Many businesses are turning to Cloud analytics since it has enticing features and functionalities that benefit them. Many new analytics tools, ecosystems, and capabilities in the Cloud can be quickly leveraged to test, pilot, and roll out new offerings. As a result, many businesses seek Cloud service providers who can assist them in allocating resources and integrating business operations across on-premise private and public cloud systems. This allows them to improve performance, reduce expenses, and comply with requirements.
The most frequently stated benefit of using Cloud analytics solutions is increased adaptability. Analytics apps and infrastructure can be built, deployed, and scaled up/down considerably faster than on-premises computer resources and new tools.
Cost-effectiveness is, unsurprisingly, a primary benefit of cloud analytics solutions. A complicated algorithm analyzing enormous amounts of data may take thousands of CPUs and days of computing time for companies without in-house computing and storage facilities.
As a result of cloud migration, organizations may instantly access the computing and storage resources they require on-demand, paying only for their use. For most businesses, this is both unfeasible and redundant. According to a study, a Cloud migration strategy can easily treble a company’s return on investment (ROI).
The third most important driver of Cloud analytics is standardization, which is inextricably linked to the first two benefits of increased agility and cheaper IT expenses. It also aids in the simplification and streamlining of IT management and the reduction of development cycles.
Organizations can leverage Cloud services that enable GPU-based solid compute capabilities for complex analytics and a collaborative ecosystem of data analysts inside a shared data environment.
The Cloud service provides you with new analytical capabilities, tools, and ecosystems that you can use to test, create quickly, and launch new products. Businesses can, for example, use Cloud-based data integration and preparation systems, which include pre-built industry models.
Cloud Analytics Migration – Go With the Need
Depending on company goals and objectives, you can pick from various cloud migration strategies in Cloud analytics and computing. Some are quick, while others are currently being updated and improved to match today’s expectations. In most cases, the Cloud migration process is divided into five stages:
- Evaluate Opportunity – analyze the cost and benefits associated with the Cloud migration process
- Discover and Analyze — access Cloud migration portfolio and formulate a migration plan.
- Plan and Design — strategically plan and design a customized cloud infrastructure for your business
- Migrate, Integrate and Validate — migrate and integrate with the relevant tools and processes.
- Operate and optimize — look for delays and bugs during operation and optimize cloud analytics.
Cloud Analytics Migration Strategies
The Cloud migration process includes six steps.
- Lift and Shift
- Lift and Reshape
- Drop and Shop
- Retire/Decommission
- Re-write/Decouple applications
- Retain/Not Moving
It’s essential to keep in mind that most migration projects use a variety of multi-layered methodologies, each with its own set of migration tools. The time it takes to move and adapt will be determined by the selected cloud migration process.
● Lift and Shift
You can migrate to Cloud analytics and solutions by lifting your application. This strategy speeds up the migration process while requiring only small resource changes. This migration approach is simple, quick, predictable, repeatable, and cost-effective.
●Lift and Reshape
This method is the same as the previous one. You might have Oracle 10 on-premises and need to upgrade to Oracle 12 on the Cloud provider. In short, the most recent version of the software has to be installed.
●Drop and Shop
This method can be used to replace an old app with a new one. On-premises, for example, you may have outmoded report generation technologies. You may need to upgrade your reports to a modern visual analytics platform during the transition. As a result, re-architecting and refactoring may be required as part of this migration strategy.
●Retire/Decommission
Your application will be decommissioned on-premises using this method. For example, you may replace your proprietary reporting tools with a single visual analytics platform.
●Re-write/Decouple applications
Before migrating to the Cloud, this strategy involves altering application binaries. This could be true for both custom and open-source software.
●Retain/Not Moving
In many cases, it may not be possible to switch to a cloud analytics platform. It may be an out-of-date and unsupported solution. Alternatively, you may be bound by some constraints.
As a result, you should be able to continue using your Cloud connection. A cloud data warehouse could be used to load data from an on-premise database.
To have a holistic view, it’s also important to understand the common concerns and challenges in operationalizing analytics in the cloud.
What are the common concerns with cloud analytics migration?
Despite compelling imperatives, businesses are concerned about moving their analytics to the cloud. Some major concerns include:
● Information security
Data on the Cloud is perceived by organizations to be fundamentally less secure than data on-premises. This viewpoint may have developed due to extensive news coverage and articles about cloud platform cybersecurity incidents. In reality, well-built cloud analytics and solutions are just as safe as on-premises technologies. The reverse is more likely to be true because established cloud services adhere to the strictest security rules and invest significantly in security solutions, skilled staff, and resources.
● Not being compatible
In a cloud environment, one of the simplest ways to transfer your data and applications is by using the lift and shift method discussed above them. However, in real-world circumstances, moving your data and programs from one environment to another is quite challenging. Legacy apps that can’t run in the cloud have various runtime settings, software platforms, and security mechanisms than cloud services.
● Selecting the right architecture and infrastructure
Organizations are worried that choosing the wrong platform for their analytics apps would lead to performance issues, data fragmentation, integration issues, and vendor lock-in along the line. When companies choose to have a cloud vendor, first, they examine their current needs. As they grow, they’ll need more interoperability, which isn’t always possible with each and every cloud-based service, potentially disrupting your processes.
● Integrating existing applications with newer Cloud-based applications
Many programs with complex interdependencies are often run in organizations, with data stored in multiple silos and formats. If they don’t have trustworthy data for mapping application dependency and resource use, respondents would view merging old apps with Cloud-based apps as a significant concern.
● Risk Assessment
Assessing critical data and key services can help you understand why safe cloud adoption techniques are necessary. However, most risk evaluation programs lack a solid management strategy framework. Data breaches and data loss can be avoided to some extent during cloud migration. You will, however, require a centralized security policy. On the other hand, creating a centralized policy is not simple and involves a comprehensive understanding of all risk kinds, critical access controls, and verification.
● Data management and governance
Given the variety of data types and sources, organizations must deal with data access and control while also improving regulatory oversight of how data is maintained and stored. These considerations may lead organizations to think twice about moving to Cloud Analytics.
What are the barriers to operating with cloud analytics?
Cloud migration approach is challenging if a company does not know what needs to be transformed. In and of itself, taking that vital first step might be challenging.
The most common cloud analytics migration issues are listed below
● Difficulty in deploying models into business applications
This could be caused by poor data quality, a lack of alignment between the data team and the business divisions, or the usage of too complicated models for users to understand or use.
● Lack of accurate data and cloud analytics governance
Simply investing time and money in data preparation and model development across the company is insufficient. Without proper governance, such disjointed efforts may not be linked with corporate objectives or realities, making them ineffective from the start.
● Data privacy concerns
Personal data has become considerably more challenging to use for Cloud analytics due to regulations such as GDPR. Most Asia Pacific firms that deal with consumers subject to EU legislation must seek customer approval or anonymize data before utilizing it for analytics, which adds to the labor and cost.
● Lack of relevant skills or staff
Finding experienced employees, such as data scientists, who can discover, organize, and evaluate data using the appropriate technologies can be difficult for many firms. This issue is unlikely to be rectified any time soon.
● Migration of larger databases
Many businesses select cloud migration to address data storage issues as well as scalability, agility, and other requirements. However, they may run into an issue selecting whether to move everything at once or in small portions. Prioritization becomes a challenge with smaller chunks.
Migrating complex data within corporate systems is yet another difficulty. Processing data on a very large scale is difficult if the targeted database includes both unstructured and structured information.
● Unable to address data quality and data preparation
Disparate processes, fragmented systems, and isolated information repositories are the most common causes of data quality and preparation concerns. Furthermore, since data continue to expand exponentially, it can be difficult for businesses to stay ahead of the problem.
Why Techwave?
Techwave works with clients to develop a thorough approach for extracting useful information from data. The INsights platform (TWIN) from Techwave facilitates a smart cloud-based data analytics platform and intuitive visualizations for quick and successful decision-making. The platform uses Big Data technology to provide insights and visualizations to various industries.
This platform is central to Techwave’s strategy and is used by our business community to drive value. The platform is based on open data architecture, is tool-agnostic, and adaptable to proprietary customer tools. The platform offers corporate use cases and solutions that use big data technology and analytics with demonstrable results.
Succinctly put, all the above-mentioned concerns and challenges are effectively addressed and resolved by Techwave’s cloud analytics solution – INsights platform (TWIN)