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.

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.

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 

Here 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.