Understanding Cloud Agnostic Architecture and Strategies
The constantly expanding public cloud space makes it imperative for businesses to adopt a cloud strategy that supports their key enterprise goals. Most organizations today prefer to work with multiple cloud providers, primarily to prevent vendor lock-ins.
A survey from Gartner shows that as many as 81% of companies use public cloud services from multiple providers. Businesses, therefore, are increasingly focussing on building multi-cloud strategies so that applications developed can consume services from multiple clouds.
However, for a multi-cloud strategy to become successful, cloud agnostic architectures are essential.
What is Cloud Agnostic Architecture?
Cloud’s most significant advantage is its flexibility. The public cloud services, when running out of storage, can automatically scale it for you. However, cloud agnostic architecture takes this flexibility to a different stage.
Cloud agnostic architecture focuses on designing applications, tools, or platforms that can run seamlessly in any cloud environment as they are compatible with any cloud infrastructure. They can also be moved to and from different cloud environments without operational issues.
Organizations can efficiently run their applications and workloads in any public cloud, even while using various cloud services. Generally, organizations have preferences for their cloud setup and have a customized mix of open source tools that work better for different applications or services. While it complicates and increases the risk quotient for application management, it is much better than getting locked in by a single vendor.
Companies can leverage cloud agnostic tools and platforms to ensure that their workflows and applications are secure, scalable, portable, flexible, and benefit from cutting-edge, open source technology that can meet core business needs.
Open Source Cloud Agnostic Strategies
Leading public cloud service providers such as AWS, Microsoft Azure, and Google Cloud Platform are powered by different open source components, such as Java frameworks. These components are further integrated with other open source cloud services, such as Apache and Kubernetes. However, these public clouds are still not fully open source. Most of these companies do not provide access to their source code, setting limitations to the cloud’s extension and customization.
Thus, open source cloud platforms emerged as an alternative for making large-scale improvements and customizations, allowing for building scalable infrastructures such as virtualized data centers and private clouds.
Open source cloud infrastructure can be described as a vendor-agnostic approach to leveraging local hardware, public cloud resources, and open source cloud technologies to set up, manage, and operate a hybrid or multi-cloud environment.
You may utilize several strategies to develop a cloud agnostic architecture for your organizations. Here are a few of the popular ones:
First Automate
With a cloud agnostic strategy, the workload for the DevOps team will grow significantly. Therefore, the automate-first method is the most crucial phase for a cloud-agnostic strategy since the infrastructure should be able to function with minimal manual intervention. A planned CI pipeline can be used to carry out this task.
Infrastructure should be constructed with little to no manual intervention, and a CI pipeline should be available for easy integration and testing. Pipelines for deployment must also facilitate scheduled deployments with approvals. Open source tools such as Spinnaker and Jenkins can assist you with this deployment.
Microservices
Microservices architecture organizes an application as a collection of services broken down into small components, each serving a specific business function and interacting with the others. When applications are split into more manageable microservice parts, they are easier to design and maintain, especially if they are meant to be cloud agnostic.
The microservice architecture enables the delivery of large, sophisticated applications in a timely, frequent, and dependable manner. Moreover, changes or additions to an organization’s technological stack are much easier to implement in a microservices environment. Swarm, Kubernetes, and Prometheus are a few from the pool of open source tools for this strategy.
Containerisation
Containers encapsulate all OS-level dependencies, configuration, runtime, libraries, and applications into a single execution. As a result, containers can be used easily and consistently on any platform without modifying the host environment. With few exceptions, a container can be deployed practically anywhere.
Containerisation, backed by cloud agnostic container orchestration technologies, is your best bet for avoiding vendor lock-in. Open source technologies such as Docker and Kubernetes are popularly used together for the best possible results.
Container Orchestration
Container orchestration is the automation of a large portion of the operational work necessary to run containerized workloads and services. It encompasses a wide range of tasks required by software teams to manage the lifetime of a container, such as provisioning, deployment, scaling (up and down), networking, load balancing, and more.
Infrastructure as Code (IaC)
The most straightforward approach is to run your containerized workloads on a cloud-independent container orchestration platform. Many platforms are available for cloud orchestration, such as Docker Swarm and Hashicorp Nomad, with Kubernetes being the most popular.
With the main purpose of being cloud agnostic, the primary challenge is to meet the vendor-specific requirements for your applications. Various third-party technologies come into play here to help you define the Infrastructure as Code (IaC). Hashicorp Terraform, Cloud Foundry BOSH, and Red Hat Ansible are some of the technologies that can assist you with this process.
Is Cloud Agnosticism Right for Your Business?
The multi-cloud environment is too good of a perk to refuse. Avoiding vendor lock-in, negotiating price leverage, and best-of-breed capabilities, the opportunity to mix, match, and combine the best possibilities is a reality. Although this approach is complex to implement–with the right strategy and concrete choices–a multi-cloud strategy could be a critical point of transformation for your company.
At GoDgtl, we work with organizations to rethink their business models and practices and help in implementing advanced technology systems and services to achieve digital transformation goals and support their business goals.
Get in touch with us to learn more about our capabilities.
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The Importance of Diverse Spend
The world is maturing into an age that values the right to equality. Governments and proactive organizations have introduced programs and policies to foster a culture of acceptance and help uplift communities and minority groups that have been historically marginalized. Workplaces today are diversely populated, and we are overcoming the damage done by our forefathers with sympathy and understanding. Supplier diversity programs extend the scope of this initiative and apply them to the supply chain.
If you are an organization still considering a policy to support diversity or are already taking steps and want to know how you can do more, here’s everything to get you started.
What Is Diverse Spend?
Diverse spend or diverse supplier spend is the portion of a company’s expenses spent with one of eight commonly recognized categories used to designate a diverse supplier. It’s possible for a business to fall into more than one category.
- Minority-Owned Business
- Woman-Owned Business
- LGBT-Owned Business
- Disabled-Owned Business
- Veteran-Owned Business
- Service-Disabled Veteran-Owned Business
- Historically Underutilized Business Zones (HUBZone)
- Small Business Enterprise
In order to support diversity, many enterprises have adopted supplier diversity programs that define yearly goals for contractual work with diverse companies.
A Brief History Of Diversity Programs
Diversity programs are policies companies adopt to improve outcomes for target group members, including women, racial minorities, or any historically marginalized group. Non-discrimination policies originated in the 1950s and 1960s with the civil rights movement that demanded justice and equality for Black Americans. Since then, many groups struggling for inclusion have joined.
The Small Business Act of 1953 led to the establishment of the Small Business Administration to “aid, counsel, assist, and protect, the interests of small business concerns.” Five years later, the Small Business Act of 1958 provided further aid through investment programs.
Over half a century later, supplier diversity programs are going strong worldwide as enterprises realize the benefits of engaging diverse suppliers.
Why We Need More Diversity Programs Today
After decades of working in diverse environments, we have data-backed studies on their impact. It is changing our world in more than just a cultural sense. There are three major areas of impact of having a diverse supplier:
- Social Responsibility Companies have a corporate social responsibility to support diversity programs and diverse spend in particular. MWEs face many challenges in finding business partners, which opens up a possible way for enterprises to give back to society. Here, enterprises are in a unique position, as only they can provide employment or award contracts to MWBEs.
- Benefits To The Economy Working with diverse spend can be more than a part of corporate social responsibility and can have many economic benefits. Small, diverse businesses aid in economic recovery and sustainability. Working with underrepresented businesses improves the economy through job creation, better wages, and tax revenue. MWBEs are proven to have a healthy impact on the economy. They offer corporate partners an 8.5% year-over-year cost savings, which is significantly more than they currently have.
- Global Impact When multinational companies adopt diversity programs or have an overseas supplier, they encourage the local governments to take an interest in diversity by showing the tremendous scope of business it presents.
Incentives To Help You Get Started
US companies have committed more than $50 billion to diverse spend. Companies can find value in working with diverse suppliers by promoting diversity across their ecosystems. Not only does it help support targeted groups, but it also impacts the company’s performance across different areas. Some of the direct benefits of having supplier diversity include:
- Perception Of Impact Consumers expect companies to take a stance on social and political issues. Backing this stance with Supplier Diversity Programs raises the perception of impact. For example, Coca-Cola spends more than $800 million on diverse suppliers annually. As a result of this and many other initiatives, individuals were found to be 45% more likely to view coca cola as a brand that values diversity.
- Government Incentives The opportunity to win government contracts is important in adopting supplier diversity programs. Many of these contracts now mandate a certain percentage of diverse spend. This also extends to many state and federal tax incentives for companies contracting diverse suppliers.
- Vendor Competition Having diverse suppliers broadens the sourcing pool and introduces competition that can improve quality and cut costs. Moreover, having sourcing options helps establish a more resilient supply chain. For example, If supply from one source is stuck due to any possible reason in today’s volatile times, you can shift to alternate sources.
The Right Thing To Do
Any company can benefit from allocating a portion of their business to diverse spend. However, it is more than just material benefits that you stand to gain from having a supplier diversity program. It is a proven fact that diversity leads to a more motivated workforce. Having an MWBE as a supplier will not only be a profitable move or place you in a favorable light. It is the right thing to do.
GoDgtl is a Certified Minority and Woman Owned Business Enterprise (MWBE) that provides digital solutions to clients who want to create modern digital experiences through transforming data, cloud, and digital strategy. Are you looking for a Minority or Women Owned business to partner with? You can join us online at the Grace Hopper Conference on September 20 – 23rd in Orlando, FL, a premier event for women technologists worldwide. We have a lifelong commitment to diversity, and it transcends throughout our company’s culture.
If you are putting diversity, equity, and inclusion at the forefront, we would like to see you there.
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The Four Stages of Data Modernization for Digital Transformation
Digital Transformation (DX) is crucial to every business, from small-scale enterprises to medium-sized businesses to multinational corporations. Our world is becoming increasingly digital, and how a company carries out its DX initiatives determines its competitiveness and relevance today.
Since the term ‘digital transformation’ means different things to different businesses, it is hard to define it. However, in general terms, digital transformation is the integration of digital technology into every key area of a business that impacts the fundamentals of its operations and its value delivery to customers.
Beyond that, DX is a cultural reform in which enterprises continually challenge the status quo and get comfortable with trials and errors. This also means that the business organizations should walk away from legacy business operations on which the company was built and embrace new business practices.
What Is Data Modernization?
In today’s digital Information Age, unimaginable volumes of data get generated every second from countless devices and sources in structured and unstructured formats. The mixture of structured data (such as documents and spreadsheets) and unstructured data (blog posts, videos, and social media comments) brings storage and data processing challenges.
Unfortunately, most organizations struggle to put this enormous data into effective use without realizing two essential things. First, their legacy data architectures stand in the way of generating deep insights for effective decision-making. Second, data modernization is the key to unlocking the limitless potential of data processing.
Data modernization helps enterprises move siloed data from legacy databases to modernized cloud-based databases or data lakes. Legacy systems have several inefficiencies, complexities, and bottlenecks. An enterprise that embraces data modernization eliminates those obstacles and turns into an agile one. So, data modernization is the foundation of digital transformation in an absolute sense.
The Four Stages of Data Modernization
Let’s explore the four stages of an efficient data modernization process toward successful digital transformation.
- Data Migration
Data migration is the first step of data modernization and most DX projects. Unfortunately, in most cases, the professionals complicate the process or tend to prematurely transform the data before data migration. Instead, they could have performed a lift and shift migration: a process that allows quicker data migrations and lets organizations leave legacy systems faster.
- Modernization of Data and Application
After the data has been migrated to the cloud, the data and application modernization phase commences. As it is carried out on the cloud, this phase enables a wide range of capabilities that are difficult to attain from on-premises (on-prem) systems. Some examples are real-time collaboration, easily accessible data sharing, and a more straightforward yet more informative Business Intelligence (BI) dashboard.
- Implementation of Modern Analytics
Data modernization empowers an enterprise to obtain more meaningful insights from data. And with modern analytics, an organization can learn more about their customers, identify customer behavior patterns, and make more informed decisions.
In addition, connecting multiple data sources to cloud-based modern analytics is simpler than connecting on-prem databases to similar data sources. Cloud-based data pipelines are easier to build and smoothly navigate through problems such as data gravity that on-prem databases fail to deal with.
- Apply Artificial Intelligence and Machine Learning for Innovation
Businesses have been using Artificial Intelligence (AI) and Machine Learning (ML) to overcome several business challenges in recent years. The last stage of data modernization is to unlock the innovation potential of AI and ML.
Some AI/ML use cases include manufacturing companies using AI/ML solutions to reduce waste by AI-based predictive maintenance. In addition, AI/ML solutions have been used by organizations to create customer profiles, learn more about customer behavior patterns and devise marketing strategies based on these profiles and forecasts.
Sailing Smoothly Through Data Modernization
According to a study conducted by Statista, 34% of respondents confirmed that their organization had fully implemented data modernization technology, while 50% stated that their organization is currently undergoing the implementation process.
Data modernization may still be a daunting, time-consuming process for some, even after breaking it down into four stages. It is a fact that if conducted in a standalone manner, the data modernization process wastes tons of your time and resources. The lack of continuity is one of the crucial challenges most enterprises face when implementing data modernization.
The best way to overcome this challenge is to shake hands with an Information Technology (IT) consulting firm like GoDgtl. With a focus on helping clients transform their off-line organizational activities and legacy business processes, we can help you modernize your data stack and enjoy modern and rich data experiences.
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In the current business world, data is the most valuable asset, and a disaster involving data loss results in several irreversible damages to an enterprise. Those damages include the loss of revenue, productivity, reputation, and even loyal customers. Disasters and their severity are hard to predict. Anyway, you can control how you respond to a disaster, and your response determines how successful your enterprise will recover from it.
The adoption of cloud computing services has been rising since the COVID-19 pandemic. A recent survey revealed that 67% of enterprises adopted cloud infrastructure by the end of 2021. Cloud computing services deliver on-demand Information Technology (IT) services from storage to applications to processing power via the internet on a pay-as-you-go basis. An enterprise can access all those IT assets by paying a fee rather than owning IT infrastructure or data centers.
Here, let’s take a look at Cloud Disaster Recovery (CDR) and how you can use it to your advantage.
Cloud Disaster Recovery: What is It?
CDR is a cloud-based service that quickly recovers your enterprise’s critical systems after a disaster and gives you remote access to your IT resources in a highly secure virtual ecosystem.
Conventional disaster recovery involves managing a secondary data center, a time-consuming yet expensive process. However, cloud disaster recovery has transformed the status quo by eliminating the need for traditional IT infrastructure and bringing down downtime significantly. According to the 2021 Data Protection Report by Veeam, the average downtime cost is roughly $85,000 per hour. And, the cost entirely depends on the size of the business organization – the larger the size, the larger the cost, and vice versa.
The Working of Cloud Disaster Recovery Explained
To understand the working of cloud disaster recovery, we should compare it with conventional disaster recovery. As mentioned earlier, the crucial element of conventional disaster recovery is a secondary data center where you can store all redundant copies of critical data and to which you can fail over workloads.
A conventional on-premises disaster recovery system generally includes the elements mentioned below.
- A dedicated ecosystem facilitates IT infrastructure, including maintenance, employees, and computing devices.
- Adequate server capacity for higher levels of operational performance that also allow scalability depending on the business needs.
- Internet connectivity with ample bandwidth allows remote access to the secondary data centers.
- IT network infrastructure, including firewalls, routers, and switches, are implemented to provide data availability and a reliable link between the primary and secondary data centers.
Conventional disaster recovery is often too complex to manage and monitor. More than that, maintenance and support of a physical on-premises DR site can be costly and time-consuming. For example, the expansion of the server capacity of an on-premises data center can only be done by purchasing additional computing devices and IT resources, which demands a lot of money, time, and effort.
The Advantages of Cloud Disaster Recovery
Cloud disaster recovery can effectively deal with most issues of conventional disaster recovery. Some of the advantages of CDR are mentioned below.
- Eliminates the need for a secondary on-premises physical site and the purchasing of additional hardware and software to carry out critical operations.
- Scalability of IT resources per the business needs
- The affordable pay-as-you-go pricing model requires you to pay only for the cloud computing services you use.
- CDR can be performed in minutes from anywhere in the world over any computing device connected to the internet.
- The backup of data across multiple geographical locations eliminates the possibility of a single point of failure. Even if one cloud-based data center fails, you can still retrieve a backup copy of your critical data.
- Cloud-based state-of-the-art IT network infrastructure ensures that the cloud services provider quickly identifies and rectifies any issues or errors. Moreover, the cloud service provider provides 24/7 support and maintenance of the cloud storage and consistent updating of hardware, software, and cybersecurity features.
Disaster recovery in the cloud is increasingly becoming a popular choice for small and medium-scale businesses looking to implement a robust business continuity strategy. With CDR, setting up a separate data center for backup is no longer required. Moreover, there is no need to install and maintain separate DR tools, which brings down costs while providing businesses access to continuous, scalable DR services.
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The digital revolution in healthcare is faster than ever, making public health all the more inclusive, efficient and sustainable. Healthcare organizations are embracing new, data-centric technologies to handle the constant strain on global healthcare systems due to several factors. Healthcare data amounts to almost 30% of the world’s total data volume, while the compound annual growth rate of healthcare data is projected to reach 36% by 2025. That’s 10% more than financial services, 6% higher than manufacturing and 11% faster than media & entertainment.
Data-driven healthcare and cloud solutions are helping healthcare professionals lower costs, provide better care and improve satisfaction. These technologies make it easier for hospitals to align business outcomes with patient outcomes by stimulating processes as well as operational efficiency. Life Sciences and pharmaceuticals have previously relied on experimental observation and costly clinical trials. Recent developments in data sciences have allowed researchers to streamline complex studies. With increasing bandwidth, networking capabilities, and processing power, healthcare information systems are improving with no end in sight. Read more to find out how data and cloud services have increased the scope of impact that quality healthcare has on our lives.
How Data and Cloud Computing Enhance Healthcare Services
Big Data Analytics
Big data is massive amounts of information generated from different sources, stored and analyzed to derive a simpler, more practical truth from the larger picture. Big data has been previously associated with supercomputers, nuclear physics and defense simulations.
Cloud-based solutions like Microsoft Azure can process datasets that are otherwise too large or complex for traditional database systems. As a result, this derivation serves as meaningful information for researchers, patients, doctors, and administrators. For example, healthcare organizations can leverage big data to predict and prepare for a future health crisis.
Big data allows healthcare professionals to drill down and know more about their patients and the effectiveness of the care they provide. Since doctors can identify and draw early warning signs, it becomes possible for them to treat diseases in the early stages. In addition, hospitals can use underutilized health records by merging data from clinics, other healthcare institutes and public health records.
Cloud Transformation
Organizations with bigger employee populations are turning to cloud healthcare solution providers to enable more affordable yet high-quality healthcare services. Cloud provides them with the flexibility, scalability and security needed to keep up with the exponential medical and digital technology advancements.
Leading healthcare organizations have already initiated value-generating cloud transformations that bring various benefits to practitioners and patients alike. Cloud-based solutions help healthcare professionals deal with the ever-increasing operational and infrastructural costs, government compliances, and security concerns. With remote storage, hospitals can access a network of servers where they can safely store large volumes of data.
Cloud offers the amount of interoperability required for advanced healthcare applications such as collaborative patient care, IoT-based devices and quality virtual treatments. For instance, Google Cloud Platform’s (GCP) healthcare data engine makes data immediately useful by providing an interoperable, longitudinal patient data record.
Pharmaceuticals and Research
Data analytics can ensure that the right patient has the right medicine at the right time. Pharmaceutical and research organizations can utilize data to understand diseases better, their progression and prevention and create safer, targeted treatments with minimal adverse events.
Predictive modeling enabled by large datasets can help determine more efficient medications and development processes. More accurate therapy trials with medicines for specific categories of the population or individuals are possible through advanced statistical analysis and better study designs. Big data applied in biomedical research can help us gain novel insights on a granular scale.
This can help with developments in personalized medication and enable a more individualized approach to healthcare. Leading industries in biomedical sciences have leveraged Amazon Web Services (AWS) cloud platform for almost a decade to do more with their data.
Transform to Digital to Let Data Transform You
Today, digital transformation is not merely adopting a bunch of feature-rich software and getting your data online. There are several digital technologies and cloud-based applications to choose from. However, businesses need to go by a proven methodology and incorporate only the right solutions to reach the highest level of efficiency.
GoDgtl has partnered with the industry’s leading cloud platforms to provide healthcare organizations with the full benefits of the modern data landscape. We can help you evaluate the most suitable cloud platform and set up optimal agile processes along with robust security measures across the enterprise.
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Every year, there is a rise in the number of businesses moving the cloud. At the same time, the number of cyberattacks is also on the rise. A 2021 survey shows that in 12 months, the average cost of compromised cloud accounts reached $6.2 million. As the number of devices and access to the new technology grows, most organizations find it challenging to monitor them, leaving them prone to cyberattacks.
Although cloud vendors invest heavily to secure their products, ensuring security and compliance is a shared responsibility between the cloud service provider and the customer. The Shared Responsibility Model is one such security framework that dictates the shared accountability between cloud service providers and their users.
Check out this infographic on Shared Responsibility Model to learn more about how you can keep your applications and workloads secure when migrating to the cloud.
What is the Shared Responsibility Model?
- The Shared Responsibility Model defines the responsibilities of both the cloud service provider, or CSP, and the client company in securing data, applications, and infrastructure hosted on the cloud.
- A report by Gartner predicts that 99% of cloud security failures will be on the client-side through 2025. At the same time, according to Jay Heiser of Gartner says, “ Exaggerated fears can result in lost opportunity and inappropriate spending.”
- The rate can be tackled with policies on cloud ownership, responsibility, and risk acceptance. Security practitioners for the company must understand what they are responsible for in the SRM.
The Two Aspects of Cloud Security
- “Security of the Cloud”: the responsibility of the service provider to protect the infrastructure offered as part of the service, including physical facilities, utilities, cables, hardware, etc.
- “Security in the Cloud”: the security at the client end that includes network controls, access management, application configurations, and data.
Shared Responsibility Across the Three Service Models of Cloud
- Infrastructure as a service (IaaS): CSP is responsible for the physical data center, networking, and servers/hosting.
- Platform as a service (Paas): CSP is further responsible for maintaining operating systems.
- Software as a service (SaaS): The CSP controls everything and shares control of the application’s configuration settings with the client.
Key Considerations
- Inventorying service usage: Keep track of who is accessing what information or applications
- Compliance with regulatory frameworks: You must ensure that the services you are using comply with the applicable regulatory frameworks
- Understanding contractual/legal aspects: You must thoroughly understand contractual agreements such as CSP service level agreements.
Takeaways
- Cloud security is a shared responsibility.
- The various responsibilities are distributed according to the service models.
- The shared responsibility model implies pro-active documentation and practices to avoid incidents of cyber-attacks
- You can employ third-party security tools on top of the CSP’s native security tools to enhance the protection.
Conclusion
While migrating to the cloud brings significant cost savings, businesses must also ensure that their sensitive data on the cloud is secure. Despite the fact that cloud vendors take every step to keep their infrastructure secure, ensuring that the cloud environment remains secure is a shared responsibility with customers.
SOURCES
https://docs.microsoft.com/en-us/azure/security/fundamentals/shared-responsibility
https://www.paloaltonetworks.com/cyberpedia/cloud-security-is-a-shared-responsibility
https://aws.amazon.com/compliance/shared-responsibility-model/
https://www.cisecurity.org/insights/blog/shared-responsibility-cloud-security-what-you-need-to-know
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Cloud computing facilitates storing or accessing applications, programs, and data over the internet instead of directly accessing them on your computer hard drive. The cloud icon often used to picture the Internet in flowcharts and diagrams inspired the term cloud computing.
Most of us would have already used several cloud computing services in our personal or professional lives. For example, document sharing services like Google Docs, Microsoft 365, and Dropbox; social networking sites like Facebook and Twitter; telecommunications applications like Skype; online streaming services like Netflix; Machine Learning (ML), Big Data analysis and Internet of Things (IoT) are all popular cloud computing services.
The worldwide cloud computing market is valued at roughly $369 billion in 2021 and is speculated to increase in size at a Compound Annual Growth Rate (CAGR) of nearly 16% from 2022 to 2030.
There are several types of cloud services. Some of the most popular ones are Infrastructure as a Service (IaaS), Software as a Service (SaaS), and Platform as a Service (PaaS). These cloud computing services can be set up in public or private cloud computing ecosystems.
The Public Cloud
Public clouds – the most popular cloud model – deliver Information Technology (IT) services like storage and servers over the internet by a proprietary third-party cloud service provider. Every hardware, software, and other supporting infrastructure in a public cloud is owned and operated by the cloud services provider. The user can access these services by managing an account using a web browser.
The unique features of public cloud services include:
- Higher levels of scalability and elasticity
- A lower subscription fee based on tiered pricing
Public cloud computing services are offered as free, freemium, or premium models, in which you are charged based on the IT resources you use. The public cloud computing services may range from standard IT services like email, applications, and storage to the enterprise-grade Operating System (OS) platform or IT infrastructure used for software development and testing.
The third-party cloud computing services provider is responsible for developing, operating, and maintaining the pool of IT resources shared between multiple users from across the network.
Public Cloud Use Cases
The public cloud computing services are best suited for the following business ecosystems.
- An enterprise with predictable IT needs like communication or collaboration services for a specific number of users.
- An enterprise in need of applications and services necessary to perform specific IT and business operations
- A business that requires additional IT resources to meet varying demands and requirements
- To meet software development and testing requirements
Benefits of Public Cloud
- No capital expenditures (CapEx): An enterprise doesn’t need to invest in IT infrastructure deployment and maintenance.
- Technically agile: It provides higher flexibility and scalability to meet unforeseeable yet varying workload requirements.
- Increased focus on business: The cloud services provider is responsible for IT infrastructure management. So, the complexities and requirements of in-house IT expertise are minimized, bringing more focus on business operations.
- Affordability: Flexible pricing as per the usage of clients.
- Cost agility: It allows enterprises to follow efficient growth strategies by investing more in innovation projects.
Pitfalls of Public Cloud
- Lesser control over cost: In higher usage, the Total Cost of Ownership (TCO) can also increase accordingly.
- Lower cybersecurity levels: Public cloud is the least secure cloud computing service, and it is not suitable for information-sensitive and mission-critical IT operations.
- Lower technical control: Lower visibility and control of the public cloud IT infrastructure may not meet your compliance needs.
The Private Cloud
The private cloud is a dedicated yet ‘private’ cloud computing service. An enterprise that uses a private cloud never shares its cloud computing resources with any other organization. Instead, the IT resources are isolated and delivered via a secure private network. The private cloud can be customized as per a business organization’s specific business and security needs.
Private Cloud Use Cases
The private cloud is suitable for:
- Government agencies and highly regulated industries
- An enterprise that uses sensitive data
- Businesses that require solid control and security over their workloads and IT infrastructure
- Enterprises that need advanced data center technologies for efficient and cost-effective business operations.
Benefits of Private Cloud
- Exclusive IT ecosystems: Private cloud offers dedicated yet highly secure IT ecosystems with restricted access for outsiders.
- Customized cybersecurity: Enterprises can run protocols, configurations, and measures to customize security based on unique workload requirements.
- Scalability and flexibility: Higher scalability, flexibility, and efficiency to meet unforeseeable requirements of ever-changing IT and business environments without compromising performance and security.
Pitfalls of Private Cloud
- Higher Cost: The private cloud is expensive compared to public cloud services.
- Immobility: Due to higher security measures, users with mobile devices have limited access to private cloud services.
- Lower Scalability: The private cloud infrastructure may not offer higher scalability if the cloud data center is based on on-premise IT resources.
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You could be migrating storage information or databases from small to large sizes. In any case, you need to plan ahead in data migration, execute with the right strategy, and ensure data accuracy.
Let us walk through the process in this step-by-step breakdown.
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Planning
Planning is one of the most critical steps for wild migrating. According to Gartner, “through 2022, more than 50% of data migration initiatives will exceed their budget and timeline—and potentially harm the business.”
Proper planning helps set expected time and success metrics. It clearly lays down the steps with the timeline and expected budget.
- Identify the data and format
- It is possible to migrate in one go in a short timespan, called the Big Bang approach
- Most companies will benefit from migrating in sequences with both systems running, called the trickle approach
- Migration in phases helps avoid downtime
- Make sure enough time has been planned for the migration
- Involve Business units and gather their needs/inputs
- Assess the resources and staffing
- Pick the right tool or build from scratch
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Preparation and Understanding the Data
Preparation involves auditing the data and creating the backup. It is also the stage when you figure out data governance.
- Refining data helps cut time and cost
- Identify what data you need to migrate
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Migration Design and Building
Migration design involves freezing upon the approach and detailing the technical architecture of the solution
- Before ETL operations, data needs mapping
- Mapping helps match source fields to target ones
- Cover all documentation by the end of this stage
- Migration design is also where you lay down security plans
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Execute and Test
This is where you Extract, Transform, and Load the data. According to a survey, 47 percent of the North American IT executives admitted that poor data quality at their company was due to errors during data migration.
- You need to extract the data from the source system and make sure of its integrity
- Once extracted, you transform it to the correct format and load it to the new system
- Testing should begin in the build phase when you start manipulating data
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Auditing and Maintenance
Once the system is live, you should set up a system to audit the data for accuracy from time to time.
- Use data quality tools to maintain high-quality data
- Maintain with future migrations in mind
Data migration is essential when moving to a cloud-centric environment, which is the current trend. Use data migration to get better performance and competitive advantage.
SOURCES
SOURCEShttps://www.bloorresearch.com/technology/data-movement/#emergingtrends
https://www.oracle.com/technetwork/middleware/oedq/successful-data-migration-wp-1555708.pdf
https://www.talend.com/resources/understanding-data-migration-strategies-best-practices/
https://easternpeak.com/blog/7-key-steps-to-perform-a-successful-data-migration/
https://zipreporting.com/en/data-migration/data-migration-process.html
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In the current Information Age, we live through a world of data. Today, everything from an individual to a multinational corporation relies heavily on various types of data and its analytics for their everyday needs. Forbes put forward truly mind-boggling statistics on the amount of data we generate every single day. It estimates that we create, at our current pace, nearly 2.5 quintillion bytes of data each day. With the adoption of the Internet of Things (IoT), this pace is only accelerating.
Despite the ever-increasing volume of information being collected through data analysis today, the functioning of the human mind is the only thing that stays away from change. We think in stories and pictures. Only through the visual representation of data can we interpret it for the benefit of an individual or an organization. Here, the relevance of Data Visualization and Business Intelligence (BI) tools emerges.
What is Data Visualization?
Data visualization is the technique to provide us with a clear idea of the information by giving it visual representation through charts, maps, or graphs. It turns the data into a much more easily digestible format for the human mind to comprehend and quickly identify trends and patterns in larger data sets.
The Benefits of Data Visualization
Data visualization aids an enterprise in quickly recognizing patterns and improves its decision-making process with interactive visual representations of data. Here are some of the benefits of data visualization for a business organization.
Easy Identification of Correlations: Data visualization enables easier identification of correlations between independent variables. Thus, an enterprise can better understand different independent variables and make better business decisions.
Identifying Trends: Identifying trends is the most useful application of data visualization. The necessary data from the past and the present helps an enterprise make futuristic yet accurate business decisions.
Examining Frequency: Data visualization helps to explore the frequency of customers’ purchasing patterns and aids an enterprise in identifying the behavioral patterns of prospective customers towards its different marketing and customer acquisition strategies.
Studying the Market: Data visualization takes the data from different markets, displays it on charts and graphs, and provides an enterprise with insights into which target audiences to focus on and which ones to ignore.
Risk and Reward: Risk and reward metrics should be analyzed through tedious yet complicated spreadsheets and numbers without data visualization. Once the data is visualized, enterprises can quickly home in on areas that may require action.
Swift Reaction to the Market: An enterprise’s ability to obtain visually enriched information easily and quickly on a functional dashboard allows it to proactively respond to ever-changing market conditions and avoid making mistakes.
What are BI tools?
In the modern-day data-driven world, data comes in both structured and unstructured formats from various sources like documents, journals, books, electronic medical records (EMR), images, videos, email, and other business sources.
Business Intelligence (BI) tools are application software that collects and processes large volumes of unstructured data from internal and external sources. They amass data primarily through queries. In addition, these tools prepare data for analysis by helping an enterprise create data visualizations, dashboards, and reports. As per a recent report by Markets and Markets, the global BI market is expected to grow from $23.1 billion in 2020 to over $33 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 7.6%.
Enterprises of every shape and size implement BI tools to manage, analyze and visualize business data or information. More than that, they are helpful for businesses to boost revenue streams and stay competitive in the market. Apart from the few benefits of BI tools for companies mentioned previously, there are a few more ways BI tools can add value to any business.
They are:
- Fast and accurate reporting
- Better, more rapid, and accurate decision-making
- Valuable business insights
- Competitive analysis
- Better data quality
- Increased customer satisfaction
- Identification of market trends
- Improved operational efficiency
- Lower margins
Every Business Needs Data Visualization and BI Tools
BI tools help an enterprise to carry out data visualization. So, the implementation of both these technologies in businesses empowers business leaders and employees to speed up and improve their decision-making and operational efficiency.
Enterprises must be power-packed with informed decisions and enhanced performance to stay ahead in today’s rapidly evolving yet highly competitive business world. BI tools and data visualization have all it takes to make this happen. The belief that BI tools and data visualization techniques are meant only for large businesses does not stand anymore. They are for everyone, be it a large or small enterprise.
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In today’s Digital Age, tremendous amounts of data are generated every single second from endless sources in various formats. First, there is structured data such as documents and spreadsheets, and now on the other side, there is unstructured data like emails, blog posts, videos, and even Twitter posts thrown into the vast mix of data. A recent study by Statista reveals that the total volume of data created, captured, and consumed globally reached 64.2 zettabytes in 2020. The study also predicts that by 2025, data creation worldwide is estimated to cross 180 zettabytes.
Every business organization in every shape and size all over the globe is pursuing digital transformation today. The generation of larger volumes of data together with global enterprises’ collective effort to transform themselves into digital businesses brings up storage challenges and the trouble of processing those myriad data types in vast volumes. Both these challenges put most business organizations under tremendous pressure to use a larger volume of data efficiently.
It is often legacy data architectures that stand in the way of nearly limitless opportunities to generate insights for effective business decision-making. Today’s massive surge in the volume, variety, and velocity of data baffles legacy databases, and they are bending under the heavyweight of these data challenges. Sooner or later, they may break. The only digital system that desperately needs modernizing is conventional on-premises data architecture. And the answer is data modernization.
What Is Data Modernization?
Data modernization includes extending yet retaining the value of legacy data assets, reusing the data buried in the layers of legacy databases, transforming them to updated modern data architectures, and correlating legacy data assets with the latest high-velocity data assets. It involves developing a scalable, flexible data stack, including modernizing databases without the limitations of many stages, integrations, and complexities. In other words, data modernization is simply moving data from legacy databases to modern databases. It is highly crucial for an enterprise that primarily deals with unstructured data.
Three General Data Modernization Methods
Every business organization has its strategies and objectives. Therefore, data modernization doesn’t come with a one-size-fits-all size. Anyway, enterprises can implement three general data modernization methods depending on their business strategies and objectives.
Data Migration
This method involves moving data to a different data management vendor while the source and target schemas remain the same. Data migration includes migrating code, procedures, etc., which usually brings no significant changes to the application. In addition, an enterprise can utilize automation tools for complete data migration. For instance, moving the data from Sybase, a database management system (DBMS) vendor, to save licensing costs comes under data migration.
Data Conversion
The source and target schemas are different during this data modernization process. Data conversion involves transformations during migration. It is a typical data modernization method during re-engineering an application and modernizing a legacy application. Despite the availability of Extract Transform and Load (ETL) tools, the process is manual. For example, data movement from legacy databases like Indexed Sequential Access Method (ISAM) to Relational Database Management Systems (RDBMS) comes under data conversion.
Database Upgrade
This data modernization method involves upgrading to a newer version of the database management system, which requires no transformation. The deprecated code is replaced, and an enterprise can use automation tools to complete the upgrade. For instance, upgrading from SQL Server 2005 to SQL Server 2012 can be considered a data upgrade method.
The Benefits of Data Modernization
The Benefits of Data ModernizationHere are some of the enterprise benefits of data modernization:
- It brings scalability to an enterprise to meet growing data analytics needs
- Efficient integration of new data sources to utilize data at any scale focusing on rising data volumes from multiple data sources
- Reduces the time to derive business insights. That means it provides the ability to find value in data even from streaming real-time data quickly.
- Democratize the process of data access for every business function
- Data modernization offers substantial cost benefits over traditional data management technologies.
Data modernization enables Information Technology (IT) enterprises and business leaders to attain and interpret data to anticipate market trends and improve business outcomes. As a result, a business acquires a solid competitive advantage by deriving actionable insights from enterprise data and data modernization strategies. It also allows organizations to deliver ideal Application Programming Interface (API)-driven application integrations and prompt decision-making in a dynamic business ecosystem.
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