Digital transformation projects take precedence in data processing owing to the operational and competitive benefits provided by cloud computing.
Because of the increasing number of internal and external breaches, organizations need to align digital transformation and migration efforts with other strategic requirements (e.g., compliance with the General Data Protection Regulation).
Today, being able to navigate a post-covision world that forces organizations to become agile, lean and focused on the outcomes that allow businesses to not only survive, but thrive in the new reality.
However, cloud migration is not just a “lift and shift” strategy. Typically, when organizations migrate from on-premises to the cloud, they are converting two different technologies. It’s also worth looking at the data challenges.
Budget and cost
For 47% of companies, the top reason to migrate to the cloud is to optimize costs. However, cloud migrations can be expensive; costs increase as migration time increases.
Companies don’t often budget appropriately for migration technology. Organizations exceeded their public cloud budget by an average of 23%. This is due to a lack of planning, leading to long and protracted migrations and reckless product decisions. In addition, manual migrations take longer and cost much more than automated migrations.
In terms of budget and cost, automated tools that scan repositories in the environment help add structure and business context: where is located, who can access, etc. as legacy structures are transformed. New structures will provide new opportunities for data and business processes.
Automated tools will help reduce risks and costs, and reduce project time. Automated software handles data cataloging, locating, modeling and managing cloud data sets.
Tools for planning and executing cloud migrations are not hard to find. Large cloud providers offer them; they can simplify migration to a provider’s platform. But the technology-independent approach to such tools adds value to cloud migration projects.
Vendor-provided tools lead customers into their environment. Independent tools, on the other hand, help understand which cloud environment is right for organizations. Their goal is to identify the cloud platform and strategy that will deliver the most value once the budget and feature requirements are defined.
Institutional Memory
Institutional memory is another hurdle companies face when exploring cloud migrations. When people leave a company, they take away with them an understanding of how and why the order of business was implemented. So you may not know what data you have and how to use it. The problem arises when it comes time to migrate; you need to understand what is there, how it is used, what the value is, and what needs to be migrated. Otherwise, time and money will be spent migrating the data and you will end up finding that it has not been used for years and there was no need to migrate it.
In addition, if you are planning to use a multi-cloud approach, it is important to make sure that the clouds you are using are compatible. Only 24% of IT companies have a high degree of interoperability between cloud environments. This means that more than 3/4 of the company suffers from inefficient cloud setups and a lack of ability to analyze data from multiple cloud environments.
Data Management
Migrating data to the cloud is only half the story; the other half is managing it. Your data sets need to be available for use by the right people for the right purposes for maximum security, quality and value.
Migration provides an opportunity to not just migrate data as is, but to make strategic changes.
Unfortunately, 72% of companies say that deciding which workloads to move to the cloud is one of the barriers to cloud adoption. However, cloud migration is not the end point; it’s just the next step to making the business agile in the long run. Determining which data sets to migrate can help prepare for growth.
Automated cloud migration and data management
The complexities of cloud migration described above can seem daunting, especially for organizations that accumulate and manage massive amounts of data. When companies are faced with manual, cumbersome work regarding business processes, IT infrastructure, etc., they often turn to automation.
Automated software tools can help with the planning and heavy lifting of cloud migrations. They should be considered when choosing platforms, forecasting costs, and understanding the value of the data being considered for migration.
Automated software tools can help with the planning and heavy lifting of cloud migrations. They should be considered when choosing platforms, forecasting costs, and understanding the value of the data being considered for migration.
Automation is a critical factor for cloud migration and data management tools.
Key benefits:
- Cost reduction: automated tools scan repositories in your environment and add structure and context in transforming legacy structures.
- Risk Reduction: Automated tools reduce risk, cost, and time to achieve desired results.
- A platform-independent approach increases the value of cloud migration projects.
- Any cloud to any cloud: automatic collection of abstracted data entities will simplify the transfer of information to another cloud platform or technology, if or when, you decide to migrate again.
- Institutional memory retention: collecting and preserving institutional knowledge around data and providing transparency.
- Continuous data management: Automation helps IT solve data management challenges during a cloud migration and then throughout the data lifecycle and minimizes human intervention.
Every environment and data is unique. So the first step in the job is to evaluate the cloud migration strategy. Then, an automation roadmap is created and docking smart data packages are developed to help IT achieve a future-proof architecture state, including accelerating data ingestion and transformation.
