based approach, or a medium-to-long-term managed services approach. Selection must be careful, with large differences existing in ESP skills and experience and low-quality assistance often seriously reducing satisfactory cloud outcomes.

Lydia Leong is aGartner distinguished vice-president research analyst

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Gartner: How to overcome a lack of cloud skills in your organisation - ComputerWeekly.com

The French Supervisory Authority (the CNIL) regularly conducts investigations based on various triggering events such as a complaint, an article or its annual program that the CNIL regularly publishes on its website.

On 15 February 2022, published a post regarding its upcoming dawn raids for 2022.

As a reminder, in 2021, the CNILs priority topics were (i) the cybersecurity of the French websites, (ii) the security of the health data and (iii) compliance with the rules applicable to cookies and other trackers further to its recommendations and guidelines released in March 2021.

For 2022, the CNIL has decided to focus its attention on three topics which should represent one third of the dawn raids to be carried out. One of the topics relates to marketing rules, which are regularly monitored by the CNIL due to the number of claims received. Another key topic is teleworking which developed with the pandemic. The third topic is the use of the cloud services, which is becoming an increasingly sensitive issue within the EU in particular in relation to transfers to the US.

Further to a public consultation, the CNIL has published the final version of its reference framework for the processing of personal data in the context of Commercial Management. This reference framework includes the applicable legal basis for each type of processing activity, the relevant data retention terms and the technical and organizational measures to ensure the security of the personal data. The CNIL refers to the several guidelines it has published to help the various stakeholders, notably in relation to direct marketing (email, SMS, phone, post), commercial management, cookies and others trackers, attendance measurement systems, behavioral targeting, online payments, and templates of information notices.

The CNIL also provides further guidance regarding the transmission of personal data, with or without remuneration, to business partners (e.g., data brokers, data resellers) and will particularly check the compliance of these professionals and intermediaries with the CNILs guidance in this reference framework.

With the COVID-19 pandemic, and the return to normal life, the use of telework has developed with specific tools, including those enabling employers to monitor the teleworking of employees.

Since 2020, the CNIL provided several rules and good practices notably through a Q&A regarding the teleworking, advising employers on the implementation of reinforced security measures to ensure the security of the personal data processed by employees who telework and precautions to take when using visio conference tools, to ensure the right balance between privacy at work and employers legitimate interest to monitor its employees work.

As a result, employers allowing teleworking have to ensure that such practice does not infringe the GDPR and the CNILs guidance.

Given the use of cloud computing technologies in all sectors, the CNIL is particularly concerned by the massive international data transfers to third countries that cloud computing technologies entail, as well as the risk of data breaches.

Regarding, the international data transfers, the CNIL has announced that it will be looking in greater detail the conditions of data transfers and the contractual framework implemented between companies (acting as data controllers) and cloud solution providers (acting as processor).

At the same time, the EDPB released a press release informing the launch of coordinated enforcement on the use of cloud technologies in the public sector. In France, the CNIL has already announced its investigations into five ministries. Therefore, the public sector stakeholders appears to be the priority for the CNIL, notably on the following subjects: process and safeguards implemented when acquiring cloud services, challenges related to international transfers, and provisions governing the controller-processor relationship.

However, private sector stakeholders should not become complacent, as investigations could potentially shift to the private sector. It is therefore important to get prepared and start the compliance review and risk assessment process in relation to any international data transfers that may occur in the context of processing activities using cloud computing.

Regarding possible data breaches that may occur in the context of cloud computing technologies, the CNIL confirmed that this is an issue which is under scrutiny, but did not detail the points of control. We can anticipate that the CNILs agents will check the record of breaches, and the decisions to whether or not to notify a breach, and will review the related documentation, including the existence of an internal data breach procedure in compliance with the French Data Protection Law.

Controllers and processors should therefore get prepared and document their various compliance efforts for 2022!

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France: the CNIL has released its annual dawn raid Program for 2022: three key priorities! - Lexology

Edge vs. Cloud: Which AI Infrastructure to Opt For?

Edge computing is all the buzz these days. Touted as the most exciting technology shift in recent years, discussions about its transformative powers are aplenty! With increasingly powerful AI/ML algorithms redefining intelligence and the availability of cheaper more powerful edge devices, the hype is turning out to be largely real. But if one were to consider the history of edge computing, it goes further back than what the recent interest would have us believe. In fact, computing and intelligence first started at the edge at a time when high bandwidth network connections were virtually non-existent for most applications. Even in the late 1990s, critical measurement devices deployed remotely in a plant or a field often had dedicated computational capability for processing incoming sensor data. The algorithms in these devices were however only rudimentary in their intelligence: mostly signal processing or data transformations. With improvements in network capability and increased connectivity, cloud-based computing started to gain traction in the late 2000s. In parallel, powerful AI algorithms rose to prominence as a means to unlock meaningful information from swathes of structured and unstructured data. In just about a decade, cloud AI had become the go-to choice for AI applications. But the shift to the cloud brought with it several concerns as well: data upload and download costs, network reliability, and data security to name a few. At the same time, the trade-off for edge computing between processing capability and cost or footprint was diminishing with the rise of affordable yet powerful edge devices. It seems that we have now come full circle back to considering edge computing as a viable and attractive option for building intelligent applications.

As the debate rages on about which option is better edge AI or cloud AI, anyone who is familiar with these two frameworks would likely respond that it depends!. The reason is that edge and cloud infrastructures are not competing but complementary frameworks. Both have seen tremendous evolution and sophistication in the last few years, particularly as a base for AI development and deployment. As with any technology selection, the choice really boils down to the specific application: the objectives, value drivers, and economics as well as any constraints on power, footprint, and connectivity. Thus, it is imperative to understand the pros and cons of both cloud and edge AI before attempting to build the right infrastructure.

Cloud-based AI is an attractive choice when seeking flexibility, scalability, and ease of deployment. Most cloud service providers today provide robust frameworks for training and deployment of AI models along with pay-as-you-go packages with little to no upfront commitment or investment. The cloud offers computational and storage options with few limitations, making it particularly suited for large AI models. But it can become an unwieldy option for real-time applications that require continuous assessment of sensor or image data as they incur significant costs by having to stream data back and forth. This data transfer also makes the cloud largely unsuited for low latency applications requiring closed-loop control or immediate actions.

Edge AI on the other hand is the logical choice for real-time data analysis for automated alarms or closed-loop control. While edge infrastructure does require an upfront investment in edge hardware, the operational costs are significantly lower compared to that for the cloud. Today, a wide variety of edge AI hardware options are available including NPUs (neural processing units), TPUs (tensor processing units) as well as SoCs (system on chip) and SoMs (system on module) with dedicated AI accelerators. Low cost and low power hardware for AI is an area of active research and is likely to provide superior options going forward. On the flip side, AI-based consumer applications have to deal with rather diverse edge devices (mobiles, tablets, PCs, etc), making edge deployment a potentially daunting prospect. Hence, edge infrastructure may not be conducive to rapid prototyping and doesnt scale as easily either. While federated learning, the concept of distributed training for AI models allows for both training and deployment on the edge, the cloud remains the logical choice for training large models requiring adequate computational power.

But the solution doesnt necessarily have to be an either-or choice. As applications transition to a more microservices-based architecture, they can be broken down into smaller functionalities or microservices with their own specific deployment framework. So instead of having to choose between cloud and edge, the focus can be on using both optimally for a specific application. For example, an application might start off with a quick prototype on the cloud. As it evolves, functionalities that require low latency and real-time decisions can be transitioned to the edge, while those that require scale and flexibility can be retained in the cloud. Model training or re-training can be centrally managed in the cloud while some federated learning on the edge can increase accuracy locally. Similarly, sensitive data can be processed on the edge and more generic data relegated to the cloud.

Organizations, developers, and practitioners would do well to think of the cloud and the edge not as distinct alternatives, but really as a continuum from edge to cloud with many diverse infrastructure options in between. That includes different types of edges operational edge, network edge, mobile endpoint, etc, and different types of distributed processing on the network private cloud, public cloud, cloudlet, fog computing, and so on. While the complexity can be a challenge, finding the right mix of technologies is starting to present a unique opportunity for organizations to maximize the value of AI while simultaneously minimizing the cost and risk.

Anusha Rammohan, Member of The IET and Senior Technology Leader, Myelin Foundry

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SAN FRANCISCO, Feb. 23, 2022 /PRNewswire/ --The global digital transformation market size is estimated to reach USD 1,759.4 billion by 2028, registering a CAGR of 23.6% over the forecast period, according to a new report by Grand View Research, Inc. The increasing adoption of advanced technologies, such as the Internet of Things (IoT) and machine learning, across several industries/sectors, is encouraging businesses to implement connected, analytical, and data-rich solutions via digital transformation. These solutions are capable of encapsulating intelligence into business operations to facilitate improved and more effective customer engagements. Moreover, the growing usage of mobile devices, smartphones, and applications across business functions is increasingly promoting digitization.

Key Insights & Findings from the report:

Read 110-page market research report, "Digital Transformation Market Size, Share & Trends Analysis Report By Type (Solution, Service), By Deployment (Hosted, On-premise), By Enterprise Size (Large, SME), By End Use (BFSI, Healthcare), And Segment Forecasts, 2021 - 2028", published by Grand View Research.

Digital Transformation Market Growth & Trends

Digital transformation enables organizations to mitigate business risks and manage disruptions, such as corporate restructuring, marketplace fluctuations, unforeseen pandemics, and unpredictable geopolitical environments, more efficiently. It enables organizations to address a range of process optimization. Moreover, it enables businesses to target a larger customer base by designing new business models, products, and services leveraging digitization. Furthermore, it helps facilitate the transformation of business activities, traditional processes, and business models to benefit from upcoming changes and opportunities pertaining to futuristic technologies.

The rapid urbanization and digitization across the globe are enabling organizations to embrace digital transformation using technology-driven solutions. The growing usage of cloud technology by various Small & Medium Enterprises (SMEs) helps them adopt modern Digital Experience Platforms (DXP) at reasonable costs without constant upgradation or replacement of systems. This is estimated to drive the market during the forecast period. Moreover, benefits, such as improvements in operational flexibility and overall customer experience, are encouraging digital transformation initiatives across SMEs, driving the market.

Digital Transformation Market Segmentation

Grand View Research has segmented the global digital transformation market on the basis of type, deployment, enterprise size, end-use, and region:

Digital Transformation Type Outlook (Revenue, USD Billion, 2016 - 2028)

Digital Transformation Deployment Outlook (Revenue, USD Billion, 2016 - 2028)

Digital Transformation Enterprise Size Outlook (Revenue, USD Billion, 2016 - 2028)

Digital Transformation End-use Outlook (Revenue, USD Billion, 2016 - 2028)

Digital Transformation Regional Outlook (Revenue, USD Billion, 2016 - 2028)

List of Key Players of Digital Transformation Market

Check out more related studies published by Grand View Research:

Browse through Grand View Research's Next Generation Technologies Industry Research Reports.

About Grand View Research

Grand View Research, U.S.-based market research and consulting company, provides syndicated as well as customized research reports and consulting services. Registered in California and headquartered in San Francisco, the company comprises over 425 analysts and consultants, adding more than 1200 market research reports to its vast database each year. These reports offer in-depth analysis on 46 industries across 25 major countries worldwide. With the help of an interactive market intelligence platform, Grand View Research Helps Fortune 500 companies and renowned academic institutes understand the global and regional business environment and gauge the opportunities that lie ahead.

Contact:

Sherry JamesCorporate Sales Specialist, USAGrand View Research, Inc.Phone: 1-415-349-0058Toll Free: 1-888-202-9519Email: [emailprotected]Web: https://www.grandviewresearch.comGrand View Compass| Grand View PipelineFollow Us: LinkedIn | Twitter

SOURCE Grand View Research, Inc.

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Digital Transformation Market Size Worth $1,759.4 Billion By 2028: Grand View Research, Inc. - PRNewswire

Royal Philips (NYSE:PHG, AEX:PHIA) bet big on the cloud in 2021, buying Capsule Technologies for $635 million to integrate Capsules medical device integration and data technologies with Philips cloud-based digital health platform.

The Philips Capsule Medical Device Information Platform (MDIP) can now integrate with more than 1,000 unique types of medical devices, the Dutch medtech giant said in October.

To help understand whats possible in medtech with the cloud,Medical Design & Outsourcing asked two leaders at Philips Virtual Care Solutions Business Category Leader Christine Stormand Informatics Transformational Programs Leader Bas Kuppens to offer their perspectives. The following exchange has been edited for space and clarity.

MDO: What are some surprising and inspirational examples of what cloud computing enables with regard to medical device design, manufacturing and capabilities/performance?

STORM: When the pandemic started, health systems were forced to pivot and adapt to new technologies to improve patient and staff experiences and safety. Solutions that were slowly evolving suddenly became in high demand. From here came the realization that health systems were not well-equipped to respond to rapidly changing care needs, largely due to the fragmented, vulnerable, and expensive IT landscape in which they operate.

When it comes to healthcares digital transformation, the need for effective data sharing across technologies and systems has increasingly become essential in helping our customers improve clinical and operational outcomes. Standardization is key to radically simplify IT infrastructures and bring transparency and predictability to total cost of ownership. When this new era starts to take shape, individual point solutions will have limited value and will need to be replaced with interconnected solutions that offer clinicians the right data at the right time.

In an emerging environment dependent on connectivity and agility, two key developments have emerged in the race to achieve connected care through digital transformation: platform approach and cloud computing. The shift to digital platforms is not new to many industries, but healthcare is playing catch-up, as many healthcare organizations are not yet equipped with IT infrastructures that can keep up with the pace of change. In fact, according to a recent McKinsey report, more than 30% of global economic activity could be mediated by digital platforms in six years, yet experts estimate only 3% of established companies or institutes have adopted an effective platform strategy.

Cloud technology can help deliver a healthcare system that is much more interconnected, scalable and modular. This will allow providers and systems to adjust to continuously evolving needs and shift the focus from the procedure to the patient. Through the cloud, data can be accessed anytime and anywhere an essential requirement for new models of care delivery where patient information must be visible in care settings including the home, outpatient clinics, and traditional hospital environments. Combining data from across these care settings can offer more insights to caregivers, enabling them to make more informed decisions.

MDO: What are some previously unthinkable advances that now seem increasingly likely to become reality?

STORM: Connecting massive systems of information and fragmented sources of data onto one continuous platform has proven to be a challenge historically, but the healthcare industry has made staggering advances. To seamlessly connect people, workflows and information using informatics, healthcare organizations will increasingly transition to secure, scalable cloud technology platforms that house integrated informatics solutions to consolidate and veer away from individual point solutions.

In healthcare, there are many data sources holding personal, operational and clinical data such as EMR, Laboratory, ECG, PACS or other data lakes. At the same time, many patients and caregivers are trying to access the information using applications or solutions along the care pathways but are struggling to access this data due to a very fragmented IT infrastructure. Digital platforms, such as Philips HealthSuite, provide the capabilities to unlock and integrate the data from various sources, and allow caregivers and patients to interact seamlessly with this data, and gain actionable insights from it.

MDO: And finally, what sort of big, futuristic dreams do these or other advancements inspire?

KUPPENS: Digital platforms are the enabling technology that can bring us closer to achieving the virtual hospital of the future, with patient and consumer-centric care delivery. This technology will enable caregivers to unlock the power of data and tie it together through ecosystem services and extract value using smart algorithms, allowing for more informed decisions. The cloud will facilitate access from across a patients care continuum, from anywhere. This allows us to work closely with our customers to develop meaningful solutions that give patients, their families and caregivers a better experience and drive better clinical and operational outcomes along the way.

As part of this transformation journey, the healthcare IT industry will go through several steps. Today, healthcare institutions often implement multi-point solutions to solve specific workflow needs. Because these solutions are all on-premise occupying scarce real estate and adding to the burden of owning the technology stacks, interoperability and inability to take a patient-centric view on data remain a challenge.

Many healthcare institutions are entering the phase of migrating these point solutions to the cloud. This allows caregivers to extract value from data coming from multiple care settings and consume innovation much more quickly and efficiently. Subsequently, ecosystems will start to grow, further unlocking data and tapping into the scalability of cloud computing.

As a last step in this journey, further modularization of platform capabilities will take place. This will pave the way for a composable healthcare enterprise, built from a series of loosely coupled platform capabilities or microservices. These microservices can be used as building blocks to construct true customer-specific solutions and will help to further accelerate innovation in healthcare.

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Philips is betting big on the cloud; two leaders discuss the connected future - Medical Design & Outsourcing