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Smoothen Next Data Migration with Robotic Process Automation
Are you planning for a digital shift? Thinking of data migration to solve your modern business challenges? Enterprises often face hurdles in the path of cloud migration and service upgrade operations due to lack of integrated processes, improper validation, high costs, and time. Even more, the increasing frequency of migrating legacy old applications makes the traditional approach suboptimal. As these challenges demand a highly structured and rules-based nature of activities, Robotic Process Engineering makes a clear sense to automate the execution of data migration projects.
This blog will highlight challenges in data migration, why RPA is crucial for migration activities, and what are the major benefits of using RPA.
Data Migration And RPA
Data Migration is the process of transitioning data from one location to another, one format type to another, or one system to another. The business driver is usually a legacy app migration or consolidation in which existing systems are replaced by new applications or modernized that will share the same databases.
These days, mid and large-scale enterprises are aiming migrations (specifically moving from on-premises infrastructure to cloud-based storage and applications) to transform and achieve greater operational efficiency. However, Gartner says 50% of migration workflows will hit hard on budget and timeline by 2022 due to misaligned strategy & execution.
During data migration, companies might face challenges that lead to escalated time and budgets with a considerable risk of irreparable data loss.
Some major data migration challenges include:
You need to process the source data, retrieve what you need out of it and map it to the format of the target system
You must use or implement custom interfaces to introduce data into the new system
Ultimately, you need to confirm whether the data migration job has been completed successfully
RPA for data migration is a user-friendly and cost-effective solution to handle such challenges pertaining to migration activities. Why? Because of its highly structured and rules-based extract, transform, load (ETL) methodology, data migration is a job that fit the profile of what robots do best.
Related: Why is Data Migration Important? Challenges and Strategies Explained
Traditional Data Migration Vs Robotic Process Automation
Traditional Data Migration
Avoid most validation rules. The majority of the validation rules are hardcoded in the user interface & application. ETL extracts & loads through backend databases.
All the validation rules are applied as data entered into the screens
Data with errors remains in the new system as source system data was added over time.
Dedicated ETL developer is required to identify rules to highlight dirty data.
Dirty data doesn’t hit the target system because it violates target system validation rules.
It highlights the data that needs attention.
ETL and data analysis tools come with high costs.
Robots are multi-functional & perform tasks faster than humans without any error.
Why RPA is the Perfect Fit for your Data Migration Projects
RPA shares the similar property of data migration, such as being rules-based and highly systematic. The profile is viable in the context of fundamental extract, transform and load (ETL) methodology.
Given below is the RPA model that highlights why each step of the ETL methodology fits the profile of what bots do better.
Extract Design – Well-strategized requirements implying how data will be fetched and verified.
Transform - Special set of rules that guide how to transform data as per your target system
Load - Clearly defined rules on how transformed data gets mapped into your target environment
Test & Recovery - Includes integration test plans, rollback, recovery procedures, and reporting for each stage.
This methodology highlights the significance of using RPA in the ETL by spelling out required outcomes, logic, and subsequent activities for each step of the migration process. Moreover, since data migration needs often arises with legacy systems that do not have API access, RPA’s unique user interface (UI)-level integration, which doesn’t interfere with the underlying systems and databases, makes it a low-risk option for migration.
For example, ASTM, a not-for-profit standard based organization, harnessed RPA software benefits to convert 100+ years old technical standards into digital assets to keep up with the current market demands of making consumers world better. More than 1800 old data repositories moved to standard Postgres, MSSQL, DocumentDB, MongoDB databases. Icreon team automated the migration process using RPA and extended the functionality of the business model to provide greater insights to departments of various industries without investing heavily in developing APIs.
Benefits of Using RPA for Data Migrations
RPA is one of the fastest yet customizable technology to implement. As an increasing number of businesses and applications are moving to the cloud, no doubt data migration using RPA offers a number of benefits in terms of making the process faster, reducing migration costs, eliminating errors, and so on.
Related: Legacy System Migration to the Cloud - Is Your Organization Ready?
According to UiPath, automation done using effective process modeling can reduce migration time and costs up to 50% and 40% respectively.
By leveraging RPA for data migration processes, you can obtain the following benefits:
High Flexibility – Low Price
Many businesses consolidate data from different sources, including web pages for online orders, invoice workflow, email exchanges, and CSV files, effectively requiring individuals to work on multiple systems simultaneously, copying and transferring data between different sources and formats. RPA is designed in such a manner that it can deal with a variety of systems, different interfaces, and data formats. Implementing robotic process engineering can automate these high-volume, low complexity tasks, which ensure optimal resource utilization & high productivity.
Garbage In – Quality Data Out
By automating intensive validation steps involved in the migration process, RPA reduces human interventions and lessens errors also. As RPA leverages existing GUIs in place of customized interfaces, any inconsistency in data quality or faulty datasets can be determined with plausibility checks of the underlying systems. In this manner, your migration data can be scanned and if any error exists, it gets rectified before moving it to the target systems. In addition to this, RPA may also be used to generate test data to improve the quality of data migration activities.
Traceability – Better Visibility
RPA allows a level of visibility into the overall migration process which can’t be possible in the human-led migration. Due to its flexibility, RPA software is capable of creating logfiles of any type in required settings. For instance, RPA creates logfiles (audit reports) in the required detail and file format such as txt, Word, Excel, PDF, etc. The bots can propagate the results in the accepted format as required by the target environment such as storage on a network drive or SharePoint, distribution by email, etc. With the audit report of every step or action, it becomes easier to find out and fix errors and retrain robots on new applicable use cases.
We live in the age of digital transformation where data operations are at the heart of achieving operational efficiency and growth. Whenever you want to standardize operations or upgrade your legacy applications, you come across the need of data migrations. RPA helps in performing data migration easily from such legacy systems without involving high budgets and uncertain timelines. Businesses are looking to take advantage of RPA to adapt to market challenges that constantly demand scalability, security, automation, and innovation.
Facing data migration challenges? Implement RPA in data migration with Icreon to take your transformation journey to the next level with better predictability, security, and accuracy.