Data migration has been a common operation in the recruitment industry lately, but it is fraught with myths, particularly when handling high-volume data.
Several staffing firms avoid it for fear of losing their valuable information or falling into the puddle of other potential risks.
However, we are here to dispel these challenges and assure you that data migration is not as difficult as it may seem. In fact, it is a pretty straightforward process if you have the basics up your sleeves.
Still hesitant? This guide will help you navigate data migration with confidence and ease! Read on.
What is data migration in recruitment?
Data migration is the process of transferring recruitment data from one system (called the source database management system) to another (often called the target) using a variety of tools and techniques.
There are three different kinds of migration staffing agencies must know:
- Database migration: This means moving your candidate records from one database to another. For example, you may be using an Excel spreadsheet to record your data until now. So, when you purchase a new database software, you will migrate from Excel to that database.
- Database version upgrade: It involves upgrading the outdated version of your database with the latest one.
- System migration: It occurs when you have bought an entire recruiting suite/system, like CRM, HR payroll, etc., and is planning to shift to it from your old system.
There are three key phases of data migration in recruitment, which are:
This phase involves analyzing the business and technical requirements of the migrated data, as well as identifying any dependencies that may exist.
It includes skimming through the current recruiting system, identifying critical data (such as candidate information and job postings), and determining the hardware and bandwidth requirements necessary for the migration.
Additionally, a migration implementation schedule and any necessary software licenses, migration scenarios, and associated tests would be developed.
Once the planning phase is complete, the actual migration of data can begin. For example, it might involve extracting candidate information and job postings from the old system and loading them into the new system.
Pre-validation procedures may be conducted during this phase to ensure that all functions work as expected.
After the data has been migrated, the new database is tested to verify the accuracy of the migrated data sources.
It involves comparing candidate information and job postings in the new system to those in the old system to identify any areas of data loss.
Any disparities are documented, and the verification process is repeated until the new system is fully validated and deployed.
Once the new system runs smoothly, the old system is shut down.
Common challenges faced by recruiters during data migration + tips to mitigate them
Challenge 1: Data complexity
Moving a large volume of candidate/client records from one recruiting software to another may lead to several issues, particularly related to the source data analysis and configuration.
Furthermore, some factors like duplicate records and inconsistent or incomplete data add to their complexity, making it challenging for recruiters to get to the roots of the problem.
So, what’s the solution? Read below:
Every database stores record in a certain way. If you are using an outdated storage format, you must transform the data before migrating it to a modern database.
How? Here are some tools and techniques that can be used to transform your data:
- ETL (Extract, Transform, Load) software: Most modern ETL tools come with built-in functionalities for automatically converting data pointers during the transformation process. These tools typically allow you to define custom transition rules, specifying how data should be converted from the source to the target format.
Here are some of the best ETL software you can look into:
- Integrate.io (Flexible paid plan available)
- Oracle data integrator (Paid)
- Singer (Free)
- Hadoop (Free)
- Talend open studio (Free)
- IBM Datastage (Paid)
For more, you can check this out!
- Custom scripts: If you have complex data transformation requirements, you may need to write custom scripts to handle the conversion of your records. For example, you might use a scripting language like Python or Perl to read data from the source database, transform it as needed, and write it to the target database.
Remember: Before you start converting your recruiting data, it’s crucial to understand the relationships between different data entities. Data profiling and analysis tools can help you identify dependencies and relationships between different data entities, which can help you design more effective data conversion rules.
Also, it’s always a good idea to perform trial conversions of a small sample of data before attempting to convert your entire database. It will allow you to test your conversion rules and identify any issues before you start migrating your production data.
And after you have converted your data pointers, it’s important to validate the data to ensure that it has been converted correctly. There are many data validation tools that can help you check for data completeness, accuracy, and consistency.
You may have been storing 30-40 digits claim numbers in a single field in your current recruitment database; however, when migrating to a new system, it may be helpful to consider breaking up these fields into smaller, easily-readable chunks.
By doing so, you will be able to easily access important records, such as the sequence number, member identifier, and date, without having to manually decode the entire claim number in your head. It will save you time and boost efficiency, particularly when reviewing and comparing multiple fields at once.
Also breaking up codified fields can also reduce the risk of errors or inaccuracies caused by human error, as it is easier to spot and correct mistakes in smaller, more manageable chunks of data.
If you have been using the same management system for many years, it is possible that the same piece of data is stored at different places, leading to redundancy, inconsistency, and potential errors.
Data normalization is the perfect answer to this issue as it involves identifying all sources where duplicate data is stored and consolidating them into a single, standardized location, ensuring the accuracy and consistency of the source data.
When migrating your data into a new system, it is essential to ensure that all data is accurately and consistently transferred to the new system. Normalizing the data beforehand can make this process much smoother and more efficient, reducing the risk of errors and ensuring that the data is structured in a way that is optimal for the new system.
Challenge 2: Data loss or corruption
Data, data, data! Successful hiring is nothing without data.
You can see from the above image how recruiters utilize their gathered data to improve their recruitment efforts. And they WON’T love losing this data at any cost!
Unfortunately, this is one of the most common challenges recruiters face during data migration.
Data loss or corruption, even if it’s one lost record, can do your business much harm financially, and all your marketing efforts may go in vain. But what can you do to avoid it?
There are two primary ways:
1) Data reconciliation
Reconcile your data while migrating and testing them. You must know how many records you must produce in the new system, removing all the duplicates and inconsistent pointers from your current recruiting database. If the output doesn’t match your expectation, you must do thorough research to determine the reason.
Here are some best practices for performing data reconciliation:
- Define the scope and purpose of reconciliation: Determine the specific data elements that need to be reconciled, the reasons for the reconciliation, and the expected outcome of the process.
- Identify the data sources: Identify the systems, databases, or data sources that need to be reconciled.
- Develop a reconciliation plan: Develop a plan that outlines the reconciliation process, including the steps to be taken, the tools to be used, and the timelines for completion.
- Cleanse and standardize data: Ensure that data is cleansed and standardized before performing the reconciliation process. It may involve removing duplicates, correcting spelling errors, and ensuring consistency in data formats.
- Perform the reconciliation process: Compare the data across the different systems, databases, or sources to identify discrepancies or inconsistencies. Resolve any issues that arise during the reconciliation process.
- Validate the results: Validate the results of the reconciliation process to ensure that the data is consistent and accurate. Run additional tests or use machine learning algorithms to identify outliers or anomalies.
Some AI tools that can help you with data reconciliation include:
- DataRobot: A machine learning platform that can automate reconciliation and identify patterns in large datasets.
- IBM InfoSphere Information Server: A data integration platform that can reconcile data across multiple systems and databases.
- Talend: An open-source data integration platform with extensive data reconciliation and validation features.
- Alteryx: A data preparation and analysis platform that can reconcile data across different sources and systems.
By following these best practices and utilizing AI tools, you can effectively reconcile your data and avoid potential data loss or corruption during migration.
2) Data validation
As you migrate your datasets from one system to another, you must ensure that it matches your expectations. For instance, check if there are the correct number of characters in each claim number field or are the applicant’s phone numbers ending up in the “phone number” field.
There are three kinds of data validation you must know:
- Sample data validation: It involves picking a random record and comparing it with source data. It’s not entirely practical since some unvalidated data may be corrupted.
- Subset data validation: Here, you pick a subset of data, like the one ranging from 500 to 5000, and compare it with source data. Just like sample data validation, it’s also not error-proof.
- Complete data validation: As the name suggests, it involves testing and comparing every record from the source data with the migrated data. It is the safest type of data validation process.
Remember: When validating your recruiting data, consider factors such as data coverage, the efficiency of the queries, stability of the process, and execution time. And always strive for complete data validation, although you may go for the other two types to double-check the records after it is entirely validated by you or any other member of your hiring team.
Challenge 3: Data mapping
Data mapping is a crucial component of data migration in recruitment. It involves defining how and exactly where the data will be transferred from one system to another, especially when migrating to a more sophisticated knowledge-based recruiting system.
Here are some common challenges recruiters may face when mapping data:
- Mapping complex data structures: If your source and target systems have different data structures, you may need to perform complex data transformations to ensure the data is mapped accurately. It may be challenging when dealing with large volumes of data or when the data structures are highly complex.
- Dealing with missing or incomplete data: If your source data is missing specific fields or contains incomplete information, it can be difficult to map that data to the correct fields in the target system, leading to quality issues and impacting the overall success of your migration.
- Ensuring data consistency: When mapping data, it’s vital to ensure consistency across all systems, particularly when you know the possibility of your source data containing duplicates or other potential risks that need to be resolved before mapping.
What’s the solution? The same as the first and second challenges. You must focus on accurate data transformation, normalization, and validation.
However, it is always recommended to work with reputable experts. They know and have expertise in data mapping and can do the task you take years to perform in a blink.
Challenge 4: Data semantics
Usually, you would expect the meaning of your migrated data to be the same as the original data. However, no matter how careful you are, there can still be some semantic issues, causing one or more of the following challenges:
- Language differences: If your source and target systems use different languages, ensuring that the meaning of data is preserved during migration can be challenging. It is particularly challenging when dealing with industry-specific jargon or technical terms.
- Mapping data to the correct fields: When migrating data, it’s vital to ensure that the data is mapped to the correct fields in the target system, especially when dealing with data with multiple meanings or when the data is used differently in different systems.
- Ensuring data accuracy: Finally, it’s essential to ensure data’s meaning is accurately preserved during migration. This can be challenging when dealing with large volumes of data or when the data is highly complex. You must thoroughly understand the data and perform rigorous testing to ensure data accuracy.
To guard your data against this issue, you and your team must do a feasibility study and use test cases before using the data. How can you form the test cases for the same?
- Understand the source and target systems: Before creating test cases, it’s crucial to have a deep understanding of the source and target systems, including their data structures, business rules, and other relevant details. It will help you to identify potential issues and create practical test cases.
- Use a risk-based approach: Prioritize your test cases based on the potential impact of data migration issues. For example, test cases for critical data that could cause significant business disruptions should be prioritized over test cases for less critical data.
- Consider edge cases: Make sure to include questions that cover edge cases or scenarios that are unlikely to occur but could have significant consequences if they do. It will help you to identify and mitigate potential issues before they become significant problems.
- Use data profiling tools: Data profiling tools can help you to identify data quality issues and inconsistencies that may need to be addressed during the migration process. These tools can also help you to identify data patterns that can be used to create effective test cases. (We have already covered them earlier!)
- Leverage automation: Automated testing tools can help you to streamline the testing process and reduce the risk of human error. You can use tools like Selenium or Appium to automate your test cases.
By following these tips, you can create comprehensive and practical test cases that help to ensure the accuracy and completeness of your migrated recruiting data.
Challenge 5: Time consumption
Data migration can be extremely time-consuming, particularly for recruitment enterprises with significant amounts of data to transfer.
Here are some factors that can contribute to the challenge of time consumption, along with tips to help mitigate them:
- Data volume: The amount of data that needs to be migrated can significantly impact the time required for the migration process. It can be particularly challenging for staffing firms that have accumulated large amounts of data over time. To address this challenge, consider prioritizing data to be migrated based on its relevance and significance to your recruitment process.
- Network speed: The speed of your network connection may impact the amount of time it takes to transfer data between systems. If you are migrating data over a slow network connection, it may be worthwhile to consider upgrading your network infrastructure to improve transfer speeds.
- Data quality: Poor data quality can impact the time required for data migration, as it can increase the need for data cleansing and transformation. Before beginning the migration process, take the time to assess your data’s quality and identify any issues that need to be addressed.
- Data mapping: Mapping data between different systems can be time-consuming, mainly using different data structures or formats. To expedite the data mapping process, consider using tools like Pimcore, Informatica, etc., automatically map data between systems.
- Testing: Before completing the data migration process, it is essential to thoroughly test the new system to ensure that data has been migrated accurately and that the system is functioning as expected. This testing can be time-consuming but is critical to ensure a successful migration.
By considering these factors and implementing appropriate strategies, staffing firms can help mitigate the time consumption challenge during data migration.
Disclaimer: None of the tools mentioned here are affiliated. You should look into your business needs before selecting any software.
Case Study: How has Recruit CRM helped L-Lindh in a smooth data migration?
L-Lindh, an executive search firm, before switching over to Recruit CRM’s ATS, faced several challenges, like:
- Dealing with an outdated recruiting software
- Disorganized network and talent pool
- Inconsistent tracking of candidate’s/ client’s data
To overcome these hurdles, the team was looking for software that matched their requirements. Though they came across multiple systems, the fear of data migration was one of their top concerns.
Recruit CRM’s 5-minute set-up process and 24/7 ready-to-support customer team caught their attention, and they could smoothly transition to new ATS without losing any valuable records.
Recruit CRM is very easy to use. It’s complete. It’s editable, robust, and comes with a very nice interface. We’re also very happy with the support towards the migration.
Lauren Lindh (Founder, L-Lindh)
Are you just like L-Lindh, looking for an ATS that works right for you?
Maybe not now, but at some point in the operation of your recruitment business, you’ll need to deploy advanced systems or relocate your data center. Then, it will be unavoidable to do data migration.
We hope this article will help you be fearless and approach the process like a pro!
Happy data migrating~
Frequently asked questions (FAQs)
Q1-When should I consider migrating my recruitment data?
You should consider migrating your recruitment data in the following cases-
- When switching to a new recruitment software
- When your current system is outdated or no longer meets your hiring needs
- When merging with another company and needing to consolidate data
- When you want to improve data quality and eliminate duplicate records
- When you need to comply with new data privacy or security regulations
Q2-What are the requirements for successful data migration?
Successful data migration requires-
- the data to migrate
- a well-defined transition plan
- the right tools and technologies
- a skilled team to execute the migration process
Q3-What are the benefits of data migration in recruitment?
Data migration has several benefits, including (but not limited to):
- Improved efficiency
- Better data accuracy
- Increased accessibility
- Streamlined workflows
- Better decision-making based on accurate and up-to-date information
Q4-How much does it cost to migrate recruiting data?
The cost of migrating recruiting data varies depending on various factors like-
- the size and complexity of the data
- the tools and technologies used
- the expertise of the team performing the migration
We can’t tell you the exact cost. Getting a quote from a reputable vendor or consultant for the estimate is best.