// Atlas Technology

A Step-By-Step Guide to Atlas Data Migration

You dislike your CRM but data migration is also a hassle. You don’t have to play coy with us.

You aren’t alone either. A recent survey from Resco showed only 37% of CRM users were very satisfied. Let’s face it, when was the last time you spoke to a friend in recruitment about how amazing their CRM was?

But you haven’t migrated your CRM. Why?

Because the data migration is DAUNTING and you are running a recruitment business. The CRM may suck but it keeps the lights on. You’ve heard nightmares about botched CRM migrations and you don’t want to go through that nightmare yourself.

The downside. You don’t get to use AI. Not really. That clunky, non-AI CRM may be easy to stick with but so was shuffling chairs on the Titanic.

AI hasn’t just made CRMs much, much better. It’s made CRM migrations much more straightforward. So sit back, grab a knob of butter to slather over this dry-toast content and let’s put your mind at ease.

What is data migration?

Data migration is transferring information from a legacy system to a modern recruitment software solution. In recruitment, the core data revolves around people, companies and jobs but there can be a lot more to it. Custom attributes, financials, contractor books, and notes all need to come with.

Every recruitment software has a data model which is a glorified collection of spreadsheets which hold everything from education all the way through to resume files.

However, legacy systems have old, inflexible data models that prevent them from offering new features to their clients.

Therefore a data migration involves moving the data from one data model to another.

What are the 3 key steps of data migration?

The data migration process typically involves three key phases:

• Plan: The planning phase is crucial for the success of the entire data migration process. It involves assessing and cleaning the source data, analyzing business requirements and dependencies, developing and testing migration scenarios, and creating a formal data migration plan.

• Execute: During the execution phase, the migration solution is implemented, and the actual data transfer takes place. This phase can be stressful, especially for stakeholders directly impacted by the migration.

• Verify: The verification phase involves validating the migrated data and decommissioning the old systems. Data validation testing is conducted to ensure that all required data has been transferred accurately and that the values in the destination tables are correct.

How does data migration work with Atlas?

Step 1: Collect your data

The first step involves receiving your data from the legacy application that you’ve used until now. Sometimes this will be a database file (BAK for instance) but sometimes it can be a collection of CSVs.

As these are large data files, the legacy provider will usually provide you with FTP credentials which will comprise a username, password, FTP address and port number.

You don’t need to worry about understanding that, just give us the details and we can secure your data.

Step 2: Initial data assessment

The first step in any migration project is to conduct a thorough assessment of the existing system. This involves analyzing the current database to identify key data elements, such as candidate information, job postings, recruitment process details, and company records. In addition, communication logs and historical data are reviewed to determine their relevance to the new system.

Another critical aspect of the assessment is evaluating the completeness and consistency of the data. Atlas’s team works to ensure that all records are accurate, up-to-date, and free from inconsistencies. Special attention is given to custom fields, such as unique tags or attributes that are specific to the business. These are carefully mapped to their equivalents in Atlas, ensuring seamless functionality post-migration.

Collaboration with recruitment agencies during this phase is essential. By understanding the scope of the data and the organization’s business priorities, Atlas ensures that only necessary and valuable information is migrated, avoiding unnecessary clutter in the new system.

Step 3: Mounting the database

For data conversion to proceed, we need to take all the data and mount it on a database in our infrastructure.

Theoretically, we could run the legacy provider application using this new database but we don’t have the code.

Atlas’s approach ensures that no critical data, such as candidate histories or recruitment metrics, is left behind. Once extracted, the data is transferred to a staging environment where it is prepared for transformation. This staging ensures security and allows for careful handling of the data before it enters the new system.

Step 4: Transforming your data

We use a process called Extract, Load and Transform (ELT). We connect this new database to a data lake via a provider called Fivetran, which allows us to transform the data in real time.

From here, we go through the data, creating one table at a time. One table for people, one table for communication information, one table for experiences etc.

Transformation is perhaps the most complex stage of the migration process, as it involves aligning the data from the old system to Atlas’s modern architecture. Older systems often use outdated formats or non-standard structures, which can complicate the migration. Atlas employs advanced tools and methodologies to automate this process and ensure accuracy.

We use the popular Data Build Tool as well as OpenAI’s O1 model to map and move data from your old provider to your new provider.

Field mapping is also conducted, where fields like “First Name” are restructured to align with Atlas’s schema, such as “first_name.” Additionally, fields like “City” and “State” might be consolidated into a single “Location” field to streamline the data structure.

Custom tags and unique business-specific fields are also carefully addressed. For instance, tags like “favourite food” or “candidate status” are identified and restructured to fit seamlessly into Atlas’s system. Another critical aspect of transformation is data cleansing. This includes removing duplicate entries, correcting inaccuracies, and eliminating outdated or irrelevant records. By ensuring that only clean and relevant data is migrated, Atlas optimizes the functionality and usability of the new system.

Step 5: Loading your data into Atlas

The final step in the migration process is loading the transformed data into Atlas. Here, we export the newly transformed data into CSVs which are then injected into the Atlas application. This is the first time you can see your data inside the Atlas platform – a magical time indeed!

Atlas’s adherence to strict data protection standards guarantees that sensitive information remains secure throughout the process. By utilizing cloud environments, Atlas minimizes errors, enhances scalability, and ensures a smooth transition from the old system to the new one.

Step 6: Verifying your data

You look at the data, comparing it to what you need and expect. Adjustments are made to ensure the data is perfect

Step 7: File injection

Atlas is a unique platform. When resumes are imported into our data platform, we don’t need a map at all. Generative AI reads the resumes, figures out which people they belong to and then enrich with data such as functions and company linkages.

What can you expect from your data migration with Atlas?

We understand that recruiters can’t afford downtime. That’s why we follow a structured process to ensure a seamless transition.

Step 1: Planning and data review

We start with a kickoff meeting to understand your needs.

Together, we decide which data is essential and what can be cleaned up.

We establish a timeline so your team knows exactly what to expect.

Step 2: Extracting data from your current system

We work with your current provider (Bullhorn, Vincere, or others) to export your database.

If your system allows self-export, we guide you through the process.

Your data is extracted into a secure environment, ensuring nothing gets lost.

Step 3: Data mapping and transformation

Different ATS platforms store data in different ways.

Our system restructures your data to fit Atlas’s modern and flexible framework.

We make sure custom fields, tags, and notes are correctly mapped.

💡 Example:
Your current ATS might store all candidate phone numbers in one field. In Atlas, we separate them into “Work,” “Mobile,” and “Other” to improve data clarity.

Step 4: Test migration and verification

Before fully migrating, we run a test migration to check that everything is structured correctly.

You’ll have the chance to review the data and ensure all critical information is intact.

Any necessary adjustments are made before the final migration.

Step 5: Final migration and going live

We pick a migration weekend so that there’s no disruption to your daily operations.

On Friday, you wrap up work on your old system.

Over the weekend, we complete the full migration. By Monday morning, you log in to Atlas with all your data ready to go.

What about our operations during data migration?

Beyond the technical aspects, successful data migration also involves managing business continuity. Recruitment agencies cannot afford downtime during the migration. Atlas’s approach includes:

• Parallel operations: Running the old and new systems simultaneously for a short period.

• User training: Providing training sessions and documentation to ensure a smooth transition.

• Cutover planning: Scheduling migrations over weekends to minimize disruption.

How do we ensure successful data migration?

Successful data migration requires careful planning and execution. Agencies should begin by identifying the core data to migrate, such as candidate info, job postings, and company records. Clear goals and timelines should be established to guide the process effectively.

During the migration, it is crucial to clean the data, removing duplicates and outdated records to enhance accuracy and usability in the new system. Engaging stakeholders early ensures that all departments are aligned and any specific requirements are addressed. Testing the migration through trial runs helps to identify and resolve potential issues before going live.

Post-migration, monitoring the system is essential to ensure data integrity and smooth functionality. Agencies should verify that all data has been accurately transferred and that the new system operates as intended. With these best practices in place, recruitment agencies can achieve a seamless transition.

What are the challenges of data migration?

Data migration is not without its challenges, which can impact the success of the project. Understanding and addressing these challenges can improve the likelihood of a successful migration.

Data quality issues

Poor data quality, such as inaccurate, incomplete, or outdated records, can result in errors and inconsistencies in the target system. Common issues include:

• Duplicate records: Leading to inefficiencies and miscommunication.

• Missing fields: Causing gaps in candidate profiles or job postings.

• Inconsistent formats: Making it difficult to standardize data across multiple sources.

Addressing these issues requires thorough data cleansing and validation before migration.

Data consistency issues

Inconsistent data formats or structures can complicate the integration process. For example, if one system uses “MM/DD/YYYY” for dates while another uses “DD/MM/YYYY,” it can create alignment challenges. To overcome this:

• Standardize formats: Ensure all data adheres to a unified format, such as ISO 8601.

• Harmonize structures: Map data fields from the old system to the new system’s schema.

Data security issues

Sensitive information, including candidate and client data, must be protected during migration. Common risks include:

• Unauthorized access: Exposing data to external threats.

• Data breaches: Compromising confidential information.

Atlas employs strict data encryption protocols and ensures compliance with GDPR and other data protection regulations to safeguard sensitive information.

Data integration issues

Integrating data from multiple sources can lead to inconsistencies if not managed correctly. Challenges include:

• Mismatched data models: Older systems may use different naming conventions and structures.

• Custom attributes: Unique business-specific fields need to be identified and mapped correctly.

To address these challenges, Atlas provides tools for custom field mapping and data validation during transformation.

Other migration challenges

Scalability issues

Ensuring the system can handle large datasets without performance degradation is a critical aspect of data migration. Recruitment agencies often manage databases containing thousands of candidates, job postings, and historical records. Challenges related to scalability include:

• Performance bottlenecks: Large-scale data transfers can slow down the system, especially if infrastructure is not optimized.

• Resource allocation: Insufficient server resources or bandwidth can lead to delays in the migration process.

• Database optimization: Older systems may not be designed to handle the load of modern applications, necessitating adjustments during the migration.

Atlas addresses scalability by leveraging cloud-based infrastructure such as Amazon RDS and Redshift, ensuring efficient handling of large datasets. Automated scaling and load balancing further optimize the process.

Time constraints

Completing the migration within tight timelines is another significant challenge, particularly for businesses that rely on their recruitment software for daily operations. Common issues include:

• Downtime risks: Prolonged migrations can disrupt business activities.

• Coordination delays: Synchronizing teams, resources, and data can be time-consuming.

• Unexpected issues: Errors during transformation or loading can extend timelines.

Atlas mitigates time constraints by employing:

• Pre-Migration planning: Detailed roadmaps outlining every step of the migration.

• Automated processes: Tools like DBT Labs reduce manual intervention, speeding up data transformation.

• Weekend cutovers: Scheduling migrations over weekends to minimize operational disruptions.

By addressing these challenges proactively, Atlas ensures that recruitment agencies can meet tight deadlines without compromising data integrity or business continuity.

Data migration strategies

Data migration can be approached in several ways, each with its own set of advantages and challenges:

• Big Bang approach: This strategy involves migrating all data at once. While it can be less costly and less complex, it carries a high risk of failure, which can be expensive to rectify.

• Phased approach: In this approach, data is migrated in stages. It is less prone to unexpected failures but can be more expensive and time-consuming.

• Hybrid approach: Combining elements of both the big bang and phased approaches, the hybrid approach offers a balance between cost, complexity, and risk.

• Incremental approach: This strategy involves migrating data in small increments. It is suitable for organizations with limited resources or tight deadlines, as it allows for gradual progress and easier management of potential issues.

What types of data migration are there?

Data migration can be categorized into several types, each serving a unique purpose:

Business process migration: This type focuses on transferring databases and applications related to customers, products, and operations. It is often driven by mergers and acquisitions, business optimization, or reorganization efforts to address competitive challenges or enter new markets.

Storage migration: This involves moving data from one storage device to another. It is often undertaken to upgrade to faster, more efficient storage solutions or to consolidate storage resources.

Database migration: This type involves transferring data from one database to another. It can be particularly complex if the source and target databases have different data structures or formats.

Application migration: This process entails moving a software application from one computing environment to another. Challenges arise when the old and new infrastructures have distinct data models and work with different data formats.

Cloud migration: This involves moving data, applications, or other elements to a cloud computing environment. Organizations often choose cloud migration to reduce costs and enhance scalability.

Why choose Atlas for your data migration?

✅ Minimal downtime: Our process ensures your recruitment team keeps working with zero disruption.
✅ Data accuracy: We handle everything, ensuring no duplicate records, missing fields, or lost resumes.
✅ Custom mapping: Your unique custom fields and workflows are preserved.
✅ Fast and efficient: A full migration typically takes a weekend, so you start fresh on Monday.
✅ Dedicated support: Our expert team is with you at every step.

Ready to migrate your data to Atlas?

Data migration doesn’t have to be complex. With a clear plan, the right tools, and expert guidance from Atlas, your recruitment agency can transition to a modern system with minimal disruption.

Contact us today to learn how Atlas can transform your recruitment operations >>