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		<title>Mastering Cloud Migration Costs: A Deep Dive</title>
		<link>https://www.clouddatainsights.com/mastering-cloud-migration-costs-a-deep-dive/</link>
					<comments>https://www.clouddatainsights.com/mastering-cloud-migration-costs-a-deep-dive/#respond</comments>
		
		<dc:creator><![CDATA[Jonathan LaCour]]></dc:creator>
		<pubDate>Mon, 11 Nov 2024 15:28:03 +0000</pubDate>
				<category><![CDATA[Cloud Data Platforms]]></category>
		<category><![CDATA[cloud costs]]></category>
		<category><![CDATA[Cloud strategy]]></category>
		<guid isPermaLink="false">https://www.clouddatainsights.com/?p=5525</guid>

					<description><![CDATA[Find out what it takes to master cloud migration costs and position your organization for continued success in the cloud.]]></description>
										<content:encoded><![CDATA[
<figure class="wp-block-image size-full"><img fetchpriority="high" decoding="async" width="1000" height="657" src="https://www.clouddatainsights.com/wp-content/uploads/2024/10/Depositphotos_7626840_S.jpg" alt="cloud migration costs" class="wp-image-5526" srcset="https://www.clouddatainsights.com/wp-content/uploads/2024/10/Depositphotos_7626840_S.jpg 1000w, https://www.clouddatainsights.com/wp-content/uploads/2024/10/Depositphotos_7626840_S-300x197.jpg 300w, https://www.clouddatainsights.com/wp-content/uploads/2024/10/Depositphotos_7626840_S-768x505.jpg 768w" sizes="(max-width: 1000px) 100vw, 1000px" /></figure>



<p>As a CTO who&#8217;s overseen numerous cloud migrations, I&#8217;ve observed a persistent challenge across the industry: budget overruns. Despite widespread awareness of potential pitfalls and intentions to &#8220;get it right the first time,&#8221; many organizations still struggle to keep costs under control during their cloud migration.</p>



<p>In this article, I&#8217;ll delve into the intricate and often complex world of cloud migration costs. We&#8217;ll explore why budgets still derail, uncover the hidden complexities that catch even experienced teams off guard, and provide practical strategies to keep your migration on track financially.</p>



<h3 class="wp-block-heading">Navigating the Complexities of Upfront Cloud Migration Costs</h3>



<p>While the long-term benefits of cloud migration are well-documented, the journey begins with significant upfront investments. However, it&#8217;s crucial to recognize that cloud service providers (CSPs) are eager to facilitate these transitions and often offer financial incentives to accelerate migrations.</p>



<h4 class="wp-block-heading">Explore Funding Options</h4>



<p>To leverage these opportunities, I recommend working closely with your preferred CSP to understand all available funding options. CSPs often connect you with preferred consulting partners and may fund a portion of their cost, enabling a quicker migration without overly burdening your IT teams.</p>



<h4 class="wp-block-heading">Perform a Comprehensive Cost Assessment</h4>



<p>When budgeting for a cloud migration, consider all associated costs. This includes the number of person-hours dedicated to the migration effort, operational costs of workloads in transition, and investments in new tooling.</p>



<h4 class="wp-block-heading">Establish Clear Metrics</h4>



<p>In my experience, establishing clear metrics is crucial. Define measurable metrics to track migration success against budget, including expected benefits, performance against existing baseline, CSP funding maximization, and infrastructure cost alignment. Continuous tracking and reporting ensure you stay on course during the migration, and quickly adapt to new information.</p>



<h3 class="wp-block-heading">Why Budgets Still Go Off the Rails</h3>



<p>Despite increased awareness, organizations continue to struggle with cost management during cloud migrations. Several factors contribute to this persistent challenge:</p>



<h4 class="wp-block-heading">Common Misconceptions about Cloud Pricing Models</h4>



<p>Public cloud utility pricing offers numerous benefits, but the pricing models are often highly sophisticated. They feature high granularity and multiple dimensions across every service, making accurate cost projections challenging.</p>



<h4 class="wp-block-heading">Overlooked Factors in Migration Planning</h4>



<p>Accurate cost projection requires discipline and meticulous planning. While perfection isn&#8217;t attainable, identifying the main drivers of infrastructure costs in advance is critical. Key considerations include core elements of compute and storage, networking costs, and understanding cost models for your most-used services (e.g., EC2, RDS, S3, and EBS in AWS for non-cloud-native workloads).</p>



<h4 class="wp-block-heading">The Ripple Effect of Initial Decisions on Long-term Costs</h4>



<p>Early migration decisions can have far-reaching financial implications. On-demand pricing is typically the least cost-effective for large-scale workloads. While committed pricing programs offer significant savings, they require upfront payments or spending commitments. As a conservative approach, I suggest initially estimating ROI based on on-demand pricing, and then revisiting the potential cost savings that can be recognized through committed pricing.</p>



<p>The choice between speed and optimization can significantly impact long-term costs. A rapid lift-and-shift approach may be necessary for time-sensitive migrations but could result in higher long-term costs. Extensive workload modernization during migration can extend timelines but may lead to quicker ROI realization.</p>



<p>In my experience, the optimal strategy often involves a mix of rehosting, re-platforming, relocating, and retiring, tailored to align with your specific business strategy and ROI goals.</p>



<h3 class="wp-block-heading">Sneaky Cost Drivers in Cloud Migration</h3>



<p>Several factors can lead to unexpected costs during and after a migration.</p>



<h4 class="wp-block-heading">Complex Pricing Models</h4>



<p>Public cloud pricing is highly sophisticated, with multiple dimensions and layers across every service. This complexity makes accurate cost projection challenging, especially for practitioners accustomed to traditional on-premises cost structures. To navigate this:</p>



<ul class="wp-block-list">
<li>Develop a deep understanding of the pricing models for your most-used services. For non-cloud-native workloads, this often means focusing on core services like compute, storage, and database offerings.</li>



<li>Pay attention to nuanced pricing elements such as data transfer costs between availability zones or regions, API call charges, and storage class transition fees.</li>
</ul>



<h4 class="wp-block-heading"><strong>Network Transit Costs</strong>:&nbsp;</h4>



<p>One of the most surprising cost factors I&#8217;ve encountered in cloud migrations is often network-related expenses. While ingress (inbound data transfer) is typically cheap or free, egress (outbound data transfer) can be surprisingly expensive. This becomes particularly significant in scenarios with high data output requirements, operations across multiple regions or cloud providers, or architectures involving frequent data movement between services or to external networks. In these cases, seemingly minor data transfer fees can accumulate rapidly, potentially leading to unexpected budget overruns.</p>



<p>To manage these costs effectively:</p>



<ul class="wp-block-list">
<li>Conduct a thorough analysis of your workloads&#8217; network requirements, both north-south (in and out of the cloud) and east-west (between services within the cloud).</li>



<li>Consider the impact of network topology on your costs. Cloud Service Providers offer an extensive range of network services that enable many network architectures. When making decisions, you will again need to be armed with data to select the architecture that best meets your business needs while still remaining on budget.</li>
</ul>



<h3 class="wp-block-heading">Strategic Approaches to Long-Term Cost Optimization</h3>



<p>Effective cloud cost management requires both accurate initial estimations and strategic long-term planning. To improve the accuracy of your cost projections, I recommend prioritizing estimation efforts on the most-used services first and in the greatest detail, while broader estimates may be adequate for less-utilized services. While spreadsheets can be useful for quick estimates, consider using dedicated cloud cost estimation tools. Many CSPs offer such tools for free or at a low cost, providing greater accuracy with less time investment.</p>



<p>As you plan for the long term, balance strategic architectural decisions with careful trade-offs. High availability and reliability in the cloud come with exponentially increasing costs for each additional &#8220;9&#8221; of uptime. Similarly, while cloud providers offer numerous performance optimization options, these often introduce significant costs that may not be justified. Remember: don&#8217;t build a Ferrari when a Prius will meet your needs. Align your choices with specific workload requirements and business strategy to avoid over-engineering at the expense of cost efficiency.</p>



<p>When it comes to pricing models, leverage committed pricing options, but time your commitments carefully. If your migration strategy involves an initial lift-and-shift followed by modernization, avoid long-term commitments that might lock you into specific timelines or architectures. Once workloads are modernized and optimized, consider longer-term commitments to maximize discounts with lower risk. This staged approach to commitments allows for flexibility during the migration and optimization phases while still capturing cost benefits in the long run.</p>



<h3 class="wp-block-heading">Mastering Cloud Migration Costs: Key Takeaways for Long-Term Success</h3>



<p>Successfully managing cloud migration costs requires a nuanced understanding of complex pricing models, careful planning, and strategic decision-making. By focusing on the major cost drivers, leveraging available tools and funding options, and aligning migration strategies with long-term business goals, organizations can navigate the financial challenges of cloud migration more effectively. Remember, the goal is not just to migrate to the cloud, but to do so in a way that maximizes ROI and positions your organization for long-term success in the cloud.</p>
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		<post-id xmlns="com-wordpress:feed-additions:1">5525</post-id>	</item>
		<item>
		<title>What 2024&#8217;s Data Management Report Means for Business</title>
		<link>https://www.clouddatainsights.com/what-2024s-data-management-report-means-for-business/</link>
					<comments>https://www.clouddatainsights.com/what-2024s-data-management-report-means-for-business/#respond</comments>
		
		<dc:creator><![CDATA[Elizabeth Wallace]]></dc:creator>
		<pubDate>Fri, 08 Nov 2024 13:35:49 +0000</pubDate>
				<category><![CDATA[Data Architecture]]></category>
		<category><![CDATA[data management]]></category>
		<category><![CDATA[report]]></category>
		<guid isPermaLink="false">https://www.clouddatainsights.com/?p=5570</guid>

					<description><![CDATA[Discover key trends from Ocient's 2024 Data Management Report, from cost control to sustainability, and what they mean for business leaders navigating today’s tech landscape.]]></description>
										<content:encoded><![CDATA[
<figure class="wp-block-image size-full"><img decoding="async" width="1000" height="551" src="https://www.clouddatainsights.com/wp-content/uploads/2024/11/Depositphotos_545083080_S.jpg" alt="Data management report" class="wp-image-5571" srcset="https://www.clouddatainsights.com/wp-content/uploads/2024/11/Depositphotos_545083080_S.jpg 1000w, https://www.clouddatainsights.com/wp-content/uploads/2024/11/Depositphotos_545083080_S-300x165.jpg 300w, https://www.clouddatainsights.com/wp-content/uploads/2024/11/Depositphotos_545083080_S-768x423.jpg 768w" sizes="(max-width: 1000px) 100vw, 1000px" /></figure>



<p>In Ocient&#8217;s latest &#8220;Beyond Big Data&#8221; report, the dual forces of opportunity and challenge shape the current landscape of data analytics and IT infrastructure. While 100% of organizations recognize the value of scaling their data analysis for business outcomes, nearly 36% struggle to fully harness their data&#8217;s potential due to complexities in data management and analysis processes. As data volumes grow, so too do the challenges associated with transforming raw data into actionable insights.</p>



<p>Here’s what the report can tell us about the current state of data management and what might be coming down the line.</p>



<p>See also: <a href="https://www.clouddatainsights.com/scaling-up-how-multi-tech-data-platforms-enhance-data-management/" target="_blank" rel="noreferrer noopener">How Multi-Tech Data Platforms Enhance Data Management</a></p>



<h3 class="wp-block-heading">Tackling Cost and Complexity in Data Management</h3>



<p>The report highlights a significant shift in how organizations approach data analytics, driven by the unpredictable costs that often hinder innovation. With 68.3% of respondents regularly surprised by their data analytics spending and 40% feeling constrained by budget limits, the financial aspect of data management remains a critical barrier. In response, there has been a notable increase, from 35% last year to 41% this year, in organizations transitioning away from outdated systems to modern platforms that better support advancements like GenAI. This movement is not only about keeping up with technological advancements but also about seeking more predictable and manageable costs associated with data analytics.</p>



<h3 class="wp-block-heading">Shifting Trends in IT Infrastructure</h3>



<p>The adoption trends of IT infrastructure are also evolving. The dominance of cloud-only solutions is waning, with only 25% of respondents continuing to invest exclusively in cloud architectures, down from 35% the previous year. This shift reflects a growing preference for on-premises and hybrid models, where organizations can maintain greater control over their data and better predict operational costs. The report also introduces new concerns around energy consumption, with 53% of respondents now viewing the energy demands of data and AI workloads as a primary concern. This shift towards energy-conscious IT is driving more companies to consider sustainable technologies and practices that align with broader environmental goals, such as reducing carbon emissions and achieving net-zero targets.</p>



<h3 class="wp-block-heading">Navigating the Technological Landscape: Implications for Businesses</h3>



<p>The findings from the &#8220;Beyond Big Data&#8221; report offer a clear snapshot of the current environment for businesses striving to stay competitive in a rapidly evolving technological landscape. These insights are not just about understanding the status quo but about recognizing the shifts that dictate how businesses must adapt to remain at the forefront of innovation. Here&#8217;s what the current environment looks like and what it demands from companies.</p>



<h4 class="wp-block-heading">Rapid Technological Advancements</h4>



<p>The push towards adopting modern platforms that support GenAI and other advanced analytics tools indicates the rapid pace at which technology is advancing. Businesses are expected to keep up and be agile enough to pivot as new technologies emerge. This requires an ongoing investment in tech upgrades and a willingness to phase out legacy systems that no longer serve the company&#8217;s strategic interests.</p>



<h4 class="wp-block-heading">Increased Demand for Flexibility</h4>



<p>The shift from cloud-only infrastructures to a more balanced approach with on-premises and <a href="https://www.channelfutures.com/cloud/more-enterprises-want-hybrid-cloud-not-public">hybrid models</a> reflects a broader demand for flexibility in data management. The need for greater control over data security, compliance, and cost management drives this trend. Businesses must adapt to this demand by evaluating their infrastructure choices through the lens of flexibility and control, ensuring they can quickly respond to changes in market conditions or regulatory environments.</p>



<h4 class="wp-block-heading">Financial Pressure and Budget Constraints</h4>



<p>The unpredictability of costs associated with data management and analytics is a significant concern. It pressures businesses to find more stable and predictable cost structures to manage their budgets more effectively. This financial pressure is accelerating the search for innovative solutions that can reduce operational costs, such as more energy-efficient data centers and AI-driven analytics that streamline operations.</p>



<h4 class="wp-block-heading">Sustainability as a Strategic Imperative</h4>



<p>With a significant uptick in concerns about the energy consumption of data and AI workloads and a strong commitment to carbon neutrality, sustainability has transitioned from a corporate social responsibility initiative to a strategic imperative. Businesses must integrate sustainability into their core operations, not only to meet regulatory and consumer expectations but also to benefit from the associated cost savings and improved brand reputation.</p>



<h4 class="wp-block-heading">The Growing Importance of Data Governance</h4>



<p>As businesses collect and utilize more data, the importance of robust data governance grows. This involves not only the security and compliance aspects but also the efficient management and utilization of data. Effective data governance ensures that businesses can maximize the value of their data assets while mitigating risks related to data breaches and compliance violations.</p>



<p>Overall, the &#8220;Beyond Big Data&#8221; report captures a landscape where data analytics is increasingly seen as both a strategic asset and a complex challenge to be managed. It underscores the ongoing need for organizations to balance innovation with cost, control, and sustainability. The upcoming year promises further developments as companies continue to navigate these challenges, potentially leading to more diversified data management strategies and continued investment in sustainable IT solutions.</p>
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		<post-id xmlns="com-wordpress:feed-additions:1">5570</post-id>	</item>
		<item>
		<title>How AI is Driving The Shift to a Private Cloud</title>
		<link>https://www.clouddatainsights.com/how-ai-is-driving-the-shift-to-a-private-cloud/</link>
					<comments>https://www.clouddatainsights.com/how-ai-is-driving-the-shift-to-a-private-cloud/#respond</comments>
		
		<dc:creator><![CDATA[Keith Pijanowski]]></dc:creator>
		<pubDate>Sun, 03 Nov 2024 01:23:53 +0000</pubDate>
				<category><![CDATA[AI/ML]]></category>
		<category><![CDATA[private cloud]]></category>
		<guid isPermaLink="false">https://www.clouddatainsights.com/?p=5555</guid>

					<description><![CDATA[Discover why AI and private cloud adoption go hand in hand. Hint: It has a lot to do with increasing operational costs.]]></description>
										<content:encoded><![CDATA[
<figure class="wp-block-image size-full"><img decoding="async" width="1000" height="750" src="https://www.clouddatainsights.com/wp-content/uploads/2024/11/Depositphotos_128704210_S.jpg" alt="AI and private cloud adoption" class="wp-image-5557" srcset="https://www.clouddatainsights.com/wp-content/uploads/2024/11/Depositphotos_128704210_S.jpg 1000w, https://www.clouddatainsights.com/wp-content/uploads/2024/11/Depositphotos_128704210_S-300x225.jpg 300w, https://www.clouddatainsights.com/wp-content/uploads/2024/11/Depositphotos_128704210_S-768x576.jpg 768w" sizes="(max-width: 1000px) 100vw, 1000px" /></figure>



<p>The innovations made possible by Generative AI are incredibly promising, which is why the market now stands at <a href="https://www.zdnet.com/article/ai-is-a-9-trillion-market-and-enterprises-have-barely-begun-to-touch-it/">nearly $9 trillion</a>. Since the technology hit the mainstream, organizations have been clamoring to build and implement AI tools that drive new levels of efficiency and innovation. In order to do so, these organizations require massive amounts of data. It also requires compute power made possible only through the use of graphics processing units (GPUs), which are processors that were initially designed for graphics processing (hence the name) but have since been modified to support the floating point calculations needed to train and host models.</p>



<p>See also: <a href="https://www.clouddatainsights.com/why-people-choose-public-private-hybrid-or-multi-cloud-solutions/">Why People Choose Public, Private, Hybrid, or Multi-Cloud Solutions</a></p>



<p>Public cloud providers saw this GPU need and their limited availability and bought all the GPUs they could from the likes of Nvidia, AMD, and Intel. As a result, most organizations turned to these public cloud providers to train and host their AI models and the applications that depend on them. This had the effect of turning the investments needed to create an AI data infrastructure into an operational expenditure as opposed to a capital expenditure if that same infrastructure were built on-premise. </p>



<p>Operational expenditures, in the context of an AI data infrastructure, are optimal when you want to get started quickly and you do not need a lot of compute and storage. However, as your business grows you will need more resources and it is basic economics that tells us that at some point, operational expenditures exceed the cost of purchasing the resources being used. Let’s call this resource limit the “operational breaking point.”</p>



<p>Today, GPU availability is improving. Control is becoming more important as data privacy and AI safety become more important. Finally, many organizations have reached their operational breaking point. The result &#8211; a resurgence of the private cloud.</p>



<h3 class="wp-block-heading">The Cost of Public vs Private Cloud AI</h3>



<p>The fact is that the success of the public cloud is built on availability, convenience, and elasticity. It was not and is not built on economics. The cost of the public cloud is now much more than what companies originally bargained for years ago when they invested in the solution. Back in 2021, <a href="https://www.spglobal.com/market-intelligence/en/news-insights/research/can-private-clouds-ever-really-compete-with-the-public-cloud">S&amp;P research</a> found that once you hit a certain level of scale, the private cloud became cheaper than the public cloud based on a combination of labor efficiency and utilization. At the time, it did not seem likely that many large-scale migrations would occur. That was until AI hit the industry. Now companies are rapidly scaling workloads to meet the demand for AI, and private cloud is becoming the new operating model of choice.&nbsp;</p>



<p>The public cloud has standardized the adoption of seeing the cloud as an operating model, rather than a destination. The cloud operating model, or cloud-native, means you build for portability and prevent your services from getting locked into one cloud vendor. This can be hard to do &#8211; all public clouds have hundreds of custom services they use to lock customers to their platform. With the cloud operating model, forward-thinking organizations realize that what you start in the public can be moved to the private cloud. This is a much more cost-effective solution.&nbsp;</p>



<h3 class="wp-block-heading">The Cloud Operating Model for AI is Private</h3>



<p>With the <a href="https://www.appen.com/whitepapers/state-of-ai-2024?utm_source=pr&amp;utm_medium=promotion&amp;utm_campaign=state_of_ai_2024">GenAI adoption surging 17% in 2024</a>, we are now seeing companies looking for new solutions to data management challenges that have popped up accompanying that sharp increase. More and more CEOs and boards are coming to the same conclusion—the importance of AI to their organizations is existential, and therefore, it is essential that they address these challenges. To do so they need control over their data, the compute needed for model training, and the models themselves. Large enterprises in regulated industries especially want to keep their data close to the chest. That very data, after all, is their secret sauce.&nbsp;</p>



<p>With data privacy and security concerns associated with AI, more regulations are designed to hold organizations accountable for the data they collect. his point of control is quickly becoming imperative.</p>



<p>By moving to a co-location facility or on-premise deployment, enterprises can own the full cloud stack and better control costs and data. The public cloud may be a good place to start, but it is not a place to stay long term.</p>



<p>The best of both worlds is a hybrid model. This model allows organizations to prepare data for processing within a private cloud, “burst” to the public cloud to run processing on rented GPUs and then bring the data back to the private cloud once completed. While these hybrid models can be effective, it’s becoming clear that the private cloud, whether colocation or on-prem datacenter, is the path forward for AI that the CTO, CIO and CFO can all agree on.</p>
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		<post-id xmlns="com-wordpress:feed-additions:1">5555</post-id>	</item>
		<item>
		<title>Four Surprising Facts About Data Storage</title>
		<link>https://www.clouddatainsights.com/four-surprising-facts-about-data-storage/</link>
					<comments>https://www.clouddatainsights.com/four-surprising-facts-about-data-storage/#respond</comments>
		
		<dc:creator><![CDATA[Chris Opat]]></dc:creator>
		<pubDate>Fri, 25 Oct 2024 17:19:31 +0000</pubDate>
				<category><![CDATA[Cloud Data Platforms]]></category>
		<category><![CDATA[data storage]]></category>
		<guid isPermaLink="false">https://www.clouddatainsights.com/?p=5538</guid>

					<description><![CDATA[Discover some surprising facts about data storage and what might be possible for future data storage solutions.]]></description>
										<content:encoded><![CDATA[
<figure class="wp-block-image size-full"><img loading="lazy" decoding="async" width="1000" height="660" src="https://www.clouddatainsights.com/wp-content/uploads/2024/10/Depositphotos_231065252_S.jpg" alt="" class="wp-image-5539" srcset="https://www.clouddatainsights.com/wp-content/uploads/2024/10/Depositphotos_231065252_S.jpg 1000w, https://www.clouddatainsights.com/wp-content/uploads/2024/10/Depositphotos_231065252_S-300x198.jpg 300w, https://www.clouddatainsights.com/wp-content/uploads/2024/10/Depositphotos_231065252_S-768x507.jpg 768w" sizes="(max-width: 1000px) 100vw, 1000px" /></figure>



<p>For those outside the storage industry, it’s easy to dismiss the subject as dry and uninteresting. But as everyday functions continue to be digitized, everyday objects get “smarter” (that is, stuffed full of sensors), and the AI ecosystem continues to grow like wildfire throughout the business world, it’s becoming increasingly clear that ensuring quality and consistent data storage will be the muscle that keeps the modern world running. As data storage has evolved and new technologies have emerged, many fascinating facts about data storage have presented themselves.&nbsp;</p>



<p>See also: Scaling Up: <a href="https://www.clouddatainsights.com/scaling-up-how-multi-tech-data-platforms-enhance-data-management/">How Multi-Tech Data Platforms Enhance Data Management</a></p>



<h3 class="wp-block-heading">Hard Drives Technically Weigh More When Full</h3>



<p>Anyone can tell that a full file cabinet weighs more than a drive storing all the same files in digital versions. But that does raise the question if the digital storage of those files weighs anything at all. As Einstein famously theorized, e = mc<sup>2</sup>. That formula shows that energy is defined by mass. Thus, we can infer that energy has a weight, even if it’s negligible. </p>



<p>Now, hard drives record data by magnetizing a thin film of ferromagnetic material and forcing the atoms in a magnetic field to align in a different direction. Since magnetic fields have differing amounts of energy depending on whether they’re aligned or anti-aligned, technically the weight does change. According to<a href="https://www.ellipsix.net/blog/2009/04/how-much-does-data-weigh.html"> the calculations of David Zaslavsky</a>, it’d be approximately 10<sup>-14</sup> g for a 1TB hard drive. Luckily, such an amount is essentially unmeasurable. There’s no need to worry about adjusting for weight in a data center when the drives are full.</p>



<h3 class="wp-block-heading">The Cloud Can Get Really Loud</h3>



<p>One thing that people don’t often realize is that the physical data centers can run at high volumes. This is thanks to a combination of factors, largely cooling systems. Backblaze previously measured its its own data centers at approximately 78db. Other<a href="https://www.sensear.com/blog/data-centers-arent-loud-right%23:~:text=Based%2520on%2520research%2520found%2520by,to%252096%2520dB(A)."> data centers can reach up to 96dB</a> roughly the equivalent volume of a nearby motorcycle, newspaper press, or power mower, with likely hearing damage after<a href="https://www.iacacoustics.com/blog-full/comparative-examples-of-noise-levels"> 8 hours of exposure</a>.</p>



<p>It’s worth investing in ways to reduce the noise—if not for worker safety, then to reduce the environmental impact of data centers, including noise pollution. There are a wealth of studies out there connecting noise pollution to cardiovascular disease, hypertension, high stress levels, sleep disturbance, and good ol’ hearing loss in humans. In our animal friends, noise pollution can disrupt predator/prey detection and avoidance, echolocation, and interfere with reproduction and navigation. Luckily, there are technologies to keep data centers (relatively) quiet such as acoustic enclosure around loud items such as diesel generators.</p>



<h3 class="wp-block-heading">Data Storage Doesn’t Last Forever, But That Could Soon Change</h3>



<p>While files themselves don’t physically expire, the storage mediums saving them degrade over time.</p>



<p>Backblaze has<a href="https://www.backblaze.com/blog/backblaze-drive-stats-for-2023/"> extensive research available on how long drives last before failing</a>, and the findings show it can take several years before that expiration happens. While every model of drive is unique, there’s some basic time frames involved: 4–7 years for hard disk drives (HDDs), 5–10 years for solid-state drives (SSDs), and flash drives have 10 years of average use.</p>



<p>However, with new technologies—and their consumer applications—emerging, we might see these timeframes get left in the dust. The Institute of Physics reports that data written to a glass memory crystal could remain intact for a million years, a product they’ve dubbed the<a href="https://physicsworld.com/a/5d-superman-memory-crystal-heralds-unlimited-lifetime-data-storage/"> “Superman crystal.”</a> So, look out for lasers altering the optical properties of quartz at the nanoscale—we certainly will be checking them out if a customer ever asks to store their files for a million years.</p>



<h3 class="wp-block-heading">Data Centers Benefit From Expensive Real Estate</h3>



<p>Optimizing your connectivity (getting data from point A to point B) to the strongest networks is no simple feat. And, it’s important to remember that there’s a hardware element to those networks. So, where there are more people, there’s more networking infrastructure. From an operational standpoint, you’d likely assume it’s a bad choice to have your data center in the middle of the most expensive real estate and power infrastructures in the world. However, there are tangible benefits to joining up all those networks at a central hub and to putting them in or near population centers. </p>



<p>We call those spaces carrier hotels—facilities where metro fiber carriers meet long-haul carriers for dozens of network providers. As a result, those carrier hotels sit on some of the most expensive real estate in the world. Citing<a href="https://dgtlinfra.com/carrier-hotels-data-center/%23:~:text=Street,%2520Phoenix,%2520Arizona-,What%2520is%2520a%2520Carrier%2520Hotel?,platforms%2520within%2520the%2520carrier%2520hotel."> DGTL Infra</a>, the biggest carrier hotels are located in the downtowns of Los Angeles, Chicago, Dallas, Miami, New York City, and Seattle. Did you know that 80% of the traffic on the internet passes through the Dallas Infomart? It’s no wonder they have over 70 carriers to connect with in that property!</p>



<h3 class="wp-block-heading">Data Storage Is Only Going to Get More Interesting</h3>



<p>Today it’s estimated that there are over 8,000 data centers (DCs) in the world, built on a variety of storage media, connected to various networks, consuming vast amounts of power, and taking up valuable real estate. As the need for storage grows and new technologies reach the market, I’m excited to see how the industry evolves and what quirks and counterintuitive concepts emerge next.</p>
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		<post-id xmlns="com-wordpress:feed-additions:1">5538</post-id>	</item>
		<item>
		<title>Cloud Computing for Scientific Research: Ensuring Comprehensive Backup and Data Visibility</title>
		<link>https://www.clouddatainsights.com/cloud-computing-for-scientific-research-ensuring-comprehensive-backup-and-data-visibility/</link>
					<comments>https://www.clouddatainsights.com/cloud-computing-for-scientific-research-ensuring-comprehensive-backup-and-data-visibility/#respond</comments>
		
		<dc:creator><![CDATA[Sid Rao]]></dc:creator>
		<pubDate>Sun, 20 Oct 2024 00:45:57 +0000</pubDate>
				<category><![CDATA[Cloud Data Platforms]]></category>
		<category><![CDATA[cloud costs]]></category>
		<category><![CDATA[Cloud strategy]]></category>
		<category><![CDATA[scientific research]]></category>
		<guid isPermaLink="false">https://www.clouddatainsights.com/?p=5529</guid>

					<description><![CDATA[If researchers adopt cloud computing for scientific research, they could help protect against data loss and implement better analysis.]]></description>
										<content:encoded><![CDATA[
<figure class="wp-block-image size-full"><img loading="lazy" decoding="async" width="1000" height="668" src="https://www.clouddatainsights.com/wp-content/uploads/2024/10/Depositphotos_233795546_S.jpg" alt="cloud computing for scientific research" class="wp-image-5530" srcset="https://www.clouddatainsights.com/wp-content/uploads/2024/10/Depositphotos_233795546_S.jpg 1000w, https://www.clouddatainsights.com/wp-content/uploads/2024/10/Depositphotos_233795546_S-300x200.jpg 300w, https://www.clouddatainsights.com/wp-content/uploads/2024/10/Depositphotos_233795546_S-768x513.jpg 768w, https://www.clouddatainsights.com/wp-content/uploads/2024/10/Depositphotos_233795546_S-930x620.jpg 930w" sizes="(max-width: 1000px) 100vw, 1000px" /></figure>



<p>Leveraging cloud computing for scientific research can provide a valuable edge and help protect data from accidental loss. It can mean the difference between efficient data storage and a complete loss.</p>



<p>Scientific research is fundamentally about producing, collecting, and analyzing data. Researchers generate data from any number of sources during their experiments, collecting data on different test groups and variables, and those test groups can contain several subjects. Before long, the amount of data points a researcher generates, stores, and analyzes becomes staggering &#8211; often requiring petabytes of space. Not only are the space requirements large, but latency and bandwidth become important factors driving the amount of compute time required to complete the task, and compute time directly drives research costs. These factors drive acquiring a dedicated storage solution.</p>



<p>Furthermore, the loss of data collected during the experiment process could have catastrophic consequences. Experiments are expensive — both in infrastructure and labor — and funding is frequently limited. Having to redo work due to cases of data loss could eat up funding and potentially cripple a research project’s success. Data loss also impacts the ability to reproduce an experiment, hampering independent verification of the results.</p>



<h3 class="wp-block-heading">The importance of efficient data storage in scientific research</h3>



<p>Because of this, scientific researchers will want to store multiple copies of their data to ensure that if one of the copies of their data is corrupted or lost, all of their hard work will not have gone to waste. However, storing multiple data sets for comparison and reproducibility can be cost-prohibitive. Beyond this, scientific researchers will have data replication in the form of repeated experiments.&nbsp;</p>



<p>Scientists often repeat their experiments under different conditions or by their peers to ensure the validity of their findings. These factors further contribute to the sheer quantity of data produced during scientific experiments.</p>



<p>Thankfully, <a href="https://aws.amazon.com/what-is/cloud-storage/">cloud data storage</a> offers an elastic and scalable solution for scientific researchers to store their data conveniently, securely, and cost-effectively. People are likely familiar with cloud storage solutions like Google Drive and Dropbox, which are essentially remote servers managed by a third-party provider that users can access via the internet.&nbsp;</p>



<p>However, consumer-facing cloud storage solutions like these may not be suitable for enterprises that produce and store large amounts of data —&nbsp;including laboratories and universities. In these cases, a specialized enterprise cloud storage solution will be necessary to manage their data storage needs.</p>



<h3 class="wp-block-heading">Why cloud storage is an ideal solution for scientific researchers</h3>



<p>One of the main benefits of cloud storage for scientific researchers is that it is an incredibly scalable solution. When storing data on-premises, storage space is restricted. You must have a room to house the data storage infrastructure, and meet certain conditions in that room — such as temperature and humidity — to ensure that the equipment functions properly. Cloud storage outsources data storage to a third-party provider, which manages these needs.</p>



<h4 class="wp-block-heading">Improves costs</h4>



<p>In turn, this scalability allows cloud storage to be an incredibly cost-effective solution for research labs and institutions. When dealing with physical data storage like server rooms, organizations must purchase more storage than they actually need to allow for backups and contingencies, not overwork the servers, and not cause latency issues. Cloud solutions enable institutions to buy precisely as much storage as they need, and as the amount of data they need to store grows, they can purchase additional storage space.</p>



<h4 class="wp-block-heading">Enables easy access</h4>



<p>Cloud storage has also been praised for enabling easy access to data. Many cloud storage solutions offer a web portal that is convenient and easy for users to access and navigate. The latency tradeoff with cloud services can be overcome with high bandwidth, dedicated and secure links to compute resources, and high-speed caching services. It is also much faster for researchers working between locations to access their data remotely via the cloud than to travel to a data storage location.</p>



<h4 class="wp-block-heading">Protects against data loss</h4>



<p>Cloud data storage also serves as an efficient hedge against data loss in cases like natural disasters and cybersecurity threats. By storing data in a centralized location, such as a server room on- or off-premises, researchers are leaving it vulnerable to isolated incidents. For example, if a flood destroys a server room where the researcher’s only copy of their data is held, that data is lost. Similarly, ransomware attacks could cause data loss if a hacker targets a single centralized server. Data storage in the cloud ensures that multiple copies of the data are stored in different locations, significantly reducing its vulnerability to loss.</p>



<h4 class="wp-block-heading">Facilitates collaboration</h4>



<p>Finally, storing data in the cloud is also an essential step in facilitating collaboration between teams in different geographic locations. Effective scientific discovery requires the brightest minds from around the world to come together sharing ideas, hypotheses, and data. Cloud technology enables researchers to share these resources and collaborate data in real time, no matter where they are. Since the cloud can be accessed from virtually any device with an internet connection, teams can access this data anytime or anywhere they need to and keep up with instantaneous updates.&nbsp;</p>



<h3 class="wp-block-heading">Adopting cloud computing for scientific research</h3>



<p>Scientific researchers generate and analyze a vast amount of data as part of the discovery process. Cloud technologies present a practical, scalable, cost-effective solution, allowing researchers to store and access their data efficiently and safely. If your research institution or lab has not already embraced the new paradigm of cloud storage technology, now is the time to protect and preserve your data by investing in a cloud storage solution.</p>
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		<post-id xmlns="com-wordpress:feed-additions:1">5529</post-id>	</item>
		<item>
		<title>Scaling Up: How Multi-Tech Data Platforms Enhance Data Management</title>
		<link>https://www.clouddatainsights.com/scaling-up-how-multi-tech-data-platforms-enhance-data-management/</link>
					<comments>https://www.clouddatainsights.com/scaling-up-how-multi-tech-data-platforms-enhance-data-management/#comments</comments>
		
		<dc:creator><![CDATA[Salvatore Salamone]]></dc:creator>
		<pubDate>Tue, 15 Oct 2024 17:52:22 +0000</pubDate>
				<category><![CDATA[Cloud Data Platforms]]></category>
		<category><![CDATA[Sponsored]]></category>
		<category><![CDATA[Apache Kafka]]></category>
		<guid isPermaLink="false">https://www.clouddatainsights.com/?p=5502</guid>

					<description><![CDATA[Most organizations today cannot lean on just one or two data management solutions. What’s needed is a multi-tech data platform that ensures performance, security, and more. ]]></description>
										<content:encoded><![CDATA[<div class="wp-block-image">
<figure class="aligncenter size-full"><img loading="lazy" decoding="async" width="991" height="657" src="https://www.clouddatainsights.com/wp-content/uploads/2024/10/2-Depositphotos_619978250_S.jpg" alt="" class="wp-image-5504" srcset="https://www.clouddatainsights.com/wp-content/uploads/2024/10/2-Depositphotos_619978250_S.jpg 991w, https://www.clouddatainsights.com/wp-content/uploads/2024/10/2-Depositphotos_619978250_S-300x199.jpg 300w, https://www.clouddatainsights.com/wp-content/uploads/2024/10/2-Depositphotos_619978250_S-768x509.jpg 768w" sizes="(max-width: 991px) 100vw, 991px" /><figcaption class="wp-element-caption"><em>Most organizations today cannot lean on just one or two data management solutions. What’s needed is a multi-tech data platform that ensures performance, security, and more.</em></figcaption></figure></div>


<p><em>Sponsored by Instaclustr</em></p>



<p>Modern data volumes and velocities have outpaced the capabilities of traditional relational database and data management solutions. Making matters even more challenging, many organizations find they need multiple modern data solutions to support events, streaming data, and more.</p>



<p>RTInsights recently sat down with Andrew Mills, a Senior Solutions Architect at NetApp Instaclustr, to talk about these issues, what technologies are needed, and how multi-tech platforms can help.</p>



<p>Here is a lightly edited summary of our conversation.</p>



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<p><strong>RTInsights: What data management challenges are businesses dealing with today?</strong></p>



<p><strong>Mills:</strong> I like to break that down into a few groups. One of the most difficult ones is data volume and velocity. Data is flowing at organizations at a very significant clip, and they must make decisions around how to ingest, process, and store those data. Data comes from many different sources, which often presents data quality and consistency issues as well.</p>



<p>Then, you have governance and security. Who can see the data? How are we going to keep it secure? What is the lifecycle of your data? Do you have stuff that is automatically deleted after a day, a week, a month, a year, or seven years? What are the cost implications for maintaining data for those durations?</p>



<p>Those are the high points for the challenges. Addressing those challenges requires a combination of advanced technologies, personnel who are skilled with those technologies, and robust processes and policies to help navigate the waters and point people in the right direction for how to deal with it.</p>



<p><strong>RTInsights: Why are data management approaches that previously worked failing now in light of these challenges?</strong></p>



<p><strong>Mills:</strong> When I started my career 15+ years ago, most organizations could store their data in a relational database and build logical relationships. You could put indexes and views in place to fetch the data quickly and rely on the access controls built into the database for security. You could assign permissions around certain databases, tables and views. Some databases offered the ability to manage column-level permissions as well, which was nice. Even though it seemed complex at the time, that was pretty simple to deal with considering the landscape today.</p>



<p>Now, we&#8217;re dealing with structured, semi-structured, and unstructured data at high volume and velocity, and those relational systems are not able to keep up. They&#8217;re not distributed, and they don&#8217;t easily scale. High availability and disaster recovery, with reasonable RTO/RPO, become a concern as well. There are definitely still use cases for relational databases, but they&#8217;re much more specific.</p>



<p><strong>RTInsights: In general, what’s needed?</strong></p>



<p><strong>Mills:</strong> Fundamentally, many organizations should treat data more like a product. Once the mindset changes on data, you start adding process and structure around the data. Instead of just DevOps and Developers, you have a Data Engineering role. This role becomes the expert around your data and can work with teams across the organization to understand their needs. Importantly, they can address things like schema evolution, security controls, governance, and access. They can be responsible for understanding costs around storage and retention and their value to the business. A role focused on this new data product allows you to look at it a little differently, which is critical.</p>



<p>Then, you start getting into the actual technologies and how you have to adjust to the types of data and the types of requests for the needed data.</p>



<p>If you think about it from a traditional product perspective, if I&#8217;m building an application that runs on a Windows or Mac computer, I&#8217;ve got lots of layers, right? I&#8217;ve got the user interface and how that&#8217;s coded for where it&#8217;s running. Then I&#8217;ve got APIs on the backend, and those might be coded in a different language. Then, you&#8217;ve got databases that support the API and UI. So, you have all of these various layers of technologies that would support a traditional app. When you begin to look at data as a product, you&#8217;re going to have that same type of layered effect with various technologies that solve distinct problems.</p>



<p>A common enterprise architecture that has become popular in recent years is Event Driven Architecture (EDA), where data is a stream. There&#8217;s this ever-present inflow of data into your organization, and you adopt a technology that allows you to save it in a durable way and then distribute it across different platforms. You&#8217;re still going to need relational databases, NoSQL databases, data lakes/oceans, OLAP databases, and/or search technologies like OpenSearch. Obviously, the exact needs of each organization will be different, but this is the landscape today.</p>



<p>As an expert in the data, you understand what data is being written and how it’s being accessed. That’s important because the choice of where you store the data and how you choose to retrieve it needs to be considered carefully. Most organizations today cannot lean on just one or two solutions. From my experience, one of the biggest pitfalls is when a company tries to use a familiar technology to solve a problem that isn’t the core competency of that technology. You end up with poor performance and having to redesign a few years after you’ve gone into production, which can be costly.</p>



<p><strong>See also:</strong> <a href="https://www.clouddatainsights.com/future-proofing-your-data-strategy-with-a-multi-tech-platform/">Future-proofing Your Data Strategy with a Multi-tech Platform</a></p>



<p><strong>RTInsights: What does Instaclustr offer, and how does its multi-tech data platform help?</strong></p>



<p><strong>Mills:</strong> Instaclustr is NetApp Instaclustr, and I say that with intention. NetApp has been a leader in the storage space for many decades. When you talk about Netapp, it&#8217;s not just storage; it&#8217;s data protection, security, visibility, and performance optimization on-premises, in private clouds, and now in the public cloud. NetApp is the only storage company to have first-party storage in all the major clouds &#8211; AWS, Google Cloud, and Azure. You can go into AWS and provision FSxN, Cloud Volumes ONTAP in Google Cloud, or Azure NetApp Files in Azure. Each of those storage solutions come with industry-leading capabilities, which is just the NetApp piece.</p>



<p>When we discuss Instaclustr specifically, the team is all-in on open source, and we have taken a curated approach to the technologies we offer. We want to help you solve each problem a data platform could have, so we don&#8217;t offer solutions that solve the same problem. We can talk about the nuance another time, but we support:</p>



<p><strong>Apache Kafka</strong>, which is great for events and real time streaming.</p>



<p><strong>Apache Cassandra</strong>, which is a distributed NoSQL DB that excels at high-volume writes and storing lots of data.</p>



<p><strong>PostgreSQL</strong>, which is one of the most popular relational databases currently on the market.</p>



<p><strong>OpenSearch</strong>, which was forked from Elasticsearch back in 2021, is a great resource for a number of search functions on very large amounts of data.</p>



<p><strong>Valkey</strong>, which was forked from Redis in 2024, is a high-performance in-memory cache.</p>



<p><strong>ClickHouse</strong>, which is a column-oriented database management system for analytical workloads.</p>



<p><strong>Cadence</strong>, which is a workflow orchestration platform to help manage complex business processes. This tech is outside of our traditional scope. Under the covers, ClickHouse uses <a href="https://kafka.apache.org/">Kafka</a> and Cassandra, and one of our biggest customers was using it in a big way. They asked us if we&#8217;d add it to our stack and host it for them, to which we obliged.</p>



<p>Lastly, we have <strong>Spark</strong>, which is another one that&#8217;s outside of the traditional database world. We have a product called Ocean for Apache Spark. We leverage the open source Spark repository, but we have our own controller that allows it to run on a technology called Ocean, which is an advanced Kubernetes autoscaler.</p>



<p>The suite of products in our platform enables you to have one vendor who has depth and breadth across a stack of products that can, for the most part, get you what you need for your enterprise.</p>



<p>We deliver help for these services in three ways:</p>



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</div>




<p><strong>Managed Platform</strong>, where we can host the infrastructure for you (SaaS), manage it in your cloud account (BYOC), and/or on-prem. We see lots of customers with hybrid environments, on-prem and in the cloud, as well as a multi-cloud approach. Our single control plane helps make that really easy.</p>



<p><strong>Support</strong>, with the expertise we have gained operating these tech&#8217;s for our platform customers, we can help you operate them at a high level, too. One of the most challenging things about open source is there is no bat phone to call when it&#8217;s 3 am, and you&#8217;re in the middle of a crisis. We can be that resource.</p>



<p><strong>Consulting</strong>, we have deep expertise across the board in these technologies. The team that provides support is focused on operational excellence, and some customers need hands-on keyboard help, architecture reviews, or best practices on using these technologies from the client. That&#8217;s where our Consulting team shines.</p>



<p><strong>RTInsights: Can you give some examples?</strong></p>



<p><strong>Mills:</strong> We&#8217;ve got many customers who leverage multiple technologies. As I mentioned with Cadence, we have a customer who uses Kafka, Cassandra, and Postgres, and they came to us and said, &#8220;Hey, we&#8217;re using Cadence, and we&#8217;d like you to manage it for us.&#8221; So, we looked at the tech, built out a team, and started managing it for them. Now, it is one of our generally available offerings. That story is not uncommon.</p>



<p>We have customers who&#8217;ll join with one product, and over time, we&#8217;ll hear, &#8220;You&#8217;re doing such a good job with my Kafka. I&#8217;d like you to take over management of this other technology.&#8221; Or they&#8217;ll ask us, &#8220;Hey, do you support this one, too?&#8221; If the answer is no, the next thing is, &#8220;Well, what will it take to get you to support it?&#8221; We then look at the tech from a variety of different angles, like licensing, marketing, and our own availability to take on a new tech. We always want to make sure we do it right, not halfway.</p>



<p>Another one that really resonates happened within the last year and a half. An organization that services manufacturers has an ERP system and provides supply chain solutions. They came to us when they had a new initiative, building a new integration platform and planning to use Kafka, Cassandra, and OpenSearch.</p>



<p>They had an enterprise architecture and asked us to take a look. I sat down with them and three members of our consulting team, who are experts in those technologies. We looked at their diagram, talked for several hours, and essentially came back and said, &#8220;All right, this is good overall. You need to consider this, this, and this for Kafka, and the way your events are flowing will cause issues here and here.&#8221;</p>



<p>We dug into their schema and their data, the way they were going to be writing the data, the tables, and stuff in Cassandra, and we said, &#8220;You&#8217;re going to have these problems here. Consider refactoring to look like this.&#8221; We talked to him about OpenSearch and the best way to do sharding and sizing. We were able to talk through those technologies and from our practice and experience and suggested a best path forward. After that exercise, they made a huge pivot.</p>



<p>With issues like that, we have so much experience that we can really accelerate time to market. And that&#8217;s what we did with this organization. They came to us after that consulting experience, and they said, &#8220;Okay, we&#8217;re ready to hop on your platform.&#8221; Today, they&#8217;re running Kafka, Cassandra, and OpenSearch on our platform. That allowed them to focus on building the integration platform, not trying to gain expertise around these specific technologies. They just lean on us for that, and they really couldn&#8217;t be happier with us.</p>



<div class="hs-cta-embed hs-cta-simple-placeholder hs-cta-embed-181201086562"
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]]></content:encoded>
					
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		<post-id xmlns="com-wordpress:feed-additions:1">5502</post-id>	</item>
		<item>
		<title>Study Reveals the Impact of Data Management on AI Progress</title>
		<link>https://www.clouddatainsights.com/study-reveals-the-impact-of-data-management-on-ai-progress/</link>
					<comments>https://www.clouddatainsights.com/study-reveals-the-impact-of-data-management-on-ai-progress/#comments</comments>
		
		<dc:creator><![CDATA[Elizabeth Wallace]]></dc:creator>
		<pubDate>Sat, 12 Oct 2024 22:41:42 +0000</pubDate>
				<category><![CDATA[AI/ML]]></category>
		<category><![CDATA[AI strategy]]></category>
		<category><![CDATA[data management]]></category>
		<guid isPermaLink="false">https://www.clouddatainsights.com/?p=5434</guid>

					<description><![CDATA[Companies need a confident data management strategy for to make AI progress. To that end, a recent study found that 59% of companies are utilizing cloud platforms to scale AI initiatives, allowing for efficient management of large data volumes and ensuring data accessibility.]]></description>
										<content:encoded><![CDATA[<div class="wp-block-image">
<figure class="aligncenter size-full"><img loading="lazy" decoding="async" width="1000" height="563" src="https://www.clouddatainsights.com/wp-content/uploads/2024/09/Depositphotos_521052424_S.jpg" alt="data management and AI progress" class="wp-image-5435" srcset="https://www.clouddatainsights.com/wp-content/uploads/2024/09/Depositphotos_521052424_S.jpg 1000w, https://www.clouddatainsights.com/wp-content/uploads/2024/09/Depositphotos_521052424_S-300x169.jpg 300w, https://www.clouddatainsights.com/wp-content/uploads/2024/09/Depositphotos_521052424_S-768x432.jpg 768w" sizes="(max-width: 1000px) 100vw, 1000px" /><figcaption class="wp-element-caption"><em>Companies need a confident data management strategy for to make AI progress. Increasingly, the cloud is playing a role.</em> <em>In fact, according to a recent survey, 59% are utilizing cloud platforms to scale AI initiatives, allowing for efficient management of large data volumes and ensuring data accessibility.</em></figcaption></figure></div>


<p>Today&#8217;s enterprises are eager to leverage artificial intelligence (AI) for a range of applications. However, AI progress is intricately linked to effective data management practices. Ensuring high standards in data quality, access, security, privacy, distribution, and literacy is crucial to avoid the pitfalls that can hinder AI development.</p>



<p>A recent report released by <a href="https://www.starburst.io/info/state-of-data-management-and-its-impact-on-development/" target="_blank" rel="noreferrer noopener">Starburst</a> looks into the current state of data management practices and their impact on AI implementation. The survey gathered insights from 300 IT professionals across the United States and Western Europe. It highlights the challenges, strategies, and future trends that business leaders should consider to harness the potential of AI fully.</p>



<p>See also: <a href="https://www.clouddatainsights.com/unifying-the-data-warehouse-and-data-lake-creates-a-new-analytical-rhythm/">Unifying the Data Warehouse and Data Lake Creates a</a><a href="https://www.clouddatainsights.com/unifying-the-data-warehouse-and-data-lake-creates-a-new-analytical-rhythm/" target="_blank" rel="noreferrer noopener"> </a><a href="https://www.clouddatainsights.com/unifying-the-data-warehouse-and-data-lake-creates-a-new-analytical-rhythm/">New Analytical Rhythm</a></p>



<h3 class="wp-block-heading">Key Findings from the Survey</h3>



<p>The survey&#8217;s scope was revealing. As it turns out, a lot is going on with AI implementation behind the scenes.</p>



<h4 class="wp-block-heading">Strong Intent and Progress in AI Adoption</h4>



<p>The study reveals a robust intent among enterprises to implement AI. An overwhelming majority of respondents (87%) express a &#8220;strong&#8221; or &#8220;very strong&#8221; desire to incorporate AI into their operations within the next 12 months. And this enthusiasm is reflected in the tangible progress being made. Another strong majority of organizations report significant strides in their AI initiatives.</p>



<h4 class="wp-block-heading">Alignment of Data Management with AI Success</h4>



<p>A clear correlation emerges between data management practices and successful AI implementation. Approximately 90% of respondents report that their data management strategies are either &#8220;somewhat aligned&#8221; or &#8220;very aligned&#8221; with their AI innovation goals. This alignment is critical for organizations aiming to maximize AI&#8217;s potential. It enables them to create new products, enhance operational efficiency, and generate deeper business insights.</p>



<h4 class="wp-block-heading">Primary Challenges in AI Evolution</h4>



<p>The survey identifies several key challenges that organizations face in their AI journey:</p>



<ul class="nv-cv-m wp-block-list">
<li>Organizing Data for AI Use: Over half of the respondents struggle with organizing structured data for machine learning and unstructured data for retrieval-augmented generation (RAG).</li>



<li>Barriers to High-Quality Data: The most significant barriers to accessing high-quality data for AI projects are data privacy and security concerns and the sheer volume of data.</li>
</ul>



<h4 class="wp-block-heading">Strategies for Improving Data Management</h4>



<p>To address these challenges, organizations are employing a range of strategies, but three specific methodologies showed a strong following:</p>



<ol class="nv-cv-m wp-block-list">
<li>Adopting Agile Methodologies: A solid 61% of respondents use agile methods to manage data projects, enhancing flexibility and responsiveness to changing AI needs.</li>



<li>Leveraging Cloud-Based Platforms: Close behind, 59% are utilizing cloud platforms to scale AI initiatives, allowing for efficient management of large data volumes and ensuring data accessibility.</li>



<li>Implementing Data Governance and Federated Data Access: 52% have adopted data governance and federated data access strategies to enhance data security, privacy, and accessibility across multiple sources.</li>
</ol>



<h4 class="wp-block-heading">The Importance of Real-Time Data</h4>



<p>Access to real-time data is highlighted as a critical factor for AI success. 62% of respondents identify it as an area requiring the most attention. Ensuring data accuracy, consistency, and availability in real time is vital for the reliability and performance of AI models.</p>



<h3 class="wp-block-heading">Emerging Trends and Recommendations</h3>



<p>Looking ahead, the study suggests several trends and strategies for aligning data management with AI initiatives:</p>



<ul class="nv-cv-d nv-cv-m wp-block-list">
<li>Addressing Ethical Concerns and Leveraging Federated Learning: As AI adoption grows, ethical considerations in data use become increasingly important. Federated learning, which allows AI to train on decentralized data while maintaining privacy, is emerging as a critical trend.</li>



<li>Building a Data-Driven Culture: Enhanced data literacy is essential for maximizing AI&#8217;s impact. 90% of respondents believe that greater data literacy would have at least a moderate impact on the success of AI projects. 40% expect a significant impact.</li>
</ul>



<h3 class="wp-block-heading">What This Means for Businesses Moving Forward</h3>



<p>The findings from this study indicate that making AI progress will require businesses to rethink their approach to data management. Moving forward, organizations should:</p>



<ul class="nv-cv-d nv-cv-m wp-block-list">
<li>As real-time data becomes more critical, companies must invest in technologies and processes that ensure high-quality and readily available data.</li>



<li>To stay competitive, businesses should adopt data management frameworks that can adapt to evolving AI technologies and methodologies.</li>



<li>Building a culture that values data literacy and cross-functional collaboration will be key to maximizing the impact of AI. This involves increasing awareness of data&#8217;s strategic importance and encouraging collaboration across different parts of the organization.</li>



<li>As AI becomes more central to business strategy, addressing ethical concerns and ensuring responsible data use will be crucial. Strategies like federated learning, which supports privacy while enabling AI training on decentralized data, will likely gain prominence.</li>
</ul>



<p>&#8220;If there&#8217;s one takeaway from this research,&#8221; Adrian Estala, VP, Field Chief Data Officer for Starburst notes, &#8220;it&#8217;s that 63% of respondents lack the confidence in their data strategy needed to fully execute on their AI initiatives.&#8221;</p>



<p>&#8220;For years, organizations have been aware of the importance of solid data management, but many have delayed addressing it. Now, with AI adoption surging, the urgency is real. However, Open Hybrid Lakehouse data architectures make it easier than ever to manage,&#8221; he says.&nbsp;</p>



<p>&#8220;The next generation of leaders will stand out by how effectively, ethically, and securely they harness enterprise data to power AI. Success won&#8217;t come from the AI model itself—plenty of powerful models exist—but from unlocking enterprise data with the right business context.&#8221;</p>



<h3 class="wp-block-heading">Prioritizing Real-Time Data Access and Quality</h3>



<p>The report underscores the critical role of robust data management in achieving AI success. Organizations must prioritize real-time data access, ensure data security and privacy, and foster a data-driven culture to leverage AI&#8217;s potential fully. By adopting scalable architectures and enhancing data quality, businesses can position themselves for long-term success in an increasingly data-centric world.</p>



<p>For enterprises looking to thrive in the AI era, the key will be developing a comprehensive data management strategy that supports real-time data access, addresses privacy concerns, and integrates agile and federated data approaches. Locking down an effective data management strategy could be the key to true AI progress.</p>
]]></content:encoded>
					
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		<post-id xmlns="com-wordpress:feed-additions:1">5434</post-id>	</item>
		<item>
		<title>Future-proofing Your Data Strategy with a Multi-tech Platform</title>
		<link>https://www.clouddatainsights.com/future-proofing-your-data-strategy-with-a-multi-tech-platform/</link>
					<comments>https://www.clouddatainsights.com/future-proofing-your-data-strategy-with-a-multi-tech-platform/#respond</comments>
		
		<dc:creator><![CDATA[Salvatore Salamone]]></dc:creator>
		<pubDate>Wed, 09 Oct 2024 15:32:20 +0000</pubDate>
				<category><![CDATA[Cloud Data Platforms]]></category>
		<category><![CDATA[Integration]]></category>
		<category><![CDATA[Sponsored]]></category>
		<category><![CDATA[Apache Kafka]]></category>
		<guid isPermaLink="false">https://www.clouddatainsights.com/?p=5466</guid>

					<description><![CDATA[As data becomes more complex, the need for a multi-tech platform has never been more evident. By looking beyond single solutions like Apache Cassandra or Apache Kafka and embracing a more holistic, integrated approach, businesses can future-proof their data strategies and stay competitive in an ever-evolving landscape. ]]></description>
										<content:encoded><![CDATA[
<p><em>Sponsored by Instaclustr</em></p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img loading="lazy" decoding="async" width="982" height="550" src="https://www.clouddatainsights.com/wp-content/uploads/2024/10/2-Depositphotos_327486454_S.jpg" alt="" class="wp-image-5469" srcset="https://www.clouddatainsights.com/wp-content/uploads/2024/10/2-Depositphotos_327486454_S.jpg 982w, https://www.clouddatainsights.com/wp-content/uploads/2024/10/2-Depositphotos_327486454_S-300x168.jpg 300w, https://www.clouddatainsights.com/wp-content/uploads/2024/10/2-Depositphotos_327486454_S-768x430.jpg 768w" sizes="(max-width: 982px) 100vw, 982px" /><figcaption class="wp-element-caption"><em>As data becomes more complex, the need for a multi-tech platform has never been more evident. Businesses need to look beyond single solutions.</em></figcaption></figure></div>


<p>Data scientists and data analysts are facing tremendous changes reshaping the data landscape. Over the past decade, the explosion of data volumes and the increasing velocity of that data have transformed how businesses gather, process, and store information. Traditional approaches that were powered by a single tool or two, like Apache Cassandra or <a href="https://kafka.apache.org/" target="_blank" rel="noreferrer noopener">Apache Kafka</a>, were once the way to proceed. However, now used alone, these tools are proving insufficient to meet the demands of modern data ecosystems. The challenges presented by today’s distributed, real-time, and unstructured data have made it clear that businesses need a new strategy. Increasingly, that strategy involves the use of a multi-tech platform.</p>



<h3 class="wp-block-heading">The Growing Complexities of Data Management</h3>



<div class="hs-cta-embed hs-cta-simple-placeholder hs-cta-embed-180609274213"
  style="max-width:100%; max-height:100%; width:700px;height:182.796875px" data-hubspot-wrapper-cta-id="180609274213">
  <a href="https://cta-service-cms2.hubspot.com/web-interactives/public/v1/track/redirect?encryptedPayload=AVxigLLCnCBDT7uv12m6%2B4MLyJpU%2FcQteyVAY5Y%2FvnPy%2BgKe%2B3z7kryTI1ma%2FhYU9njxO8pRAd%2FqIha4H39%2B8ZkT03i1CLfl8xPWN7RpXwgHIk1NerM%3D&#038;webInteractiveContentId=180609274213&#038;portalId=8019034" target="_blank" rel="noopener" crossorigin="anonymous">
    <img decoding="async" alt="White Paper &nbsp; The Benefits of Open Source and the Risks of Open Core &nbsp;" loading="lazy" src="https://no-cache.hubspot.com/cta/default/8019034/interactive-180609274213.png" style="height: 100%; width: 100%; object-fit: fill"
      onerror="this.style.display='none'" />
  </a>
</div>



<p>The data management challenges facing businesses today are vastly different from those they dealt with a few years ago. Businesses now generate massive streams of real-time data that require processing and analysis on the fly. This data can range from transactional records to social media streams, machine-generated logs, and IoT device outputs.</p>



<p>Handling this diversity demands a solution that can manage not only large volumes of data but also diverse types of workloads — all while maintaining low-latency performance. The traditional tools of the past are not built to handle these multifaceted requirements. As data grows in complexity, the ability to efficiently manage, store, and extract value from it requires a more robust, dynamic, and adaptable architecture.</p>



<p><strong>See also: </strong><a href="https://www.clouddatainsights.com/the-benefits-of-instaclustr-managed-platform-for-apache-cassandra/" target="_blank" rel="noreferrer noopener">The Benefits of Instaclustr Managed Platform for Apache Cassandra</a></p>



<h3 class="wp-block-heading">The Shortcomings of Traditional Approaches</h3>



<p>Both Apache Cassandra and Apache Kafka have long played a major role in data management and analysis. They both offered distributed, scalable architectures. Additionally, Apache Cassandra offers high availability and fault tolerance for handling distributed databases, while Apache Kafka excels at streaming and real-time data processing. However, businesses that rely solely on these technologies may find themselves limited by the inherent design constraints of these tools. Some points to consider include:</p>



<p><strong>Cassandra’s limitations</strong>: While Apache Cassandra is excellent for managing massive amounts of structured data across distributed environments, it struggles with unstructured or semi-structured data. Additionally, it can be challenging to integrate with the growing number of real-time analytics and machine learning applications, limiting its utility in modern data-centric operations.</p>



<p><strong>Kafka’s limitations</strong>: On the other hand, while Kafka is well-suited for handling real-time data streams, it is not built to manage long-term data storage or complex querying. Kafka’s capabilities are complementary to, but not a replacement for, other data storage and management tools.</p>



<p>In an environment where businesses need to extract insights from both real-time and historical data, neither Cassandra nor Kafka alone can fully address all the necessary components of an agile, resilient, and future-proof data strategy.</p>



<h3 class="wp-block-heading">Why Businesses Must Look Beyond Cassandra and Kafka</h3>



<p>As the complexity of data ecosystems grows, relying on a single tool is no longer feasible. Businesses now need to manage data pipelines, data lakes, streaming analytics, and real-time processing—all within the same environment. A multi-tech approach can handle this variety of workloads while maintaining scalability, fault tolerance, and high performance.</p>



<p>For example, integrating <strong>Apache Kafka</strong> with <strong>Apache Cassandra</strong> provides some level of real-time stream processing combined with distributed storage, but even this combination has its limits. You still need more sophisticated solutions to handle emerging data types, the complexities of hybrid cloud environments, and advanced use cases like AI and machine learning models that require real-time feedback loops.</p>



<h3 class="wp-block-heading">The Multi-Tech Platform: A Holistic Solution</h3>



<p>A multi-tech platform blends several specialized technologies into a unified ecosystem to meet diverse data and application needs. This approach delivers several critical benefits:</p>



<ol start="1" class="nv-cv-d nv-cv-m wp-block-list">
<li><strong>Flexibility and adaptability</strong>: A multi-tech platform provides the flexibility to combine best-of-breed tools that are specifically designed to handle distinct data types and workloads. For example, using <strong>Apache Cassandra</strong> for distributed data storage, <strong>Kafka</strong> for real-time streaming, and adding tools like <strong>Elasticsearch</strong> for search and analytics or <strong>Redis</strong> for caching creates a versatile platform that can adapt to a wide range of data use cases.</li>



<li><strong>Seamless integration of technologies</strong>: These platforms are built with the goal of enabling seamless communication between different data management tools. This ensures that each tool performs the task it was optimized for without creating silos or performance bottlenecks. This results in better efficiency, reduced latency, and an overall more resilient system.</li>



<li><strong>Future-proofing your data architecture</strong>: With a multi-tech platform, an organization is not locked into a single vendor or technology. This flexibility is key to evolving with the latest advances in data management and adapting to the growing demand for advanced analytics, machine learning, and AI-driven data processing.</li>
</ol>



<h3 class="wp-block-heading">How Instaclustr by NetApp Helps Deliver Multi-Tech Solutions</h3>



<p>Implementing a multi-tech platform can be complex, especially considering the need to manage integrations, scalability, security, and reliability across multiple technologies. Many organizations simply do not have the time or expertise in the different technologies to pull this off.</p>



<p>Increasingly, organizations are partnering with a technology provider that has the expertise in scaling traditional open-source solutions and the real-world knowledge in integrating the different solutions.</p>



<p>That’s where <strong>Instaclustr by NetApp</strong> comes in. Instaclustr offers a fully managed platform that brings together a comprehensive suite of open-source data technologies. By leveraging the expertise of Instaclustr, businesses can adopt a multi-tech platform without the headaches of managing the underlying infrastructure or complex integrations. Instaclustr’s offerings include:</p>



<ul class="nv-cv-d nv-cv-m wp-block-list">
<li><strong>Apache Cassandra</strong>: As a managed solution, Instaclustr provides businesses with a highly available, scalable, and low-latency distributed database optimized for the demands of modern data architectures.</li>



<li><strong>Apache Kafka</strong>: Instaclustr offers a managed Kafka service that integrates seamlessly with other components in the data stack, enabling real-time data streaming at scale without compromising reliability or security.</li>



<li><strong>Redis and Elasticsearch</strong>: For caching and search/analytics needs, Instaclustr’s support for Redis and Elasticsearch ensures that businesses have the right tools to handle large-scale queries and deliver rapid results.</li>



<li><strong>Managed Platform Services</strong>: Instaclustr also provides management services for critical components like security, monitoring, backups, and disaster recovery, ensuring that your multi-tech platform remains resilient and operational at all times.</li>
</ul>



<div class="hs-cta-embed hs-cta-simple-placeholder hs-cta-embed-180609274213"
  style="max-width:100%; max-height:100%; width:700px;height:182.796875px" data-hubspot-wrapper-cta-id="180609274213">
  <a href="https://cta-service-cms2.hubspot.com/web-interactives/public/v1/track/redirect?encryptedPayload=AVxigLLCnCBDT7uv12m6%2B4MLyJpU%2FcQteyVAY5Y%2FvnPy%2BgKe%2B3z7kryTI1ma%2FhYU9njxO8pRAd%2FqIha4H39%2B8ZkT03i1CLfl8xPWN7RpXwgHIk1NerM%3D&#038;webInteractiveContentId=180609274213&#038;portalId=8019034" target="_blank" rel="noopener" crossorigin="anonymous">
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      onerror="this.style.display='none'" />
  </a>
</div>



<h3 class="wp-block-heading">Conclusion</h3>



<p>As data becomes more complex, the need for a multi-tech platform has never been more evident. By looking beyond single solutions like Apache Cassandra or Apache Kafka and embracing a more holistic, integrated approach, businesses can future-proof their data strategies and stay competitive in an ever-evolving landscape. With Instaclustr by NetApp providing the tools and expertise to manage this complexity, businesses are better equipped to unlock the full potential of their data.</p>
]]></content:encoded>
					
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		<post-id xmlns="com-wordpress:feed-additions:1">5466</post-id>	</item>
		<item>
		<title>Achieving Cloud Neutrality with a Multicloud Approach</title>
		<link>https://www.clouddatainsights.com/achieving-cloud-neutrality-with-a-multicloud-approach/</link>
					<comments>https://www.clouddatainsights.com/achieving-cloud-neutrality-with-a-multicloud-approach/#respond</comments>
		
		<dc:creator><![CDATA[Ignacio M. Llorente]]></dc:creator>
		<pubDate>Mon, 07 Oct 2024 00:30:24 +0000</pubDate>
				<category><![CDATA[Cloud Data Platforms]]></category>
		<category><![CDATA[cloud neutrality]]></category>
		<category><![CDATA[multicloud]]></category>
		<guid isPermaLink="false">https://www.clouddatainsights.com/?p=5426</guid>

					<description><![CDATA[Discover how deploying multicloud successfully can enable better digital transformation through cloud neutrality.]]></description>
										<content:encoded><![CDATA[<div class="wp-block-image">
<figure class="aligncenter size-full"><img loading="lazy" decoding="async" width="1000" height="667" src="https://www.clouddatainsights.com/wp-content/uploads/2024/09/Depositphotos_168639542_S.jpg" alt="cloud neutrality" class="wp-image-5427" srcset="https://www.clouddatainsights.com/wp-content/uploads/2024/09/Depositphotos_168639542_S.jpg 1000w, https://www.clouddatainsights.com/wp-content/uploads/2024/09/Depositphotos_168639542_S-300x200.jpg 300w, https://www.clouddatainsights.com/wp-content/uploads/2024/09/Depositphotos_168639542_S-768x512.jpg 768w, https://www.clouddatainsights.com/wp-content/uploads/2024/09/Depositphotos_168639542_S-930x620.jpg 930w" sizes="(max-width: 1000px) 100vw, 1000px" /><figcaption class="wp-element-caption"><em>Discover how deploying multicloud successfully can enable better digital transformation through cloud neutrality.</em></figcaption></figure></div>


<p>When considering a move to the cloud, it might be tempting to pick a single provider. But digital transformation services aren’t one-size-fits-all. In fact, there are many reasons why digital transformation requires organizations to take a cloud neutrality approach and make use of cloud services from multiple providers and avoid being locked into a single provider. Increased cost saving and price flexibility, risk mitigation, enhanced security and service availability, unlimited scalability, better agility, and the promise of each provider’s best solutions are all too great to ignore.&nbsp;</p>



<p>See also: <a href="https://www.clouddatainsights.com/solving-the-challenges-of-multi-cloud-cost-management/">Solving the Challenges of Multicloud Cost Management</a></p>



<p>The days of single public cloud deployments are gone. According to Gartner, by 2026, more than 90% of enterprises will extend their capabilities to multi-cloud environments, up from 76% in 2020. The use of multiple clouds is by far the most common pattern among enterprises, with 89% (73% hybrid cloud, 14% multiple public cloud and 2% multiple private cloud) adopting this strategy in <a href="https://info.flexera.com/CM-REPORT-State-of-the-Cloud?_gl=1*17hyv4g*_gcl_au*MTM1MDQxODAwMy4xNzI0MzM3MTUx">Flexera’s 2024 State of the Cloud Report</a>.</p>



<p>The truth is that managing and supporting multi-cloud is not an easy task. In an ideal world, application workloads—whatever their heritage—should be able to move seamlessly between (or be shared among) cloud service providers and to be deployed wherever the optimal combination of performance, functionality, cost, security, compliance, availability, and resilience is to be found—while avoiding the dreaded ‘vendor lock-in’.&nbsp;</p>



<p>These are some key principles for making multi-cloud adoption a success:</p>



<h3 class="wp-block-heading">1. Avoid Multi-Cloud through Hyperscalers</h3>



<p>Although big cloud providers have spent years ignoring multi-cloud and hybrid cloud, now they are making their first steps towards embracing them. Hyperscalers are now starting to offer new platforms (e.g. AWS ECS Anywhere, Google Anthos) that work on other providers, and pre-configured hybrid cloud appliances (e.g. AWS Outpost, Microsoft’s Azure Stack) that promise to bring the power of the public cloud to the private cloud. While they offer the simplicity of using the same interfaces both on the public cloud and on a private data center, these proprietary solutions do not avoid the pitfalls of single-vendor reliance and can be very expensive in the long run.&nbsp;&nbsp;</p>



<h3 class="wp-block-heading">2. Avoid Proprietary-Source Solutions</h3>



<p>The evolution of the modern cloud is leading to the creation of highly complex systems, often based on proprietary orchestration solutions by major vendors (e.g. Nutanix, VMware), that expand private clouds with resources from cloud providers. These proprietary-source solutions have predatory pricing and licensing models, are complex and expensive to deploy and maintain, and usually require the user to manually migrate or rebuild workloads. When the solution combines hardware and software, the problem is exacerbated and vendor lock-in is inevitable.&nbsp;</p>



<p>A recent example that highlights this trend is <a href="https://www.cio.com/article/2513749/will-vmwares-licensing-changes-push-devirtualization-of-data-centers.html">VMware&#8217;s acquisition by Broadcom</a>, which has resulted in widespread concern due to substantial changes in pricing and licensing structures, further intensifying the challenges of vendor lock-in.</p>



<h3 class="wp-block-heading">3. Adopt True Multi-Cloud&nbsp;&nbsp;</h3>



<p>Multi-cloud is not only about achieving interoperability, defined as the ability to manage your workload across every cloud from a single pane of glass. A true multi-cloud solution should also bring portability, defined as the execution of your workloads with the same images and templates on any infrastructure and their mobility across clouds and on-premises infrastructure, enhanced security, defined as the use of dedicated, isolated resources with improved security, privacy, and control, and expanded service availability, defined as the execution of applications to meet the quality of service requirements.</p>



<h3 class="wp-block-heading">4. Not All Workloads Are Heading to the Cloud</h3>



<p>Although multi-cloud will become the norm, there’s still a place for the on-premise data center, at least in the near term, either as part of a hybrid cloud strategy or to host legacy applications that, for whatever reason, are not suitable for migration to the cloud. Some of the main reasons to keep using on-premises resources to host workloads include cost, control, security, and performance. Moreover, modern distributed cloud environments can include edge on-premise or on-campus micro data centers in cases requiring extreme privacy and/or latency.</p>



<p>Findings from the <a href="https://uptimeinstitute.com/resources/research-and-reports/uptime-institute-global-data-center-survey-results-2023">2023 Uptime Institute Global Data Center Survey</a>, the longest-running survey of its kind, reveal that for the first time, IT workloads hosted in on-premises data centers now account for slightly less than half of the total enterprise footprint. At the same time, <a href="https://services.google.com/fh/files/misc/esg_ebook_google_cloud_multicloud_application_deployment_february_2023.pdf">a report by the Enterprise Strategy Group</a>, published in February 2023, shows that 26% of enterprises still follow an on-premises-first policy, meaning they deploy new applications using on-premises technology unless there&#8217;s a more compelling case to use public cloud services.</p>



<h3 class="wp-block-heading">5. Be Ready for Cloud Repatriation</h3>



<p>Cloud repatriation is the process of reverse-migrating application workloads and data from the public cloud to a private cloud located within an on-premise data center or to a colocation provider. Companies need to think about cloud repatriation upfront, optimize early, and apply a vendor-neutral approach from the very beginning.&nbsp;</p>



<p>A 2021<a href="https://opennebula.io/get-ready-for-cloud-repatriation-set-up-your-multi-cloud-with-opennebula/"> study by VC firm Andreessen Horowitz</a> found that the cloud accounted for 50% of the cost of goods sold (COGS) in the top 50 public Software-as-a-Service companies—and with the number of public software companies growing, the problem adds up to $100 billion in market value. <a href="https://opennebula.io/get-ready-for-cloud-repatriation-set-up-your-multi-cloud-with-opennebula/">Cloud expenses are not really OpEx</a> because many large companies end up having to accept spending commitments with the provider. For example, <a href="https://techcrunch.com/2017/02/09/snap-will-spend-1-billion-on-amazon-cloud-services-amended-filing-shows/?guccounter=1&amp;guce_referrer=aHR0cHM6Ly9vcGVubmVidWxhLmlvLw&amp;guce_referrer_sig=AQAAAEAPeG1IojILDlyxihesQzycNtLzKWavS5gFNv4StMmz_emup1H3xB0rWfaUdgsCGh94i5pW-m3NeePgfIuINpQZmIq4kD0G0MxZ8V9z5qddT7RxDYa-qsfkPFnj35hIdB9O30IpQ0UfgNXuB6VFXdCmZ2bDBFn6YEmmIqjh7ksw">Snap</a> said in 2017 that it had committed to spending $2 billion over five years on Google and $1 billion over five years on AWS. Other studies demonstrate that <a href="https://www.business2community.com/cloud-computing/overprovisioning-always-on-resources-lead-to-26-6-billion-in-public-cloud-waste-expected-in-2021-02381033">overprovisioning and always-on resources</a> will lead to $26.6 billion in public cloud waste in 2021, not to mention energy waste.</p>



<p>Given these rising costs, it&#8217;s no surprise that enterprises are reconsidering their cloud strategies. According to a Barclays survey, 83% of enterprise CIOs plan to repatriate at least some workloads in 2024—an increase from just 43% in the second half of 2020. This shift reflects a growing concern about optimizing cloud spending and avoiding unnecessary waste, as enterprises seek more cost-effective, flexible solutions for their IT infrastructure.</p>



<p>See also: <a href="https://www.clouddatainsights.com/what-strategic-decisions-to-consider-for-cloud-repatriation/">What Strategic Decisions to Make for Cloud Repatriation</a></p>



<h3 class="wp-block-heading">6. Automate Deployment and Operations&nbsp;</h3>



<p>The original vision of cloud computing was on-demand, automated services that scale dynamically to meet demand. While this vision is now a reality for a single cloud, multi-cloud automation is complex and requires specialized tools to piece together solutions from technology stacks and services offered by hyperscalers. Multi-cloud platforms should be based on the automated deployment of nodes at cloud and edge locations with dynamic configurations to fit the needs of heterogeneous environments, the nature of the workloads, and development workflows. Deciding where to place an application is a complex decision based on infrastructure costs, data fees, performance, uptime, and latency.&nbsp;</p>



<h3 class="wp-block-heading">Streamline Your Operations with an Open Source Multi-Cloud Platform</h3>



<p>It&#8217;s essential to find ways to simplify cloud operations, as each additional provider in a multi-cloud environment increases management and operational complexity significantly. Many organizations have implemented private cloud infrastructure and now manage workloads across multi-cloud environments using open source solutions. By using a vendor-neutral platform to orchestrate the datacenter-cloud-edge continuum, they can achieve unified management of IT infrastructure and applications and achieve the dream of cloud neutrality.</p>
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		<title>Empowering the Manufacturing Workforce with AI</title>
		<link>https://www.clouddatainsights.com/empowering-the-manufacturing-workforce-with-ai/</link>
					<comments>https://www.clouddatainsights.com/empowering-the-manufacturing-workforce-with-ai/#respond</comments>
		
		<dc:creator><![CDATA[Dustin Johnson]]></dc:creator>
		<pubDate>Mon, 07 Oct 2024 00:21:06 +0000</pubDate>
				<category><![CDATA[AI/ML]]></category>
		<category><![CDATA[AI in manufacturing]]></category>
		<guid isPermaLink="false">https://www.clouddatainsights.com/?p=5327</guid>

					<description><![CDATA[Explore how industrial organizations can overcome workforce challenges through upskilling and modern technologies like advanced analytics and generative AI, ensuring competitiveness and progress toward corporate objectives such as Net Zero goals.]]></description>
										<content:encoded><![CDATA[<div class="wp-block-image">
<figure class="aligncenter size-full"><img loading="lazy" decoding="async" width="1000" height="667" src="https://www.clouddatainsights.com/wp-content/uploads/2024/08/Depositphotos_120079998_S.jpg" alt="AI in manufacturing" class="wp-image-5328" srcset="https://www.clouddatainsights.com/wp-content/uploads/2024/08/Depositphotos_120079998_S.jpg 1000w, https://www.clouddatainsights.com/wp-content/uploads/2024/08/Depositphotos_120079998_S-300x200.jpg 300w, https://www.clouddatainsights.com/wp-content/uploads/2024/08/Depositphotos_120079998_S-768x512.jpg 768w, https://www.clouddatainsights.com/wp-content/uploads/2024/08/Depositphotos_120079998_S-930x620.jpg 930w" sizes="(max-width: 1000px) 100vw, 1000px" /><figcaption class="wp-element-caption"><em>Explore how industrial organizations can overcome workforce challenges through upskilling and modern technologies like advanced analytics and generative AI, ensuring competitiveness and progress toward corporate objectives such as Net Zero goals.</em></figcaption></figure></div>


<p>From productivity enhancement to optimization and decarbonization efforts, today’s industrial organizations are setting ambitious corporate objectives with even more ambitious deadlines. In fact, according to <a href="https://newsroom.accenture.com/news/2022/nearly-all-companies-will-miss-net-zero-goals-without-at-least-doubling-rate-of-carbon-emissions-reductions-by-2030-accenture-report-finds#:~:text=NEW%20YORK%3B%20Nov.,Accenture%20(NYSE%3A%20ACN).">Accenture</a>, nearly all organizations (93%) will fail to achieve their Net Zero goals if they don’t at least double the pace of emissions reduction by 2030.<br><br>Data remains the key driver for progressing toward these corporate objectives. However, despite significant investments in digital transformation strategies, many companies still lack the ability to convert raw data to meaningful and actionable insights.&nbsp;&nbsp;</p>



<p>Innovative analytical technologies provide a mechanism for transforming data to insights, but this requires a workforce capable of understanding the inputs and interpreting insights. As <a href="https://blog.lnsresearch.com/how-to-get-a-30-day-employee-to-a-30-year-performance-level">industrial organizations continue experiencing</a> a significant drop in the average tenure and time-in-role for employees, they are rapidly losing experienced subject matter expert (SME) knowledge.<br><br>Investments in new technologies like advanced analytics, AI, and machine learning are helping bridge this problematic knowledge gap. Ensuring proficiency in these technologies requires organizational investment in training and upskilling the workforce, which can be difficult due to time and other resource constraints. This article examines both the need for and challenges of upskilling workers across the industrial sector, and details how technology partners are accelerating workforce empowerment to maximize user adoption and efficiency with these tools to help companies overcome these roadblocks.</p>



<p>See also: <a href="https://www.clouddatainsights.com/a-roadmap-to-boost-data-team-productivity-in-the-era-of-generative-ai/">A Roadmap to Boost Data Team Productivity in the Era of AI</a></p>



<h3 class="wp-block-heading">Facing workforce challenges</h3>



<p>The shortage of skilled labor in manufacturing has been anticipated and extensively documented for the last several years. Many of the most experienced workers are approaching retirement, while others are moving to more trending fields like renewables. According to the <a href="https://www.weforum.org/publications/the-future-of-jobs-report-2023/">World Economic Forum&#8217;s Future of Jobs Report 2023</a>, six out of ten workers will need training before 2027. However, only half currently have access to sufficient training opportunities. Additionally, the report highlights that, across various industries, training workers in AI and big data is a top priority for upskilling over the next five years.<br><br>Moreover, recent studies indicate that employees are increasingly seeking more than just high salaries. They recognize that mastering new technologies is essential for their long-term career prospects. For instance, a <a href="https://www.getireport.com/oil-and-gas/">GETI report</a> reveals that 87% of workers in the oil and gas sector would contemplate changing jobs, with many prioritizing professional growth and learning opportunities as crucial factors in their decision.<br><br>As a result, companies that do not invest in new technologies or enable their workforce to effectively use these tools risk losing their competitive advantage and worsening the existing skill shortage.</p>



<h3 class="wp-block-heading">Upskilling with modern technologies</h3>



<p>Luckily, the companies that are proactively investing in both innovative technologies and upskilling are seeing measurable productivity and efficiency improvements, while also attracting and retaining an energetic workforce.</p>



<p>Modern advanced analytics platforms enable personnel across industrial organizations to access multiple data sources to seamlessly combine and analyze data regardless of its source. Combining this capability with intuitive self-service tools empowers subject matter experts (SMEs) to transform their raw data into meaningful insights.</p>



<p>With access to these technologies, teams can easily and efficiently increase uptime, complete root cause analyses, or monitor greenhouse gas emissions in real time. However, while easy to learn and use, there is a notable requirement for training—both in leveraging the technology’s functions and features, and in the principles of data analytics.<br><br>Historically, the most prevalent challenge in technical training was a lack of time, especially in large, uninterrupted blocks that often required personnel to spend multiple days off-site. To combat these constraints, modern technology companies are spearheading new approaches to&nbsp; upskill the workforce.</p>



<h3 class="wp-block-heading">Enhancing workflows with GenAI</h3>



<p>The emergence of generative artificial intelligence (GenAI) over the last two years is ushering in new and exciting opportunities for workers to get faster results, while reducing the need for formal education and training.<br><br>Forward-thinking industrial organizations are investing in embedded and stand-alone solutions that offer their teams a way to generate text or code based on user prompts, making it easier to achieve operational excellence. By providing summaries and detailed explanations in natural language, SMEs can better understand the full process picture and make data-driven decisions with beneficial results. This empowers personnel to efficiently analyze massive datasets, identify trends and anomalies, and make proactive, informed decisions, fostering operational improvements in production, quality, and yield across the industrial sector.</p>



<p>Additionally, for engineers without formal training or time to study programming languages like Python or R, these solutions significantly lower the barrier to entry for setting up advanced analysis with sophisticated algorithms. It also facilitates better project understanding and collaboration with data scientist colleagues or third parties.</p>



<h3 class="wp-block-heading">Understanding your technology and your data</h3>



<p>While GenAI brings long-awaited improvements for the future of manufacturing, it is not reasonable for organizations to treat AI or generative technologies as a magic bullet that will solve all operational issues. This is especially true in the process industries, which uniquely require data cleansing and contextualization due to noisy data. Data quality is also crucial to the success of these solutions. The technology’s output is only as good as the quality of the data, and as the saying goes, garbage in equals garbage out.<br><br>Before deploying these solutions, it is also critical for teams to assess whether users are equipped with the knowledge to prepare their data effectively and the skills to develop and maintain GenAI solutions.&nbsp;</p>



<h3 class="wp-block-heading">Investing in upskilling</h3>



<p>While new technologies, including advanced analytics platforms and GenAI tools, promise to boost productivity, they require a skilled workforce that can understand and act on their results to make meaningful impacts across organizations. Organizations that focus on both empowering their workforce and adopting modern tools will see the greatest returns on their investments.&nbsp;</p>
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