Whether those goals are best met with one cloud, a hybrid model, or a multicloud model will depend on your unique situation, dependencies, budget, and available resources. We’ll cover the difference between multicloud and hybrid cloud so you can make an informed next step.

Public Cloud vs Private Cloud?

Hybrid environments combine public and private clouds. And in the case of hybrid IT, it can also include non-cloud environments. Generally, the choice between public and private cloud will come down to how much control businesses want over resources compared to the amount of flexibility they need.

Public cloud providers, such as AWS and Azure, rent out resources to companies in predetermined amounts at a discount, or on a model where you pay for what you use. Businesses have the flexibility to scale up or down their resources on-demand. However, they must navigate and configure the security settings and tools provided by the public cloud provider to ensure optimal security.

Private cloud can run on-premises or offsite with a data center provider. Organizations have significantly more control over configurations and security settings in a private cloud environment. However, scaling resources can be more challenging, and the infrastructure is often more expensive compared to public cloud options. This control and security, combined with the challenges of scalability and cost, make hybrid cloud solutions an attractive option for many businesses.

What is the Difference Between Multicloud and Hybrid Cloud Computing?

In cloud computing, we often hear the terms “multicloud” and hybrid cloud. While both terms sound similar, there are a few key differences organizations tend to overlook. Understanding the differences between these two cloud approaches is essential for organizations that are striving to ensure cloud optimization and meet business needs.

Architecture

A hybrid cloud is the combination of cloud and on-premises infrastructure in a unified framework. It could include public cloud (Microsoft Azure, AWS, etc.) and private cloud infrastructure. Hybrid cloud adoption has increased over the past few years due to its many benefits, which we’ll be covering shortly.

Multicloud computing is the use of multiple public cloud platforms to support business functions. Multicloud deployments can be part of an overall hybrid cloud environment. A hybrid cloud strategy may include multiple clouds, but a multicloud strategy isn’t necessarily hybrid.

Intercloud Workloads

In a multicloud environment, workloads are deployed across different public clouds and often require additional processes and tools for interoperability. Similarly, hybrid cloud environments can include these workloads but also involve movement between cloud and on-premises infrastructures. This flexibility is often necessary for legacy systems with numerous dependencies that cannot be easily migrated to the cloud.

Vendor Lock-in

Vendor lock-in happens when a business feels overly reliant on one cloud provider and finds it difficult to switch to a new provider without significant investment and resources to do so. While both formats may introduce vendor lock-in, this may be more common in hybrid cloud environments where businesses are only using one public cloud provider. In a multicloud configuration, organizations may have more flexibility to move workloads to different public cloud environments.

Pricing

This flexibility in options within a multicloud environment can lead to more competitive pricing for businesses. Public cloud resources can be purchased in discounted packages for predictable workloads, while pay-as-you-go pricing is available for variable workloads.

Availability

With hybrid cloud, availability depends on both the public cloud provider and the on-premises infrastructure in use. In contrast, a multicloud environment can offer higher availability since data and workloads are distributed across multiple public clouds, reducing the risk of downtime.

Data Storage

Data storage has some similarities and differences between cloud environments. In hybrid cloud storage, on-premises storage (private cloud) is combined with public cloud resources. This provides greater control for sensitive data stored on the private cloud, but also requires tools to move data between environments that may be harder to set up compared to multicloud environments. Hybrid cloud can be ideal for businesses that have a mix of sensitive and non-sensitive data, and for those that want greater control over their core infrastructure.

With multicloud storage, data is stored across public cloud providers, which offers greater flexibility and scalability. Although multicloud storage can also be complex to manage, it reduces the risk of vendor lock-in by providing businesses the option to choose between different public cloud providers based on their specific needs and cost considerations. Multicloud is well-suited for businesses that want more scalability and flexibility, and don’t have as many data residency regulation concerns.

Security

In comparing multicloud and hybrid cloud environments, security plays a crucial role. Hybrid cloud setups allow organizations to implement tailored security measures across both public and on-premises infrastructures, providing greater control over sensitive data. In contrast, multicloud environments, which rely on multiple public cloud providers, often have less room for customization. While this can present challenges for specific compliance needs, many public cloud providers still meet essential standards such as GDPR and HIPAA. Ultimately, the choice between the two depends on an organization’s specific security requirements and regulatory obligations.

Flexibility

In terms of flexibility, hybrid cloud environments offer organizations the ability to seamlessly integrate on-premises and public cloud resources. This allows businesses to choose where to host specific workloads based on factors like cost, performance, and compliance. On the other hand, multicloud environments provide flexibility through the use of multiple public cloud providers, enabling organizations to select the best services from each provider.

While both approaches enhance adaptability, hybrid clouds excel in integrating legacy systems, whereas multicloud setups offer diverse options and avoid vendor lock-in, allowing businesses to respond more dynamically to changing needs.

How is Hybrid Cloud Similar to Multicloud?

Despite these differences, hybrid cloud and multicloud share many similarities. They can both be solid frameworks to store sensitive data when configured well, but they can come with common challenges, such as cloud complexity.

Infrastructure Security

Both hybrid and multicloud environments operate on a shared responsibility model, where the level of infrastructure security responsibility may vary. Cloud providers are responsible for securing the underlying infrastructure, while customers must secure their applications, data, and access controls within that infrastructure.

Key responsibilities for businesses include identity and access management (IAM), data encryption, and vulnerability management. Users should have access only to the resources necessary for their roles, whether in public or private clouds. Data must be protected both at rest and in transit, so organizations need to implement proper encryption measures. Regularly scanning for vulnerabilities and applying patches is essential to mitigate risks associated with security weaknesses, including zero-day attacks. By actively managing these responsibilities, organizations can enhance their overall security posture in any cloud environment.

Storing Sensitive Data

Even though public cloud providers offer fewer security customizations for businesses, both hybrid and multicloud environments can be suitable for storing sensitive data. Hybrid cloud gives organizations the power to place their most sensitive information on private infrastructure, whereas multicloud infrastructure allows for redundancy across multiple public cloud providers, mitigating risks from outages and data breaches.

Managing Data

In both multicloud and hybrid cloud, businesses must determine how to manage data across different platforms without compromising accessibility or performance. Hybrid clouds require tools and processes to facilitate data movement between public and private environments. While multicloud setups can simplify data management by leveraging multiple public clouds, they may still necessitate additional configuration to ensure effective data movement between those clouds.

Regulatory Compliance

Different businesses and industries are subject to different regulatory requirements, such as HIPAA, GDPR, CCPA, and PCI-DSS. Most public cloud providers are certified to meet common compliance standards, but if you have very specific needs, you may need to talk with the provider to confirm they can meet your compliance capabilities. Hybrid cloud offers more control over regulatory compliance, allowing businesses to store sensitive data on-premises or in an offsite private cloud.

Cloud Complexity

Cloud complexity is an issue for hybrid and multicloud environments, but what is being managed is where the difference resides. Hybrid cloud involves managing public and private cloud infrastructure. Multicloud involves managing different public cloud provider platforms, APIs, and security settings.

Can a Hybrid Cloud be a Multicloud?

A hybrid cloud can incorporate multicloud elements if it includes multiple cloud environments, such as a combination of public and private clouds. However, multicloud specifically refers to the use of multiple public cloud services from different providers, so it is not accurate to consider all multiclouds as hybrid clouds. While a hybrid cloud may include public clouds, it is distinguished by the integration of on-premises or private cloud resources.

Why Do Companies Use Multicloud?

Companies use multicloud to escape vendor lock-in and improve flexibility and performance across cloud environments. This isn’t a great fit for companies that have legacy frameworks they can’t easily move to the cloud. However, for businesses looking to innovate, multicloud can be a great option.

Why Do Companies Use Hybrid Cloud?

Companies tend to use hybrid cloud when they are either not completely ready to move all of their workloads to the cloud, or when moving some workloads would require more effort than it is worth, but they still want to leverage the benefits of the cloud. Hybrid cloud can serve as a happy medium or a long-term solution for digital transformation in a company, allowing for more innovation and flexibility compared to on-premises frameworks.

Find the Right Cloud Strategy For You with Cloud Experts

Choosing between hybrid cloud and multicloud hinges on your unique business needs. Data sensitivity, scalability, compliance requirements, and budgetary limitations will determine the optimal solution. Need guidance in figuring out what configuration will work best for you? TierPoint’s cloud experts can help you choose the right mix of cloud platforms that will help you reach and exceed your digital transformation goals while keeping your financial constraints and regulatory requirements in mind.

Part of adopting the cloud is convincing your leadership that it’s time to modernize your IT infrastructure. The drivers could be network performance, on-premises data center costs, and more. Read our complimentary eBook to learn how to have those conversations.

Although more companies have added cloud environments to their infrastructure, many have done so in a haphazard fashion by addressing needs as they’re realized rather than using a pre-planned strategy for cloud adoption. Those who take a piece-by-piece adoption approach are more prone to cloud sprawl, which can lead to:

• Unnecessary complexity
• Cloud budget waste
• Compliance issues
• Security gaps
• Reduced agility

To promote IT modernization and prevent future headaches associated with cloud sprawl, IT leaders should take time to develop and deploy a structured plan that will serve as a guide for implementing and governing the cloud and its resources across their organization. With that, let’s explore what exactly a cloud adoption strategy is, what challenges to keep in mind, and what to include throughout the planning process.

Why Cloud Adoption?

Because one of the biggest challenges businesses face in cloud migration is identifying app dependencies, it’s important to understand the current and future cloud environment before applying a cloud adoption framework. Businesses should be able to clearly define their objectives for cloud migration and evaluate the factors needed to find success with cloud adoption.

Organizations may choose cloud adoption to achieve the following:

• Improve scalability
• Provide better accessibility to data and applications
• Offer new opportunities for collaboration
• Save on capital expenditure costs
• Improve efficiency through automation and boosted performance
• Incorporate cloud-based services and innovate using newer technologies

And may need to consider the following factors:

• Migration complexity that may require a phased approach
• Skill gaps that may hinder smooth cloud adoption
• Existing IT infrastructure and data and what may need to change to improve the success of cloud migration
• How cloud migration will impact the business before, during, and after the project

What is a Cloud Adoption Strategy?

A cloud adoption strategy details the reason and approach an organization will take when moving to the cloud. This could include best practices, business goals, and the steps a business needs to take to achieve cloud adoption, defined by Amazon Web Services (AWS) as envision, align, launch, and scale in the AWS cloud adoption framework.

On a high level overview, an adoption strategy is the foundation for deploying and governing the use of the cloud across the entire organization, and should be created in conjunction with a cloud operating model.

Additionally, it should help the IT organization communicate the importance of cloud to the rest of the organization and explain how existing workloads and data can be moved to improve efficiency, modernize systems, boost automation and integration capabilities, and more.

Key Steps to a Successful Cloud Adoption Strategy

By assessing and planning a cloud adoption before deployment, and monitoring after migration is complete, businesses can ensure they have a more successful cloud adoption experience. Here’s what you should include in your strategy.

Assessment

Start by evaluating your existing IT infrastructure. This can include applications, data storage, and any app dependencies that need to be considered when moving to a new environment. Analyze the level of complexity and compliance needs associated with moving to the cloud, and understand any security settings that may need to change.

Planning

Your cloud adoption plan should include a definition of your objectives, identification of business factors, and creation of a cloud migration framework. Whether you’re looking to enhance data security, improve collaboration across teams, or improve business operations in some way, define your objectives early so you know how to measure success and prioritize phases.

Next, go beyond the technical considerations and evaluate the business factors relevant to cloud migration. What in-house skill sets can you draw on for cloud adoption, and where might you need to hire outside help? If your organization needs to meet certain compliance standards, one cloud provider may be more appropriate than another. You may also want to develop a data security plan to address concerns about ransomware and other cybersecurity risks. You may want to conduct a cloud adoption readiness assessment.

From there, develop a tailored cloud adoption framework that defines the migration approach you will take, the tools you will use, the timeline in which certain phases will take place, and the metrics you will use to measure success.

Deployment

After you’ve created a well-defined framework, it’s time to choose an appropriate deployment model. Each model – public cloud, private cloud, and hybrid cloud – offers unique benefits and considerations, so it’s essential to understand which one aligns best with your organization’s needs, security requirements, and budget.

• A private cloud offers dedicated resources and enhanced control, making it ideal for organizations with strict security and compliance requirements
• Public clouds, provided by third-party vendors, offer scalability and cost-effectiveness, making them suitable for businesses with fluctuating workloads
• The hybrid cloud model combines elements of both private and public clouds, allowing organizations to leverage the benefits of each. Hybrid cloud adoption enables businesses to keep sensitive data on-premises while taking advantage of the public cloud’s scalability for less critical workloads

Within the deployment model you choose, you’ll migrate relevant workloads to the cloud environment with the chosen approach, use identified tools, and adhere to established deadlines. Some applications and data may be migrated before other workloads based on dependencies and complexity. Organizations may also want to start with lower-risk applications to test the effectiveness of the approach before moving business-critical workloads.

Optimization

Because cloud optimization is an ongoing process, and not a one-time task, businesses should plan to continuously monitor their cloud environment to identify opportunities for better performance, stronger security, and improved cost efficiencies. New cloud services will also emerge in the months and years after a cloud migration. Businesses should have in-house or outside experts with a finger on the pulse of the latest technologies to continue to enhance cloud environments.

Cloud Adoption Strategy Challenges

Building a cloud adoption strategy can come with complications and challenges. Being aware of what your business might encounter, and planning for it along the way, will help your cloud adoption strategy go smoothly.

Security

Cloud computing comes with a lot of advantages, but the added ease of access and flexibility also means additional endpoints and vulnerabilities that can be used to infiltrate your business. To address these security concerns, it’s pertinent to understand the shared responsibility model in cloud security. While cloud platforms implement detailed security measures and adhere to strict regulations, the responsibility for data protection is shared between the provider and the customer. Cloud providers typically secure the infrastructure, while customers are responsible for securing their data, applications, and access management. This model emphasizes that organizations must actively participate in their cloud security strategy, implementing measures such as encryption, access controls, and regular security audits.

By understanding how cloud environments work and clearly defining security responsibilities, you can significantly improve your organization’s overall security posture and better protect assets in the cloud.

Vendors

Working with several vendors can help your organization get the exact cloud configuration you need, but it also opens the door to added complexity. Using more than one cloud provider can complicate billing, compliance, and application and workload management across all environments, not to mention potential security concerns. The better visibility you have across vendors, the less it will be a problem to operate between them.

Compliance

Compliance concerns vary by industry and region but can include data protection needs (GDPR and the like), specific procedures for sensitive financial or medical data, or complying with regulations set by an industry agency or governmental body. Best practices can be even harder to establish when compliance needs to be met in different ways on different cloud platforms.

ROI

Leadership can be slow to greenlight a project if proving the ROI is difficult. While cloud adoption can save money on capital expenditures, like hardware, physical data center rentals, utilities, and so on, the initial migration process can feel like extra spending to stakeholders who don’t see the bigger picture of a model that prioritizes automation and in-house resources. Creating a cloud adoption strategy that proposes migration in phases can help establish a lower entry point and make a case for further cloud adoption.

IT Skills Gaps

Without the right team members at the helm, it can be near impossible to execute a cloud adoption strategy or form one in the first place. Organizations are feeling the pinch from a shortage of IT skills in the market and over three-quarters of companies are looking for ways to address this discrepancy. Cybersecurity specialists alone represent a huge gap in the workforce, which currently stands at 3.4 million. Talent shortage and skills gaps in the U.S. are predicted to cause a loss of $8.5 trillion by 2030. For most businesses, looking outside the organization for providers who can be part of a cloud strategy team will be the only way to continue to modernize and stay competitive.

How to Plan a Cloud Adoption Strategy

Need help planning your cloud adoption strategy? Here are a few best practices to help you get started:

Consider the Business Value

When planning your cloud adoption strategy, you should be able to answer the following:

• How can a cloud investment help solve business problems, enable further innovation, and, overall, achieve your ideal long-time business goals?
• How will you prioritize the delivery of high-value cloud products and initiatives?
• How can you plan migration to achieve cloud success?
• How will you project and measure the impact of your cloud adoption strategy?
• Which cloud platforms will meet your governance and compliance needs?

Pick Your Platform

Thoroughly research your cloud options, and pinpoint which workloads will work best in which cloud environment – be it public, private, hybrid, or multicloud. With this information on hand, select your platform(s) and establish guidelines, principles, and guardrails for your architecture.

Keep in mind that it’s ideal to leverage platforms that have the capacity to meet your needs now and in the future so you can try to avoid a large migration if you outgrow your baseline infrastructure. With that, distributed cloud can be the happy compromise between private cloud and public cloud configurations. Multiple clouds can still be used to meet compliance, performance, or data security requirements, but with distributed cloud, they’re all managed centrally by a public cloud provider.

Define Operations and Management Guidelines

When developing your cloud adoption strategy, creating guidelines around operations and management is key. This area of your plan should include, but is not limited to, things like:

• Design principles to follow
• How to optimize operations to allow for scalability while delivering business outcomes
• Ways to improve the reliability of workloads
• Cloud environment monitoring
• How to ensure the availability and continuity of critical data and applications

Maintain Governance

Document how your cloud initiatives will maximize overall benefits for your organization while also minimizing any risks associated with cloud transformation. During this phase, set up policies, define how corporate policies will be enforced across platforms, and determine identity and access management to prevent the risk of future cloud sprawl. Additionally, consider how you can incorporate cost management and cloud cost optimization strategies to reduce unnecessary budget spend.

Establish Security, Disaster Recovery, and Resilience Practices

IT resilience can be make or break for business revenue, productivity, and reputation. Build holistic security and ongoing security management, for example a disaster recovery plan checklist and data resiliency plan. These plans include the following best practices within your security plan:

• Threat detection
• Vulnerability management
• Data protection
• Application security
• Incident response

Decrease the Talent Gap

The talent gap is one of the biggest challenges organizations have to contend with when working toward cloud adoption, and it’s a necessary obstacle to overcome. Part of your cloud adoption strategy should include promoting a culture of continuous growth and learning. Focus on providing internal learning opportunities and workshops that…

• Enhance cloud fluency
• Help transform the workplace to enable and modernize roles
• Evolve alignment with and accelerate new ways of working in the cloud

Choosing the Right Architectural Principles to Follow for Cloud Adoption

The architectural principles you follow to determine your cloud adoption should be based on your workloads, applications, what workloads/applications are most urgent to move, the characteristics and requirements of each workload/application, and any other dependencies you need to keep in mind. Try running an exercise using the 7 R’s of cloud migration (Retain, Rehost, Revise, Rearchitect, Rebuild, Replace, and Retire) to determine if you should focus your efforts on: 

• Cloud-native application adoption 
• Cloud-first adoption 
• Cloud-only adoption

Cloud-Native Application Adoption

Organizations focused on cloud-native adoption will prioritize technologies and services available via the cloud platform or provider being used, making the switch from original systems to cloud-native applications. This can look like taking advantage of tools provided by AWS and Microsoft Azure, for example.

Cloud-First Adoption

Cloud-first is when organizations always think about cloud-based solutions first before implementing a new IT system or replacing an existing one. In this scenario, you prefer to develop directly on cloud platforms from the start. There may be a reason to select an on-premises solution, whether it’s due to how it works with your other systems, the time it would take to switch things over, or necessary features not being available in cloud-based apps, but this strategy also doesn’t exclude non-cloud solutions.

Cloud-Only Adoption

With cloud-only adoption, organizations would look to cloud-based solutions to replace all of their current systems and fulfill all of their IT and organizational needs. Achieving a cloud-only adoption is manageable in theory, due to the many solutions available in the cloud. However, taking a cloud-only approach will largely depend on the in-house or

third-party resources employed to take this on, as well as how willing those who use the current systems are to change.

Accelerate Your Cloud Adoption Journey with the Help of TierPoint

Successful cloud adoption, deployment, and management all boils down to bringing in the right people who are qualified to handle your specific business requirements. Even with a robust internal team, organizations can benefit from bringing in an outside perspective. A managed services cloud provider can take your business goals, desired outcomes, and current IT environment, and help you identify the best roadmap to cloud adoption.

Need help building your cloud adoption strategy? TierPoint is here to help. We offer cloud readiness and cloud migration assessments to help build the best roadmap for your cloud adoption journey. Contact us to begin your assessment or download our Journey to the Cloud eBook to improve your cloud strategy.

How Has Data Management Evolved with the Rise of AI?

As the amount of data we consume, create, and store has exploded, with global numbers estimated to reach 180 zettabytes by 2025, data management has become even more important, and artificial intelligence (AI) can help aid in its evolution in several ways:

• Automation: AI can automate tedious, manual tasks such as classification, data cleansing, and anomaly detection through machine learning algorithms. This can allow humans to focus on more strategic work.
• Data quality: Data coming in from different sources and in different formats can be streamlined and checked for accuracy by AI, ensuring higher data quality and reliability. This results in more accurate analyses and better decision-making.
• Security: Through anomaly detection, AI can help prevent security breaches and work to protect sensitive data.
• Integration: Instead of data existing in silos, AI can bridge the gap through automation and data quality measures, allowing for a more unified view of information that can be used for further data analysis.
• Analytics: AI can also do some heavy analytical lifting that can speed up the time it takes to reach important insights. Trends, forecasts, and patterns can be visualized and calculated more easily using artificial intelligence.

The Role and Benefits of AI in Data Management

AI can play an important role in effectively managing the growing volumes of information worldwide, both in the cloud and on-premises frameworks, providing a set of tools organizations can use to unlock value in their data.

With automation, businesses can enjoy streamlined processes and reduced time and effort on manual tasks. AI-powered data quality measures can help businesses make better-informed decisions. Real-time monitoring and threat identification can better safeguard data, and real-time insights can get organizations to their next product or service decision faster than ever. In many ways, artificial intelligence empowers companies by giving them a competitive edge and a head start toward pursuing innovative new projects.

6 Top Trends in AI-Powered Data Management

Instead of businesses reacting to new data challenges, AI can put them in a more proactive role. Emerging trends include organizations leveraging AI for data cataloging, advanced analytics, intelligent data preparation, keener predictions, and more.

1. Leveraging Automated Data Cataloging and Metadata Management

Data cataloging is when organizations create an inventory of all their data. Metadata can include information such as the location of the data, its type, a description of the data, where it came from (lineage), and the owner responsible for its maintenance.

Traditionally, data cataloging is a time-consuming and error-prone manual process. AI can automate this by tagging and classifying data assets, making it easier for users to find data, fix inconsistencies, and reduce errors.

2. Performing Advanced Data Analytics and Insights

Even with deep expertise and strong deductive powers, humans can miss subtle patterns in large datasets. Machine learning algorithms can be used to find hidden patterns or identify relationships more easily, especially in complex datasets. This can move businesses from simple to more sophisticated insights. AI can also create predictive models that allow for stronger trend forecasting.

3. Conducting Intelligent Data Preparation and Cleansing

Data preparation and cleansing are important in helping individuals accurately analyze and explore data. AI can automate tasks including finding and removing duplicate data, fixing inconsistencies in formatting, and filling in missing values. This creates better data that can be used to train AI models more accurately and reliably.

4. Applying Natural Language Processing for Data Exploration

Natural language processing (NLP) gives AI the ability to understand human language. When AI can understand natural language queries, it’s easier for humans to explore datasets in a more accessible and intuitive way. NLP can automate text summarization, find topics and themes in data, categorize named entities, and conduct sentiment analysis.

5. Using Predictive Analytics and Anomaly Detection

Sometimes we don’t see what’s coming down the road before it’s too late. AI can take historical data and use it to predict future trends or find anomalies in current data. This can assist businesses in anticipating issues before they become problematic, as well as make data-driven decisions to improve operational effectiveness.

6. Automating Data Governance and Compliance

Instead of reacting to breaches, AI-powered data governance and compliance measures can prevent issues before they occur. Data access control, audit logging, and lineage tracking can all be conducted with the help of AI tools. AI can also anonymize sensitive data, identify potential security risks from anomalous behavior, and automatically restrict access to data if suspicious activity is identified.

Unlocking the Power of AI in Data Management

Managing a vast amount of data can be challenging, but the right AI-enabled data management tools can simplify the complex. TierPoint’s AI advisory consulting services can help you better navigate and leverage your in-house data to unlock its true power and potential. Contact our advisory team today to start exploring how AI can transform your data management practices.

Learn how businesses like yours can use artificial intelligence and machine learning with our complimentary whitepaper. Download it today!

If you’re using a data warehouse or a data lake, you may feel limited by your current capabilities and find it hard to untangle greater complexities. However, there is an alternative – data lakehouses. We’ll cover what data lakehouses are, what makes them different from other modern architectures, and how businesses can implement them to tackle various challenges.

Data Warehouse vs. Data Lake: Key Challenges

Although data warehouse and data lake storage architectures have played a key role in data storage and analysis, each configuration has its limitations that can keep organizations from the full potential of their data.

Data Warehouse Limitations

While data warehouses can store and analyze structured, pre-defined data for businesses, the rigidity of the schema definition required can make it difficult to accommodate new data sources or evolve the warehouse with changing business needs without significant restructuring. Data warehouses also struggle with handling unstructured data, such as images, social media posts, and sensor readings.

Data Lake Drawbacks

Data lakes can store vast amounts of data in their native format, so organizations don’t have to worry about structure. However, flexibility doesn’t come without challenges, including a potential lack of organization and data quality issues. It can also be harder to support complex queries in a data lake. Plus, the sheer quantity of data can pose a security risk without appropriate governance measures and access controls.

What is a Data Lakehouse?

Instead of having to choose one or the other, a data lakehouse offers a hybrid solution for businesses that need flexibility and scalability grounded by governance and structure. Afterall, data lakehouses combine elements of data lakes and data warehouses and can support structured, semi-structured, and unstructured data.

Key Features of a Data Lakehouse Architecture

Some of the layers that make up data lakehouse architecture include:

• Data ingestion layer: Brings data from internal and external sources into the data lakehouse
• Data storage layer: Raw data can be saved with cloud object storage and frequently accessed data can be handled by tiered storage
• Data processing layer: Prepares data for analysis with real-time pipelines and batch processing
• Metastore/data catalog: Data lineage, access control policies, and schema definitions are stored here to maintain data quality and improve data discovery
• Query engine: SQL and BI tools allow users to query and analyze structured, semi-structured, and unstructured data

A 2024 survey by Dremio found that 86% of respondents plan on unifying their data and that 70% of respondents believe half of analytics will be in data lakehouses in the next three years.

Types of Data Lakehouse Tools and Platforms

Cloud providers like Amazon Web Services (AWS) and Microsoft Azure have data lakehouse services that leverage cloud-native data processing tools and cloud infrastructure. Open-source platforms, including Delta Lake and Apache Druid, also offer core data lakehouse functionalities and can integrate with many different cloud storage solutions. Data management platforms can also have lakehouse capabilities and provide data governance, visualization, and integration capabilities. 

Benefits of Adopting a Data Lakehouse Architecture

Moving your data over to a new architecture can feel difficult, but adopting a data lakehouse architecture comes with many benefits that outweigh the cost of switching.

Improved Decision-Making

By providing a unified view of your data, lakehouses eliminate silos and centralize both your structured and unstructured data in one platform. When all data is available in the same place, businesses can conduct holistic analyses and make better data-driven decisions.

Because data lakehouses support more data formats, the configuration also allows businesses to leverage more powerful analytics tools. This can help organizations identify previously hidden patterns and predict trends with greater accuracy.

Better Performance and Scalability

When data volume and processing needs change, data lakehouses can scale to meet new demands. This improves performance and cuts down on manual provisioning. Since real-time processing is easier with data lakehouses, businesses can gain access to valuable insights much faster, giving them a competitive edge.

Simplified Data Management and Governance

Instead of being relegated to one data type, data lakehouses enforce governance policies across all data types, improving the consistency of data quality and ensuring regulatory compliance. When all types of data are stored together, the central repository makes data management more straightforward, improving the user’s ability to discover and understand relevant datasets they need to review or analyze.

Cost-Effectiveness and Efficiency

Cloud object storage is a cost-efficient way to store data in a lakehouse, meaning expenses are lower compared to more traditional solutions. Data lakehouses also cut down on the need to manage multiple disparate systems, reducing operating costs and increasing efficiency.

Use Cases and Applications of a Data Lakehouse Architecture

The versatility of data lakehouses makes them ideal for several use cases and analytical needs. Here are a few applications that may make a data lakehouse attractive to your business.

Advanced Analytics and Business Intelligence

While traditional architectures can result in siloed data, data lakehouses can create a 360-degree view of user data. This can make recommendations and user profiles more relevant, and can also help businesses identify trends to develop new products and services.

Advanced analytics and business intelligence can also enable organizations to analyze both historical and real-time information, making it easier to pinpoint patterns that may indicate fraudulent activity.

Machine Learning and Artificial Intelligence

Machine learning and artificial intelligence can predict potential equipment failures for manufacturers, provide personalized recommendations to retail shoppers, and analyze call records to find customers at risk of churning. Because data lakehouses aren’t limited in their ability to store and analyze data, machine learning, and artificial intelligence can use several different data sources for more nuanced data-driven decisions. The Dremio survey found that 81% of respondents are using data lakehouses to support AI applications and models.

Real-Time Data Processing and Streaming Analytics

Data lakehouses can ingest and process data streams from connective devices in real-time. This is important in situations where real-time decision-making is a must – for example, heath sensors on patients or sensor data from smart grids. Real-time data can also improve response time during major sales or business events, getting a handle on customer sentiment more efficiently.

What Industries Benefit the Most from Using a Data Lakehouse Architecture?

Any businesses or industries that deal with a complex array of data can potentially benefit from data lakehouse architecture.

• Financial services: Everything from customer transactions, sensitive personal data, and social media sentiment can be gathered by banks, investment firms, and insurance companies. Data lakehouses can help businesses in this industry detect fraud, mitigate risks, and personalize products for users.
• Retail and eCommerce: When customers make purchases, browse websites, and sign up for loyalty programs, the data can be aggregated into a lakehouse for a more unified view of individual behavior.
• Manufacturing: Manufacturers are leveraging Internet of Things (IoT) devices more in the production pipeline for real-time reporting on performance and operation. Data lakehouses can help with predictive maintenance, as well as optimization of certain production processes.
• Healthcare: Healthcare organizations collect sensitive health data, including patient intake forms, historical records, and imaging results. Data lakehouses can form connections between patient data and other sources of information, streamlining and personalizing treatment plans and other patient experiences.
• Government: Government agencies can be altered to emerging threats, optimize their resource allocation, and aggregate smart city sensor data in a data lakehouse.

How to Evaluate if a Data Lakehouse Architecture is Right for Your Business

For some businesses, traditional data architecture will be enough. However, if you’re struggling with data volume, variety, or management, or you’re not getting enough out of analytics, you may want to make the switch to a data lakehouse.

Data Volume and Variety

If your organization amasses a large volume of data, either structured or unstructured, handling the scale with a data lakehouse can be worth the investment. You’ll also want to think about the variety of your data. If you have some structured databases, some sensor data, and information you want to collect from social media feeds, data

lakehouses can help you manage and store a variety of formats, giving you a unified platform for your data.

Analytics Requirement

Traditional architectures can accomplish simple reporting, but if you’re looking for more advanced analysis using AI or machine learning or looking to combine data from different formats into one reporting platform, data lakehouses can help you form deeper analyses and reach more nuanced insights.

Current Data Management Challenges

Think about your current data management struggles. If data silos, limited storage options for unstructured data, or data governance issues exist due to your present data architecture, lakehouses can help.

How to Design and Implement a Data Lakehouse Architecture

The more careful you are in planning your data lakehouse architecture, the more success you’ll have in implementation. Here are the steps businesses should follow when designing their ideal data lakehouse setup.

Outline Your Business Needs and Goals

Your business needs and goals will shape what your data lakehouse looks like and what services you choose to support it. Start by analyzing the different types of data you need to store and access and their level of structure. What current data sources are you storing, and what might you want to add once you incorporate a data lakehouse?

A data lakehouse should work for your business and the specific problems you want to solve. By identifying your use cases early, you can start formulating your data ingestion strategy, governance policies, and list of potential tools.

Knowing what success will look like for you can also help you track how the data lakehouse has impacted your business. Do you want to speed up your decision-making, improve your ability to conduct data-driven marketing campaigns, or optimize your resource allocation? Establish the metrics you will use to track success early.

Research and Select Your Cloud Platform and Services

Decide whether you want to work with a major cloud provider, such as AWS or Azure, or a third-party provider for data lakehouse services. Your ultimate decision will come down to a combination of features, integration possibilities, pricing, scalability, and tools that each platform carries and supports. Your cloud platform should work with whatever services you choose, whether they are open-source or commercial

Define Your Data Ingestion Strategy

How will data be extracted from databases, social media platforms, applications, IoT devices, and any other sources you may want to pull into your data lakehouse? What should be streamed in real-time and what can be batch-processed?

Once you know how you want data to come in, you’ll also want to establish a process for transforming, cleaning, and validating data before it goes into the data lakehouse. This can improve consistency and data quality.

Outline Your Data Architecture and Governance Principles

Even though data lakehouses can store structured, unstructured, and semi-structured data, you will want to outline guidelines for data schema and structure based on how you want to use them. To maintain data usage and regulatory compliance, create policies for access control, data security, and data retention.

Establish Your Security and Access Controls

Protect your repository of structured and unstructured data through access controls, intrusion detection systems, and encryption, keeping bad actors out via multiple tactics. Not all users will need access to all data held in the data lakehouse. Assign read, write, and modify permissions based on user roles and responsibilities held at the business to bolster security.

Develop a Plan for Monitoring, Optimization, and Management

Address new issues quickly by implementing monitoring that checks for data quality and system performance. Explore cost-optimization strategies based on data storage usage and designate a team to conduct ongoing data lakehouse management. This can include security updates, performance optimization, and user support.

Need Help Managing Your Data?

Data lakehouses offer a powerful solution for organizations struggling with data volume, variety, or management limitations in the cloud. But determining the best-fit cloud environment to support data lakehouse architecture requires careful planning, expertise, and the right cloud partner. At TierPoint, our team of cloud experts can help guide you in the right direction – contact us today to learn more. In the meantime, download our whitepaper to explore different cloud options available for data management.

What is Serverless Computing and How Does it Work?

Serverless computing, aka “serverless”, is an approach to building and running applications without having to manage the underlying infrastructure, such as virtual machines or servers. While serverless computing doesn’t mean there are literally no servers involved, it abstracts away the need for developers to manage them directly. How? A cloud service provider handles the provisioning and scaling of the computing resources needed to run the application code. The provider also maintains and scales the servers behind the scenes, allowing developers to focus on writing the application code rather than worrying about infrastructure.

For serverless computing to work, you simply write your application code, package it as functions or services, and the cloud provider takes care of executing that code only when it is triggered by an event or request. For example, an incoming email may trigger a task to log the email somewhere, which is executed via the serverless platform.

Benefits and Challenges of Serverless Computing

Serverless computing is a unique way to run applications, and it also comes with unique benefits and challenges.

Benefits of Serverless

• Lower Operational Costs: Because there’s no server management overhead, businesses can save money on infrastructure provisioning, scaling, and management. When the code is invoked, those are the resources you’ll pay to use.
• Improved Resource Utilization: Serverless computing eliminates the need to manage idle servers. Instead, resources are dynamically allocated and scaled up or down based on real-time application needs. This on-demand approach translates to significant cost savings since you’ll only pay for the resources you use, and the resources are also used more efficiently.
• Automated Scaling: Traffic spikes and dips can be accommodated easily through serverless architecture, improving performance without the need for manual action.
• Improved Developer Productivity: Without the complexities of managing a server, developers can focus on application-centric matters, such as core logic and improving development cycles.
• Faster Time to Market: Because serverless computing can speed up development cycles, it can also cut down on the time it takes to get a product to market.

Challenges of Serverless

• Complexity with Long-Running Processes (LRP): Serverless isn’t the best approach for long-running computations – applications that may run a process for hours or days. These can create complexities that serverless platforms can’t handle, due to potential inconsistencies in performance and higher costs to run the applications.
• Security Concerns: While serverless applications offer development and scalability benefits, they introduce unique security challenges including vulnerabilities in the code itself, unauthorized access to resources due to misconfigurations, and data privacy concerns. To mitigate these risks, developers must use strong security best practices, such as implementing proper authentication and authorization mechanisms, encrypting sensitive data, and regularly conducting security audits.
• Vendor Lock-In: It can be hard to move to another vendor later if businesses rely heavily on a certain cloud provider’s serverless platform. This may prevent you from getting the best pricing or service you could have.
• Cold Starts: Unlike traditional applications that are constantly running in the background, serverless functions spin up when needed. This can introduce a noticeable latency spike when a function is first invoked because the environment needs to be initialized. This initial delay can be a concern for applications where responsiveness is critical, such as real-time chat or mobile gaming.
• Problems with Debugging and Monitoring Capabilities: Traditional server environments are perfect for monitoring and debugging tools, but these tools may not work as well in serverless environments. So, transitioning to serverless often requires developers to embrace new tools and workflows specifically designed for this environment. These tools might include function-level monitoring solutions and cloud-native observability platforms that cater to serverless functions.

Use Cases for Serverless Computing

Serverless computing can be used in several scenarios as a flexible development model. Microservices and event-driven APIs are a perfect use case for serverless platforms. Data streams can also trigger serverless functions, allowing for real-time analytics and data processing. Internet of Things (IoT) devices, the back end of web and mobile applications, and content and delivery networks (CDN) can all use serverless platforms. Serverless functions can even be chained together to create a workflow, automating complex tasks with a series of events.

What Are Containers and How Do They Work?

If you’re looking to virtualize on the operating system (OS) level, containers may be the right fit. Containers are software units that can package together application code, libraries, and dependencies. These lightweight and portable units can run on the cloud, desktop, or a traditional IT framework. With containers, multiple copies of a single parent OS can be launched with a shared kernel, but unique file systems, memories, and applications.

Containers work by first creating a container image with code, configurations, and dependencies. This image is used to create a container instance when the application is run. Sometimes, multiple containers may need to operate together, which is where orchestration tools may play a role in ensuring that containers are started and stopped at the correct times.

Benefits and Challenges of Containers

While containers can be lightweight, portable, and easy to scale, they can also come with challenges businesses should weigh before deciding to use them.

Benefits of Containers

• Portability: Containers can be seamlessly deployed across different environments because they can run well on any system that has a compatible container runtime (what loads and manages containers). This makes testing, development, and production easy.
• Isolation: Containers offer process-level, a powerful isolation feature. This means applications run in self-contained environments, unable to directly impact each other or the underlying host system. This isolation is crucial for security and stability as it prevents conflicts between applications and their dependencies.
• Scalability and Improved Use of Resources: Application demands can cause containers to scale up or down, improving resource allocation. Hardware is also utilized more effectively with containers since they share the host kernel with other containers.
• Simplified Collaboration and Development: Collaboration is improved with standardized container images. These consistent and collaborative environments also foster more efficient development lifecycles.

Challenges of Containers

• Security: Isolation can give containers a valuable security boost. However, security vulnerabilities can still exist in the container image or the runtime. With that, it’s crucial to implement strong security measures and regularly update container images to help decrease security risks.
• Storage: Resources can be optimized with containers, but if businesses aren’t careful, container deployments can pile up and result in more container images than are necessary. Storage management is critical.
• Orchestration Complexities: When you’re using multiple containers to pull off a complex deployment, it’s easier to manage each individual container, but arranging them together properly can pose more of a challenge.
• Vendor Lock-In: Like serverless computing, some containerization tools may only work with one cloud provider, leading to vendor lock-in. Look for vendor-neutral solutions if you are concerned about this.

Use Cases for Containers

Containers can also be used with microservices applications like serverless platforms, but they have many other use cases as well. Cloud-native development depends on containers, which can be used to scale seamlessly across cloud environments. Continuous integration and delivery (CI/CD) pipelines can be aided by containers, which offer consistent environments throughout the development lifecycle. Businesses can even choose to modernize legacy applications through containerization, removing a barrier to cloud migration. Using containerization is also appropriate for emerging technologies such as machine learning, high-performance computing, big data analytics, and software for IoT devices.

Serverless vs. Containers: Key Differences

While serverless and containers can be used in similar ways, there are some key differences between the two technologies.

Architectural Differences

With serverless, businesses don’t have to worry about server infrastructure, just the code. Containers are a self-contained unit that includes the application code, configuration, and dependencies. Developers retain some level of responsibility for managing underlying servers.

Scalability

While both serverless architecture and containers offer scalability, there’s a difference in how scaling is managed. Containers need to be scaled using orchestration tools like Kubernetes, which manage the deployment, scaling, and management of containerized applications across clusters of servers. In contrast, cloud providers handle the scaling automatically in serverless environments, abstracting away the underlying infrastructure management tasks from the developers.

Deployment, Management, and Maintenance

Businesses enjoy a simplified deployment process with serverless platforms. A cloud provider will handle the infrastructure updates and management. This is different from containerization, where developers will need to manage container images, orchestration, and servers.

Testing

Containers are easier to test, because they offer a more controlled environment that resembles production. Serverless configurations, on the other hand, can be harder to test because serverless functions are more ephemeral.

Lock-in and Portability

Lock-in is more of a problem with serverless compared to containers. Code is more likely to be specific to a certain cloud provider with serverless platforms. You can find open-source and vendor-agnostic container tools for vendor neutrality more easily.

Factors to Consider When Choosing Between Serverless vs. Containers

Because there are distinct differences between going serverless and using containers, businesses need to carefully evaluate their needs and capabilities to decide what will work best for them. You may want to consider the following when making your decision.

Application Requirements

Short-lived, event-driven tasks that have unpredictable traffic are perfect for serverless. Comparatively, containers work well for long-running processes and applications. Predictable workloads are better for containers, whereas automatic on-demand scaling is best for serverless.

Cost and Pricing Models

Serverless billing is a pay-per-use situation, which can be cost-effective for applications that experience sporadic traffic. If traffic is consistent at a certain level, especially a high level, containers may be better. Vendor lock-in can mean not being able to take advantage of competitive pricing models, which can be a bigger problem for serverless.

Development and Operational Complexities

Businesses looking for lower levels of complexity around server management will be happier with serverless environments. Containers require more configuration at the start and are more for businesses to manage on their own. Because serverless applications are more ephemeral, debugging and monitoring can be more complicated compared to containers.

In-House Development Expertise

You’ll also want to consider what your team can already do. If your team is well-versed in container orchestration, containers can be the way to go. If you’re looking for a shorter learning curve, serverless may be for you. However, both technologies can require additional training, as well as a working familiarity of cloud platforms.

Security

The shared responsibility explains who is responsible for the security of different components in an infrastructure. Businesses need to secure the code and data in serverless environments. Containers require businesses to secure the container image as well as the host environment. Cloud providers will patch infrastructure for clients in serverless environments, while businesses will need to scan and update container images themselves to combat vulnerabilities.

Integration with Current Infrastructure

If you’ve already invested in container orchestration or virtual machines, adding more containers can be a more efficient next step. However, if you haven’t invested in any new infrastructure, serverless can provide a much-needed jumpstart.

Can You Use Both Serverless and Containers?

When considering the merits and drawbacks of serverless computing and containers, you may think you have to pick one, but there are some scenarios where choosing a hybrid architecture may be more appropriate. For example, if you have event-driven tasks with fickle traffic, you can implement serverless functions to handle them. More predictable operations can be served by containers.

Choosing the Right Technology for Your Objectives

Once you thoroughly understand your project’s requirements, your existing resources, and your development team’s capabilities, you can select the technology that is right for you. Consider the workload type, scalability needs, security implications, budget, existing infrastructure, team skills, and ability to handle a learning curve in the decision-making process.

Need some support to make your decision? You don’t have to decide solo. Learn more about our IT advisory services and talk with a member of our team today.

FAQs

What is Data Gravity? 

The concept of data gravity describes the gravitational pull data exerts on other data, applications, and services. With cloud computing, data gravity often manifests as large volumes of data accumulating in a specific data storage service. As this data mass grows, it attracts more applications and services, creating a concentrated hub of data activity. This phenomenon isn’t limited to cloud environments; it can scale up to describe similar effects within data centers.

What Causes Data Gravity?

While some people may operate on an “inbox zero” philosophy, many others may allow their inboxes to pile up, and they may find that the fuller the inbox gets, the less they pay attention to deleting new emails. This inertia also describes how data gravity works – as data grows in a specific area, it’s easier for more to accumulate in the same area.

It can also be difficult to move, organize, or delete data when applications are dependent on certain datasets. Data governance policies can also restrict the movement of certain sensitive data subject to regulatory standards.

According to the 2023 Data Gravity Index 2.0 report, the shift from a physical economy to a digital economy is one of the driving forces behind data gravity. By 2025, it is estimated that 80% of data will live within enterprises. Plus, compliance requirements will mandate that IT leaders retain copies of customer data for longer. Some data gravity is unavoidable, but there are also unnecessary accumulations businesses should work to mitigate.

How Does Data Gravity Affect Cloud Environments?

Because data isn’t necessarily tangible, it can be difficult to see how data gravity impacts cloud environments. However, there are several negative impacts and cloud risks associated with data gravity.

Performance Degradation

When large datasets are localized to one cloud location, they can put a strain on resources in that area and decrease processing times. This can cause drags on application performance and lead to poor user experience.

As artificial intelligence and machine learning use become more prevalent, performance will become an even bigger priority. The Data Gravity Index 2.0 predicts that an increase in these tools will likewise increase data gravity.

Latency and Network Congestion

Latency is a particular performance metric that can be strained with data gravity. When data needs to be accessed frequently from users in geographically dispersed locations, low latency is vital. However, data gravity can increase the time it takes for data to travel from applications to users. Longer processing times can also result in more users on the network at any one time, increasing congestion.

Cost Inefficiencies

Data storage costs can start to creep up with data gravity, leading to significant budgetary drains over time. Worse yet, organizations may be paying for inactive or redundant data without noticing.

Data Management Complexity

As data increases, cloud becomes more complex, making it harder to manage and govern. Keeping the quality and access control consistent becomes harder due to data gravity.

Security, Regulatory, and Compliance Concerns

Security and regulatory concerns also become more difficult. Depending on where the cloud storage is located, data residency and sovereignty laws might be in effect, and risks go up with a greater concentration of data.

5 Strategies for Mitigating Data Gravity in the Cloud

Just because inertia has led to more data that you can handle doesn’t mean you can’t counteract it. Here are a few ways you can mitigate the effects of data gravity in your cloud infrastructure.

Data Classification and Prioritization

Some data is more critical for safety or daily operations reasons. Classifying your data based on how critical it is for company operations and access frequency can help you prioritize where it is stored.

High performance cloud tiers cost more and should only be used for data that requires high performance, low latency, and stringent security controls. Data that isn’t accessed as frequently can be either archived or put into lower-cost tiers.

Data Archiving and Deletion

As data comes in, there should be a plan for how to treat it during its lifecycle. Some may be destined for deletion or archival, which can help free up resources, reduce storage costs, and improve performance. A data lifecycle management plan can serve as a framework for these data decisions. Deleting data securely can also keep security risks lower.

Data Compression and Optimization

Another way you can reduce the strain from data in the cloud is through compression and optimization of storage efficiency. Specific data types may work well with certain compression algorithms, for example, while other data can be optimized through the removal of duplicates or conversion to more efficient formats.

Data Lakehouse Architecture

Data lakehouses can centralize your data storage and enable more advanced analytics. Serving as a hybrid solution, data lakehouses offer both the flexibility of data lakes and the structure of data warehouses. Businesses can store data of different types while maintaining strong data quality and analytics capabilities. When it comes to data gravity, data lakehouses can reduce siloing, leading to more efficient storage setups.

Hybrid and Multi-Cloud Strategies

Various data needs and access requirements can be met with more specificity. For example, sensitive data can be stored in a private cloud, whereas often-accessed data can exist in a high-performance public cloud environment. Tiering data storage can also reduce reliance on any one vendor. Another option is utilizing a cloud service like Managed Azure Stack to help organizations leverage the full range of Azure to gain insights from their data.        

Building a Data-Centric Cloud Strategy

The best time to get a handle on your data is right from the beginning of your cloud architecture design. If you’re about to make the move to the cloud, ensure that data gravity mitigation strategies are built into the foundation of your plan. TierPoint can help you manage your data and build a path to the cloud with optimization and performance top-of-mind. Contact us to learn more.

What is Cloud Efficiency and Why is it Important?

Cloud efficiency is important primarily because cloud resources feel less tangible. When businesses focus on cloud efficiency, they’re striving to make the most of their cloud resources. It’s easy to end up paying for resources that aren’t being used, and cloud efficiency counters this by focusing on eliminating waste, optimizing performance, and improving agility, all while creating a more sustainable cloud environment. 

The Impacts of Cloud Inefficiency

Cloud computing can improve scalability and efficiency for businesses, but it doesn’t come without its challenges. There are significant business impacts, as well as environmental impacts, to cloud waste.

Cloud bills can rack up if businesses aren’t paying attention to usage. Because cloud resources can be less visible, the strain on your budget can feel more subtle. Vendor lock-in can also leave organizations feeling stuck with their current provider without adequate leverage to renegotiate contracts.

Excess cloud resources can also cause performance drag and create unnecessary security vulnerabilities. Downtime and data breaches can not only be costly from a financial standpoint, but from a reputational standpoint as well.

Using unnecessary resources in the cloud can also come with negative environmental impacts. Approximately 3 to 4% of global emissions come from the digital sector, and that number is expected to double by the end of next year. High-performance computing, artificial intelligence, machine learning, and 5G connections will all contribute to this spike. Businesses that are able to streamline their usage will save money and improve their sustainability.  

8 Ways to Improve Your Cloud Efficiency

When improving cloud efficiency, businesses may want to implement one or several measures to optimize their cloud environment and reduce the resources they’re using. Here are 8 ways you can start.       

1. Implement Comprehensive Cloud Cost Management

By leveraging cloud cost monitoring and forecasting tools, you can gain real-time insights into your cloud spending with information on resource usage, trend predictions, and cost breakdowns.

Some cloud cost management techniques include:

• Rightsizing Resources: Real-time reports can help you ensure virtual machines, storage, and other resources are appropriately sized for your workload demands.
• Using Reserved Instances: Cloud resources can be purchased at a discounted rate with reserved instances. This can be ideal for predictable workloads but shouldn’t be used for usage that varies greatly.
• Automating Cloud Cost Management Processes: Automated rules can be used to scale instances based on predicted usage patterns and shut down idle resources. Businesses can also set up alerts for anomalies in activity.

2. Optimize Storage

Cloud efficiency can be improved via storage optimization, which can be done in a few different ways. One key strategy is implementing a tiered storage architecture, where data is categorized based on access requirements and stored on appropriate media types. Frequently accessed data should reside on high-performance storage like solid-state drives (SSDs), while less critical data can be archived on lower-cost options such as object storage or tape. This approach ensures that data is stored in the most cost-effective and performance-optimized manner.

Another technique for storage optimization is data deduplication and compression, which reduces the storage footprint and associated costs by eliminating redundant data and compressing files before storing them in the cloud. This minimizes the amount of storage provisioned and the data transferred over the network, leading to significant cost savings. Additionally, organizations can automate data lifecycle policies to transition infrequently accessed data to lower-cost storage tiers or archive services, ensuring that data is stored cost-effectively based on access patterns and storage resources are used efficiently over time.

3. Improve Cloud Operational Efficiency

Automating infrastructure provisioning and application deployment workflows can reduce costs associated with operations, decrease the number of manual errors, and improve consistency in the cloud provisioning process. Two strategies to improve cloud operational efficiency include incorporating Infrastructure as Code (IaC) and adopting an internal DevOps culture.

Infrastructure as Code

Infrastructure as Code (IaC) can be used to define a cloud environment using code. This approach unlocks benefits like version control, repeatability, and streamlined infrastructure management.

Adopt a DevOps-Centric Culture

Embrace a DevOps culture to bridge the gap between development and operations. This fosters seamless collaboration, allowing teams to continuously optimize cloud deployments, boost efficiency, and respond swiftly to evolving business demands.

4. Drive Cloud Performance and Reliability

When you’re monitoring resource utilization, you can use the information gathered to improve cloud performance and reliability. Regularly monitor resource utilization and application performance to identify potential performance bottlenecks.

Reliability is a big part of cloud resilience – ensuring that your cloud environment will continue to be operational after a disruption. Cloud-based disaster recovery solutions can provide comprehensive protection for your environment. After implementation, schedule times to regularly review and test your disaster recovery plans.

5. Streamline Cloud Governance and Compliance

Establishing a cloud governance framework can ensure you are following well-defined procedures and policies around cloud cost optimization that can be easily shared organization-wide. A central team that has expertise in cloud governance, security, and compliance – a cloud center of excellence (CCoE), can provide guidance and best practices for cloud adoption and optimization in your business.

6. Consider Integrating Edge Computing

Geographically dispersed users and applications that benefit greatly from low latency can be supported with edge computing that brings processing closer to the end user. By reducing the distance data needs to travel, edge computing can dramatically decrease the costs associated with data transfer, especially for organizations with globally distributed users and applications. This not only reduces bandwidth costs but also minimizes the risk of network congestion and bottlenecks, leading to improved performance and a better overall user experience.

Keep in mind that rather than replacing cloud with edge, organizations typically adopt a hybrid cloud and edge computing strategy. This allows certain workloads and data processing tasks to be performed at the edge, while others are handled in the cloud, leveraging the strengths of both architectures.

7. Design New Applications to be Cloud-Native

While you may need to continue to use and integrate legacy tools in your new cloud environment, designing new applications with a cloud-native approach can improve your resource utilization and cost efficiency over time. This can look like using microservices architecture to break down applications into smaller, more independent services, or leveraging containerization technologies to package applications and dependencies together.

8. Build an Internal Culture of Cost Awareness

While governance documentation, such as cloud policies and guidelines, can indeed set the initial tone for cloud cost expectations. However, to truly foster a cost-conscious mindset throughout the organization, businesses must go beyond mere documentation and actively promote and reinforce cloud cost ownership across teams.

One effective approach is to invest in comprehensive training and educational programs tailored to different roles and responsibilities within the organization. These programs should aim to empower team members with the knowledge and skills necessary to make cost-conscious decisions when working with cloud resources.

For developers and engineers, training could focus on best practices for designing and building cost-efficient cloud architectures, optimizing resource utilization, and leveraging cost-effective services and pricing models. This could include hands-on workshops, coding challenges, and real-world case studies that highlight the impact their decisions can have on cloud costs.

For project managers and business stakeholders, training could emphasize the importance of incorporating cloud cost considerations into project planning, budgeting, and decision-making processes. This could involve sessions on the impact of capital expenditures vs operational expenses, cloud cost forecasting, chargeback models, and techniques for aligning cloud spending with business objectives.

Ready to Optimize Your Cloud Environment and Improve Efficiency?

Navigating the complexities of cloud computing and optimizing your cloud environment for efficiency, performance, and cost-effectiveness can be a daunting task to do alone. At TierPoint, our team brings a wealth of knowledge and experience to the table. With a deep understanding of the latest cloud technologies and best practices, our cloud consultants can give you the guidance you need throughout your digital transformation.

In the meantime, download our whitepaper to discover how cloud optimization drives ROI and additional ways to help optimize costs.

What is Cloud ROI?

Cloud ROI measures the financial benefit an organization gains by adopting cloud-based solutions compared to the initial and ongoing costs associated with them. While moving to the cloud can include an upfront investment, cloud ROI demonstrates how the investment will generate returns over time.

Measuring Cloud ROI – How is it Calculated?

It can be difficult to get an accurate calculation of cloud ROI when there are so many parts that may be added and removed during a cloud migration process. However, the basic calculation involves starting with the total cost of ownership for moving to the cloud and acknowledging savings earned from equipment, facilities, and components that are no longer needed.

Gains from the investment can be in the form of equipment savings, a decrease in licensing fees, savings on property costs, and more. Once those have been identified, organizations can take the gain minus the investment and divide it by the investment to get the cloud ROI.

It’s important to note that the ROI may not be positive immediately due to the total cost of ownership included in the investment. Making the initial switch can take a lot of time, require outside skills, and require a calculation of operating expenditures (OpEx) versus capital expenditures (CapEx).

Common Challenges in Measuring Cloud ROI

We’ve already mentioned that calculating cloud ROI can be a complicated endeavor, and this is due to a combination of complex cloud pricing, a need to quantify intangible benefits, and difficulty aligning business objectives with cloud investments.

Complex Cloud Pricing and Billing Models

Cloud pricing can be confusing for the initiated. Even seemingly straightforward monthly or annual licenses can come with hidden fees associated with going over set limits. Understanding how each cloud structure works, and which instances are right for your workloads, will untangle cloud pricing complexities.

Quantification of Intangible Benefits

While you will be able to quantify much of the savings about cloud migration, certain benefits are intangible or much harder to measure, such as increased agility or improved collaboration. You may be able to quantify this over time by looking at productivity levels and output before and after cloud implementation, but capturing this information can be more difficult.

Alignment of Business Objectives and Cloud Investments

Just because cloud computing is continuing to gain steam doesn’t mean that it makes sense for your business. You need to think about your objectives – where are you trying to go in the next year, the next five years, or the next decade? Organizations looking to compete in the digital landscape will likely benefit from cloud migration. However, if you have legacy applications or workloads that are hard to migrate, or your leadership team is not on board with making changes, it can be hard to align objectives with investments in cloud computing.

Tips for Selling the Value of the Cloud to Leadership

That being said, how do you get everyone on board if you feel that cloud migration is right for your business and would generate cloud ? Here’s how you can sell the value of cloud to leadership.

Craft a Compelling Business Case

Your business case for selling to the cloud to leadership should clearly communicate the strategic value of cloud adoption. This may be about how the cloud can enable better business agility and application performance, or how it can aid in your disaster recovery planning. Cloud optimization can bring several benefits, including improved performance, better connectivity, greater ability to scale resources, and so on. Identify which cloud features are most strategically beneficial to your business and use them in your pitch.

Clearly Outline Business Needs for Future Innovation

Selling a vision for the cloud isn’t just about your present situation, but about your future as well. The cloud enables rapid innovation by making faster development and deployment cycles possible. Cloud infrastructure can also power more demanding workloads, such as high-performance computing and artificial intelligence / machine learning (AI/ML).

Aging data centers can slow your progress and prevent future innovation. Conversely, the cloud can serve as an intelligence platform that can store large blocks of infrequently accessed data, achieve quicker response times, and serve as a safe repository for customer interactions.

Create a Cost-Benefit Analysis

A cost-benefit analysis should cover 5 years and include the following elements:

• Capital equipment savings
• Increased reliability and redundancy
• Energy cost savings
• Real estate expenditure savings
• Efficiencies in staffing

Creating one can clearly demonstrate the bottom-line benefits cloud infrastructure can bring to a business.

Determine the Right Cloud Environment

The more well-researched your case for cloud is, the more likely it is to be picked up by leadership. Conduct some research to determine which environments may be best suited for your goals. Depending on the nature of your business, public, private, muticloud, or hybrid architectures may be appropriate.

Suggest a Phased Approach

Changes don’t need to happen all at once. You could create a cloud adoption strategy that includes a phased approach and focus on low-risk, high-impact projects. Although the move to cloud requires an upfront investment, stepping into new projects can be an easier sell to leadership.

Clearly Communicate with Stakeholders

Whatever you decide to share, be sure to clearly communicate your goals, expected benefits, and implementation steps with stakeholders. Use the presentation to address concerns and reaffirm long-term benefits.  

Choosing the Right Partner for Cloud Success

One of the best ways to improve cloud ROI is by working with experts who are experienced in cloud migration. TierPoint’s experts understand the considerations and potential pitfalls that may get in the way of successful cloud adoption. Whether you’re considering a phased approach or a bigger project, we can help you plan and sell the cloud to your leadership team. Download our whitepaper to learn more.

DevOps teams rely heavily on cloud automation. This organizational structure brings together software development and operations team members to improve the development and deployment process. However, cloud automation can improve business processes in many other ways outside of this team.

Here’s how you can get the most out of cloud automation and grow with evolving technology.

What is Cloud Automation?

Cloud automation is the practice of using different approaches to reduce human intervention in tasks related to cloud computing environments. It involves implementing tools and processes that automate the provisioning, configuration, management, and optimization of resources and services in the cloud.

At its core, cloud automation enables the automated setup and deployment of virtual machines (VMs), containers, storage, networks, and other infrastructure components on-demand. This is made possible through the use of Infrastructure as Code (IaC), which allows organizations to codify their infrastructure resources into text-based configuration files. These IaC files can then be versioned, tested, and automatically deployed through cloud automation workflows.

After cloud resources are set up, automation can be used to put ongoing tasks on autopilot, such as performance monitoring, software patching, and resource scaling. IaC plays a role here as well, ensuring that the configuration of these cloud resources is maintained consistently across environments according to defined policies and standards which helps minimize manual errors and drift.

How Does Cloud Automation Work?

Cloud automation works by taking every day, manual processes and making them run automatically. Organizations can automate deployments in several different ways, but common approaches involve using artificial intelligence (AI), IaC, or configuration management tools to define the outcome you want from a given trigger or inciting event.

A trigger could be a specific time of day, a desired action, or a code push that incites an action or a series of actions to take place. Businesses may choose to automate provisioning resources, such as storage or servers; application deployment; security settings; or steps in a workflow to welcome new customers. Any tasks with predictable, repeatable steps may be able to be automated.

Types of Cloud Automation

Cloud automation encompasses many tools and practices, so there are a number of different types of cloud automation.

Some of the most common forms of cloud automation include:

• Infrastructure Provisioning: Automating the process of setting up and managing cloud resources, often using IaC to define and manage infrastructure through code. This includes automating the provisioning of serverless architectures.
• Monitoring and Remediation: Automating the monitoring of cloud environments to detect and diagnose issues and leveraging observability tools to provide insights into system behavior, performance metrics, and log data.
• Application Deployment: Automating the deployment of applications to streamline the release process, integrating DevOps tools and practices to automate building and testing.
• Configuration Management: Automating the maintenance of consistent configurations across multiple cloud environments, detecting and remedying configuration drift to ensure systems remain in the desired state.
• Security: Integrating security practices into the DevOps pipeline (DevSecOps), automating the implementation of security controls, vulnerability scanning, and compliance policy enforcement.
• Workflow Orchestration: Automating the sequencing and coordination of complex cloud-related tasks and processes, improving agility, scalability, and reliability in cloud operations.

Advantages of Cloud Automation

Repetitive tasks can add a lot of time to your day without you realizing it. Automation frees team members up from repetition, saving time and allowing them to focus on more interesting activities.

Manual tasks are also more prone to human error, something that cloud automation can greatly reduce. When you automate deployments, you speed up tasks like provisioning and can bring applications and services to market more quickly.

Optimized resources and processes will also save your organization money over time. According to NetApp’s 2023 State of Cloud Ops report, 82% of organizations believe that automation is either “critical” or “very valuable” when it comes to improving return on investment and optimizing operations in the cloud.

Disadvantages of Cloud Automation

Before starting any cloud automation project, it’s important to get leadership on board with the initial investment in time and money. The payoff of cloud automation comes after implementation, but the upfront investment in tools and training has to be factored into an organization’s budget.

Cloud automation offers a lot of freedom and flexibility, but businesses may still experience vendor lock-in when they use public cloud provider-based tools to configure automations. And, while cloud automation can significantly reduce errors, if there is an error in the automation itself, this problem can become amplified.

Despite 95% of organizations having some level of automated cloud operations, only 15% currently have “significant” levels of automation. Part of this could be due to the initial investment needed to implement cloud automation. It’s important to start slow and work with people well-versed in cloud automation to minimize the disadvantages.

When Would You Use Cloud Automation?

The opportunities to use cloud automation are vast and growing, but here are a few common use cases where eliminating manual tasks can be valuable.

Virtual Machine and Storage Provisioning

Cloud automation tools can streamline the VM and storage provisioning process by automatically provisioning VMs based on pre-defined specifications for CPU, memory, storage, and operating systems. You can also create automations to dynamically allocate storage to optimize resource utilization, providing what your applications need, when they need it.

Resource Scaling

Cloud resources can also be scaled up or down as needed. When automated, resource scaling can optimize performance during peaks in demand and reduce costs during lulls.

Network Configuration

Virtual networks, security groups, and subnets are all important parts of cloud management, but they can be time-consuming to do manually. Automated network configuration can create these tasks and help businesses set up secure, reliable network environments in the cloud.

Application Development, Deployment, and Management

Cloud automation is closely tied to DevOps. Application development, deployment, and management can be automated as part of a continuous integration / continuous development (CI/CD) pipeline, allowing continuous delivery of new features and updates while building in automated steps at multiple points of the development process.

Vulnerability Scanning

It’s hard for a team, let alone one person, to scan and identify every potential vulnerability in a cloud environment. Even the most connected cybersecurity experts may miss a key update or be unaware of an emerging threat with a zero-day vulnerability. Cloud automation can include regular vulnerability scans of your environment, identifying vulnerabilities and even generating responses to more severe threats.

Identity Provisioning

Another source of vulnerability concerns your team members. Employees should receive different levels of access based on their roles and responsibilities. Automations can make this process easy by pre-defining access according to someone’s position and scope of work in the company. You can also create automations to quickly revoke access should someone leave the team.

Cloud Cost Monitoring and Reporting

Cloud usage can get out of hand without monitoring tools in place. Cloud cost monitoring and reporting improves your visibility over spend in your cloud environment. Automations can send notifications for uncharacteristic spikes in usage and suggestions for cost optimization.

How to Maximize Cloud Automation?

Organizations engaging in cloud automation best practices will have well-defined goals before focusing on tools and increasing scope. They’ll also know to start small, build over time, and test and monitor their automations regularly. Here are some steps you can take to make the most of your cloud automations.

Define Goals and Determine Use Cases

First, define your goals for taking on a cloud automation project in the first place. Is your business looking to speed up certain processes, reduce the risk of human error, or optimize the use of your current resources? Your goals will determine your use cases, which will also lead to the right tools and approaches. Cloud infrastructure provisioning, security patching, application deployment, and configuration management all have different steps and tools.

Leverage the Right Tools

Leveraging the right tools, combined with a strong implementation and configuration plan, will help you automate in effective ways. Configuration management tools, such as Ansible and Puppet, can enforce consistent configuration across cloud resources. AI tools can also be used to automate the provisioning and configuration of cloud resources like virtual machines, containers, storage, and networks – this includes automating tasks like scaling resources up or down based on demand.

Businesses that use containerized applications can benefit from container orchestration platforms – Azure and AWS both have managed Kubernetes services.

Cloud-native automation tools can help businesses run automated responses to certain events. Some examples include AWS CloudTrail, Google Cloud Scheduler, and Azure Automation.

Utilize Infrastructure as Code

You can also choose to employ infrastructure as code. IaC can cut down on the time it takes to configure infrastructure and allow for automated provisioning and management of an organization’s cloud resources. IaC offers additional benefits, such as version control, consistency, and repeatability of infrastructure deployments.

Popular tools include AWS CloudFormation, Terraform, and Azure Resource Manager.

Start Small and Scale Up

The good thing about adding automation to your business is that you don’t have to make changes all at once. Start by automating well-defined, low-risk tasks. After you’ve earned some quick wins, gradually expand the scope of automation.

Take a Modular Approach

Break down complex automation processes into smaller, reusable models. By taking a modular approach, you can improve maintainability, simplify troubleshooting, and facilitate future scaling. Instead of having to make changes to an entire process, you can fix small parts of a modular automation and make improvements much more efficiently.

Don’t Forget to Test and Validate

An automation that’s not running properly doesn’t save you any time, and could even cost you extra time to correct automatic mistakes compared to previously manual tasks. Implement tests to confirm that individual components are working as intended, and that integrated automations are working well together.

Implement Monitoring and Logging Practices

Sometimes, introducing new variables can cause issues for automations that previously ran without incident. Proactive monitoring using cloud monitoring or AI-based tools helps you track the health and performance of your automated deployments, and comprehensive logging can give businesses a detailed view of how each automated task is executing. If any issues come up, this visibility and documentation will make troubleshooting easier.

Stay Updated on Emerging Trends and Technologies

As technology evolves, your automations should follow suit. Shortcuts might get shorter, more personalized, and more sophisticated in the years after implementation. It’s a good practice to revisit your automations periodically and identify opportunities for greater optimization.

Unleash the Potential of Cloud Automation

Don’t let manual processes and inefficiencies hold you back. By leveraging automation tools, cloud best practices, AI technologies, and DevOps knowledge, our team at TierPoint can help you streamline operations, enhance efficiency, and accelerate time-to-market for your cloud initiatives.

Contact us today to schedule a consultation and learn how our team can help you harness the power of automation to drive innovation and achieve your cloud goals. In the meantime, download our whitepaper to discover how AI and machine learning can be used to supercharge your cloud environment and operations.

Understanding Cloud-Based AI

Cloud-based AI, also known as AI as a Service (AIaaS) or AI Cloud, represents the intersection of artificial intelligence and cloud computing. With AI Cloud, businesses can leverage AI tools and capabilities in the cloud without the need for significant investments in development or maintaining additional hardware.

Cloud computing allows for on-demand access to computing resources without the need for investing in physical infrastructure. Cloud-based AI further expands these capabilities by providing access to machine learning, natural language processing, predictive analytics, and more within a convenient cloud environment.

How Does Cloud-Based AI Work?

By taking advantage of cloud-based AI, it will seamlessly integrate artificial intelligence tools and resources into cloud infrastructure. The process begins with a user request, which could involve generating content, identifying an image or face, applying a rule based on preset criteria, or other AI tasks. Subsequently, the necessary data for executing the AI task is transferred from the user’s device to the cloud.

The cloud infrastructure processes the data, establishing the connection between the user’s request and the appropriate resources to handle it. The AI resources then analyze the data using relevant technologies. Subsequently, the results are sent back to the user. The key distinction between cloud-based AI and other AI implementations lies in the hosting location—cloud-based AI operates within the cloud environment.

11 Advantages of Cloud-Based AI

The advantages of cloud-based Ai can empower businesses to form rapid-fire insights, personalize the user experience, collaborate more effectively, and innovate more quickly to gain a competitive edge. Stay agile, competitive and responsive with the following advantages of cloud-based AI:

Rapid Analysis and Deeper Insights

Connecting disparate data sets can be challenging, hindering the ability to glean meaningful insights. Cloud-based AI addresses this by facilitating seamless data integration and enabling rapid analysis.

With the power of AI, businesses can efficiently process vast amounts of data, identifying trends and patterns that might otherwise go unnoticed. This not only enables real-time decision-making but also empowers organizations to anticipate future needs based on historical data, providing deeper and more actionable insights.

Cost Effectiveness and Resource Optimization

Traditional on-premises hardware and software solutions often entail significant upfront costs, not to mention the ongoing expenses for maintenance, upgrades, and scalability. Additionally, AI tools typically demand substantial computational resources, further escalating costs.

Cloud-based AI offers a more cost-effective alternative by eliminating the need for large, one-time CapEx investments in hardware and software. With cloud computing, businesses can access AI resources on a pay-as-you-go basis, scaling resources up or down based on demand. This not only optimizes resource utilization but also allows organizations to allocate financial resources more efficiently, ensuring they only pay for the AI services and resources they actually use.

Automated Operations

The combination of AI and cloud computing offers a powerful synergy for automating operations and streamlining repetitive, manual tasks. AI algorithms can be trained to create rules and adapt to new inputs, enabling automated processes that minimize human intervention.

By leveraging AI-driven automation in the cloud, businesses can significantly reduce human errors and enhance productivity.

Scalability and Agility

Public cloud environments offer unparalleled scalability compared to traditional on-site data centers. Businesses that employ public cloud computing can size resources up or down based on needs almost instantaneously. This helps protect organizations against overpaying for resources they don’t need. When it comes to AI, where computational requirements can be substantial, scalability is a necessity.

Easy Access to AI/ML Tools

Legacy infrastructure can often be restrictive and challenging to integrate with modern AI and ML tools. In contrast, cloud computing offers a seamless connection to a wide range of AI and ML resources, enabling businesses to leverage advanced technologies without the constraints of outdated systems.

Platforms like Azure and AWS further simplify access to AI/ML tools by offering built-in services and tools that can be easily integrated into existing workflows. These cloud providers provide a comprehensive suite of AI and ML services, from data analytics and machine learning to natural language processing and computer vision, empowering organizations to innovate and drive digital transformation more effectively.

Monitoring and Security

Cloud providers offer robust security features designed to safeguard data both at rest and during processing.

Additionally, AI-powered cloud services enhance security by proactively identifying and mitigating potential risks. Through continuous monitoring and machine learning algorithms, these services can adapt and evolve to detect new and emerging threats, thereby strengthening the overall security posture of organizations. By combining the scalability and flexibility of cloud computing with the intelligence of AI, businesses can achieve a higher level of security while maintaining operational efficiency.

Data Management

Data access, organization, and storage can be more efficient with cloud-based infrastructure compared to on-premises data centers. Cloud computing can help businesses aggregate and integrate data from different sources, organize it through automated rules, and provide elastic storage options.

Accessibility and Collaboration

Migration to cloud-based AI solutions can usher in new opportunities for accessibility and collaboration for businesses, making it simple for team members to access business-essential applications from any device. Cloud-based AI tools, such as predictive analytics tools and generative AI, can help teams brainstorm, coordinate, and reach decisions more quickly than ever before.

Personalized Experiences

Artificial intelligence can take user preferences and behaviors and use these inputs to create a tailor-made experience. User satisfaction can increase because people are receiving content most likely to resonate with them. This data can also improve the effectiveness of marketing campaigns and the customer service process, leading to more new customers and increased loyalty.

Powerful Computing Capabilities

Modern tasks, especially AI-based tasks, require significant computing capabilities. High-performance computing can perform complex calculations at intense speeds and use GPUs (graphical processing units) instead of CPUs (central processing units). Enabling high-performance computing can be resource-intensive and expensive for businesses. Cloud platforms deployed in high-density colocation facilities allow businesses to take advantage of these capacities without the high upfront investment.

Numerous Use Cases

The applications for cloud-based AI are still in their infancy. Organizations can benefit from predictive maintenance, real-time alerts, intelligent forecasting, automated personalization, optimized supply chains, and more. Use cases for cloud-based AI are sure to grow in the years to come.

Applications of AI in Cloud Computing

Cloud providers, including AWS and Azure, have many different ready-made AI services businesses can use to augment their operations, insights, and customer experience.

With AWS AI services, organizations can perform tasks such as the following:

• Extract text and data from documents
• Perform quality control and augment human capacity during the review process
• Bring lifelike speech to text
• Create personalized applications and user experiences using machine learning
• Find anomalies in data sets and get to the root cause more quickly
• Review code automatically
• Create end-to-end prediction models
• Analyze health data

AWS ML services can enable businesses to:

• Create generative AI applications
• Incorporate AI into existing business applications
• Develop, train, and deploy ML models for a variety of use cases in a cloud environment

Azure AI services can help organizations:

• Bring generative AI applications to market quicker
• Create, train, and tweak AI models based on business data
• Improve safety by finding harmful AI-generated and user-generated content swiftly
• Use foundation models to build AI apps
• Translate documents and text from more than 100 languages in real time

Companies that rely on Internet of Things (IoT) devices, are looking to build chatbots, or want to provide AI as a Service can leverage these AI/ML tools and more in the cloud.

What to Consider Before Adopting Cloud-Based AI

New capabilities can be exciting, but that doesn’t mean they should be taken on without thoughtful consideration of benefits and challenges. Before adopting cloud-based AI, businesses should have a solid understanding of common data privacy concerns, AI ethics and governance considerations, and integration possibilities.

Data Security and Privacy

While cloud providers are responsible for some level of data security, businesses also need to understand what they need to do to safeguard business and user data.

Cyber insecurity was listed as one of the most severe short-term global risks by the World Economic Forum, and it’s also been listed as a risk driver for adverse outcomes of AI technologies. Being aware of the risks associated with cloud computing and AI can better equip businesses to address them.

Cloud providers such as Azure have shared responsibility models that divide tasks between the provider and the customer depending on the type of deployment being used. For example, an IaaS deployment places more responsibility on the customer, whereas a SaaS deployment places more on the provider like Microsoft.

To navigate the complex landscape of data security and privacy in cloud-based AI environments, businesses should develop comprehensive security strategies, implement robust data protection measures, and stay informed about evolving cybersecurity threats and regulations. By doing so, organizations can foster trust, maintain compliance, and mitigate risks associated with data security and privacy, ensuring the safe and responsible use of cloud-based AI technologies.

AI Ethics and Governance

Because AI algorithms are trained by humans, they’re inherently susceptible to biases, which can perpetuate and even amplify over time through iterative training. Achieving complete objectivity in AI algorithms is a challenging endeavor, as they can inadvertently reflect and even exacerbate societal biases present in the training data.

Developers can make AI models better when they have a greater awareness of inherent bias and implement ethical AI practices to combat them.

Integration and Interoperability

While cloud-based AI is a great end-goal, the path to get there may be complicated for some businesses. Legacy frameworks may have dependencies that are difficult to translate in a new cloud environment, and old applications may not integrate well with cloud infrastructures. Before performing cloud migration and before considering cloud-based AI projects, businesses should create a cloud migration strategy and follow or build an AI adoption framework.

Future Cloud-Based AI Trends to Consider

The way businesses operate and interact with customers will look different in the coming years, and much of that is likely to be attributable to AI. Here are some of the trends on the horizon, and how they may continue to shape and grow the AI space.

Ongoing Evolution of Cloud-Based AI

Machine learning and AI are nothing new, but the democratization of AI is. Cloud-based AI has already become more accessible, thanks to services by providers like Azure and AWS. User-friendly interfaces and pre-built tools will make it easier than ever for businesses to leverage AI capabilities without the need for internal specializations.

The greatest short-term global risk, according to the World Economic Forum, is misinformation and disinformation. Much of this stems from what is spread by AI models. As these models become more complex, there will be a greater need for explainability – explanations of how AI arrives at certain decisions. This will be one of the key ways AI models can become more reliable and reduce the amount of disinformation and misinformation being proliferated.

Impact of Edge Computing on Cloud-Based AI Solutions

Edge deployments bring data processing closer to the source of data generation, whether it’s a user device, IoT sensor, or autonomous vehicle. This proximity reduces latency, enhances real-time processing capabilities, and improves the overall efficiency of AI applications deployed in edge environments.

Opportunities for Innovation and Growth

We’ve only just started to see the degree of personalization that AI can provide. Customer interactions in healthcare, retail, and education can become much more specified as AI models learn more from user data and apply it to customized experiences. Businesses will also see further development in automation capabilities, as well as real-time AI-driven decision-making.

Unleash the Potential of Cloud-Based AI

Wherever you are in your cloud or AI journey, it’s always valuable to work with a partner to get you to that next step. Are you at the start of a cloud migration process? Are you trying to figure out how AI can factor into your business processes? TierPoint’s IT advisory consulting can help you identify opportunities for AI/ML tools and services within a cloud computing framework.

Learn more about our AI consulting services and delve into additional business applications for AI and machine learning in our white paper.