With the enterprise embracing digital business, infrastructure and operations leaders are being asked to build agile infrastructure and processes that support rapid and evolving enterprise needs.

With data volume and variety expanding rapidly, effectively migrating that data is essential to digital transformation. These days it’s also a big business, with the global market for data migration projected to reach $22.8 billion by 2026.

Although migrating to the cloud means reduced costs, scalability, and improved security, among other benefits, companies often make the same common mistakes that can be easily avoided when migrating to the cloud. Failure to keep these mistakes in mind could mean that your cloud migration project will be time-consuming and costly.

When it comes to data migration, the common mistakes will be discussed here, we will also provide you with tips on how to avoid them.

• What is Data Migration?
• Some Data Migration Challenges
• How to Succeed with a Data Migration Strategy
• What are the different types of Data Migration?
• Steps to Data Migration
• What are Data Migration Tools?
• Avoid these 3 Data Migration Mistakes
• Improving Data Migration
• What Are The Challenges of Data Migration?
• Why do Data Migration Projects Fail?
• What is The Best Approach For Data Migration?
• How do You Handle Data Loss During Data Migration?
• How do You Validate Data Migration?
• What Are The Three Data Migration Tools Available?
• What is The Process of Data Migration?
• How do You Create a Migration Plan?
• How do You Prevent Data Loss When Migrating to The Cloud?
• How do Databases Handle Data Loss?
• What is Business Continuity in Cloud Computing?
• Data Migration Compatibility Issues
• Common Issues With Data Manipulation
• Data Migration Risks
• Solution For Successful Implementation of Data Migration
• 3 Challenges Associated With Migration of Data From One EHR Product to Another
• Data Migration Failure
• Challenges to Migrating to a New Automated System
• Data Migration Testing Challenges

What is Data Migration?

Data migration is the process of moving data from one location to another, one format to another, or one application to another. Generally, this is the result of introducing a new system or location for the data.

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The business driver is usually an application migration or consolidation in which legacy systems are replaced or augmented by new applications that will share the same dataset. These days, data migrations are often started as firms move from on-premises infrastructure and applications to cloud-based storage and applications to optimize or transform their company.

Some Data Migration Challenges

Let us describe the data migration challenges in little more detail. Data migration can be a simple process, however there are challenges one may face in implementation.

Storage Migration

Storage migration can be handled in a manner transparent to the application so long as the application uses only general interfaces to access the data. In most systems this is not an issue. However, careful attention is necessary for old applications running on proprietary systems.

In many cases, the source code of the application is not available and the application vendor may not be in-market anymore. In such cases storage migration is rather tricky and should be properly tested before releasing the solution into production.

Database Migration

Database migration is rather straight forward, assuming the database is used just as storage. It “only” requires moving the data from one database to another. However, even this may be a difficult task. The main issues one may encounter include:

• Unmatched data types (number, date, sub-records)
• Different character sets (encoding)

Different data types can be handled easily by approximating the closest type from the target database to maintain data integrity. If a source database supports complex data formats (e.g. sub-record), but the target database does not, amending the applications using the database is necessary.

Similarly, if the source database supports different encoding in each column for a particular table but the target database does not, the applications using the database need to be thoroughly reviewed.

When a database is used not just as data storage, but also to represent business logic in the form of stored procedures and triggers, close attention must be paid when performing a feasibility study of the migration to target database. Again, if the target database does not support some of the features, changes may need to be implemented by applications or by middleware software.

ETL tools are very well suited for the task of migrating data from one database to another i. Using the ETL tools is highly advisable particularly when moving the data between the data stores which do not have any direct connection or interface implemented.

Application Migration

If we take a step back to previous two cases, you may notice that the process is rather straight forward. This however, is extremely uncommon in the case of application migration. The reason is that the applications, even when designed by the same vendor, store data in significantly different formats and structures which make simple data transfer impossible.

The full ETL process is a must as the Transformation step is not always straight forward. Of course, application migration can and usually does include storage and database migration as well. The advantage of an ETL tool in this instance is its ready-to-use connectivity to disparate data sources/targets.

Difficulty may occur when migrating data from mainframe systems or applications using proprietary data storage. Mainframe systems use record based formats to store data. Record based formats are easy to handle; however, there are often optimizations included in the mainframe data storage format which complicate data migration.

Typical optimizations include binary coded decimal number storage, non-standard storing of positive/negative number values, or storing the mutually exclusive sub-records within a record. Let us consider a library data warehouse as an example. There are two types of publications – books and articles. The publication can be either a book or an article but not both.

There are different kinds of information stored for books and articles. The information stored for a book and an article are mutually exclusive. Hence, when storing a book, the data used has a different sub-record format for a book and an article while occupying the same space. Still the data is stored using rather standard encoding.

On the Contrary, proprietary data storage makes the Extract step even more complicated. In both cases, the most efficient way to extract data from the source system is performing the extraction in the source system itself; then converting the data into a printable format that can be parsed later using standard tools.

Character Encoding

Most of the systems developed on the PC-based platforms use ASCII encoding or national extension based on ASCII. The latest one is UTF-8 which keeps ASCII mapping for alpha and numerical characters but enables storage of characters for most of the national alphabets including Chinese, Japanese and Russian.

Mainframe systems are mostly based on EBCDIC encoding which is incompatible with ASCII and conversion is required to display the data. ETL tools should support the conversions between character sets, including EBCDIC.

How to Succeed with a Data Migration Strategy

Despite the difficulty and risks, IT can ensure a successful project within budgets and deadlines. It takes expertise, strategic planning, management buy-in, and software tools.

A working data migration plan will include the following:

Budget for Expert Help

Many IT organizations prefer to be hands-on, and some migration budgets do not allow for expert advice. However, unless IT already has migration specialists on staff, they will save money and time by hiring consultants who are experts at data migrations.

Plan the Strategy

Understand the design requirements for migrated data including migration schedules and priorities, backup and replication settings, capacity planning, and prioritizing by data value. This is also the stage where IT decides on the type of migration implementation schedule, sometimes referred to as “big bang or trickle.” Let’s look at these terms.

Big Bang migration completes the full transfer within a limited time window. There is some downtime during data processing and movement, but the project is completed quickly.

Trickle migration carries out the project in phases, including running source and target systems in parallel. Trickle migration is are more complex than Big Bang and takes longer, but has less downtime and more testing opportunities.

Work with Your End Users

Treat the data migration project as a business process instead of simply a set of technical steps, and involve your end-users. They will have understandable anxiety over the success of the migration project.

Work with them: understand the data rules and definitions, what data is subject to compliance, and priority data that should migrate first. Also understand what they are hoping for from the move: Analytics? Better performance? An easier way to issue legal holds?

By taking the time to work with end-users, you will have a more successful data migration project in less time and at lower cost. 

Audit the Data and Fix any Issue

Know how many TBs of data you are migrating, and target storage capacity and growth expectations. Database migrations require auditing the source database for unused fields, obsolete records, and database logic; and making changes before migrating data to the new platform.

Migrating storage is easier because you do not have to update the older storage and map to the new. But migrating data between storage systems is not as easy as simply copying data from one secondary system to another. Use software tools to locate dark data, and delete or archive them properly before the migration.

Delete obsolete files, abandoned e-mail accounts, and outdated user accounts. Dedupe and compress the source data if you are moving data over the WAN, then migrate and test.

Backup the Source Data Before you Move It

If the worst happens and you lose data during the migration, be prepared to restore it to original systems before trying again. Best practice is to create backup images that you can immediately restore to the original system should the migration lose data.

Move and Validate the Data

Invest in automated data migration software that allows you to schedule staggered migrations of data subsets, validates data integrity in the target system, and issues reports for troubleshooting and verification. Protect databases during active migrations with a software tool that syncs the source and target databases in real-time.

Final Test and Shutdown

Once you have migrated all data, test the migration using a mirror of the production environment. When it all checks out, carefully go-live and conduct final tests. Once the new environment is running smoothly, shut down the legacy system.

Then make it easier on yourself for the next data migration, because there will be one. Instead of spending expensive resources to update the source data before the move, institute governance controls and analytics in the new environment.

Continuously monitor the migrated data for orphaned work sets, unusual access patterns, and security. The migrated data will perform better in its new platform, and the next data migration will go faster and smoother.

What are the different types of Data Migration?

There are four main data migration types:

1. Storage Migration

This involves moving physical blocks of data from one type of hardware (such as tapes or disks) to another.

2. Database Migration

This kind of migration is used for moving an entire database from one vendor to another, or to upgrade the software currently being used for the database.

3. Application Migration

When an application vendor needs to be changed, it will result in the need for a substantial transformation, since almost every application operates on a specific data model.

4. Business Process Migration

This relates directly to a company’s business practices, often orchestrated by business management tools that will need to be replaced or updated in the event of a merger or acquisition. For this, the movement of data can be required for anything from one store, database or application to another.

Steps to Data Migration

Whatever your reasons for desiring data migration, and whatever type of migration is required, you will need to implement the following procedure to ensure efficient transfer of data:

1. Identify and explore the source systems by grouping customer names, addresses and similar kinds of company data types based on the target model.
2. Assess the quality of the source data to ensure that the new system does not fail due to incorrect or duplicate data or inconsistencies in operation style.
3. Define the architecture and design requirements of the migration process while also specifying the testing process and the method for transitioning them to the new system. Determine whether there will be a parallel run, a zero-downtime migration and whether, once the migration is complete, the old system will be decommissioned.
4. Prepare subsets of data and then test each category of data one at a time. If the project is vast, several categories can be tested in parallel. The reason for this testing is to build a scalable, reliable method of data migration within a specified time frame.
5. Now is the time to execute the migration, after shutting the source system down. Time taken for this step varies depending on the amount of data that needs to be transferred.
6. Review the audit trails and logs to ensure data has been correctly migrated, and determine the appropriate time to retire the old system.
7. Now that the migration is complete, it is time to manage ongoing improvements and monitor the data quality of the new system.

Remember to follow the best practices for an efficient data migration process, which include enforcing data migration policies, testing and validating the data that is transferred to ensure that no loss in quality has occurred and documenting the entire process.

What are Data Migration Tools?

Data migration is a tedious task that would require a lot of human resources to complete the activity manually. Hence, it has been automated and is done programmatically with the help of the tools that are designed to serve the purpose.

Programmatic data migration comprises of phrases like extract data from the old system, load data to the new system, data verification to ensure if data is migrated accurately.

Let’s discuss the top 10 tools that are best suited for data migration.

1. IRI NextForm

IRI NextForm is available in multiple editions as a standalone data and database migration utility, or as an included capability within the larger IRI data management and ETL platform, Voracity.

You can use NextForm to convert: file formats (like LDIF or JSON to CSV or XML); legacy data stores (like ACUCOBOL Vision to MS SQL targets); data types (like packed decimal to numeric); endian states (big to little), and, database schema (relational to star or data vault, Oracle to MongoDB, etc.).

Key features:

• Reaches, profiles, and migrates data graphically in IRI Workbench, a familiar and free Eclipse IDE for job design, deployment, and management.
• Supports close to 200 legacy and modern data sources and targets, with the ability for more through custom I/O procedures or API calls.
• Uses standard drivers like ODBC, MQTT, and Kafka for data movement, and supports local, cloud and HDFS file systems.
• Data definition and manipulation metadata are in simple, self-documenting 4GL text files that are also represented in dialogs, outlines, and diagrams for easy understanding and modification.
• Builds job tasks or batch scripts for execution, scheduling, and monitoring from the GUI, command line, etc., plus secure team sharing in a Git Hub for version control.

2. Xplenty

Xplenty is a cloud-based data integration platform. It is a complete toolkit for building data pipelines. It provides solutions for marketing, sales, customer support, and developers. These solutions are available for retail, hospitality, and advertising industries. Xplenty is an elastic and scalable platform.

Key Features:

• Xplenty has features for easy migrations. It will help you migrate to the cloud.
• Xplenty provides the features to connect to legacy systems.
• It will help you to connect easily to on-premise, legacy systems, and migrate data from them.
• It supports Oracle, Teradata, DB2, SFTP, and SQL servers.

3. DBConvert Studio

DBConvert Studio by SLOTIX s.r.o. is the most suitable tool for database migration and synchronization. It supports ten the most popular on-premises databases, including SQL Server, MySQL, PostgreSQL, Oracle, and more.

For large data storage volumes, it would be reasonable to consider migrating databases to one of the following cloud platforms like Amazon RDS/ Aurora, MS Azure SQL, Google Cloud SQL, and Heroku Postgres.

Key Features:

• The following three scenarios of data migration are possible: Source to Target migration, One-way synchronization, Bidirectional synchronization.
• All database objects can be renamed during migration.
• Data types can be mapped as for all Target tables as for separate tables.
• Filters can be applied to extract the necessary data from the Source database.
• The source table can be reassigned to an existing Target table.
• Flexible built-in Scheduler can be used to launch tasks at a specific time without GUI running.

4. AWS Data Migration

AWS Data Migration tool that is owned by Amazon is best suited for cloud data migration. It helps to migrate databases to AWS in a secure and easy manner.

Key features:

• AWS data migration tool supports homogeneous as well as heterogeneous migrations such as Oracle to Oracle (homogeneous) or Oracle to Microsoft SQL(heterogeneous) etc.
• It minimizes the application downtime to a remarkable extent.
• It facilitates the source database to remain fully operational throughout the migration activity.
• It is a very flexible tool and can migrate data among the most widely used commercial & open-source databases.
• It can be used for continuous data migrations due to its high-availability.

5. Informix (IBM)

Informix by IBM is another excellent tool repository that can be used to move data from one IBM database to another IBM database. It primarily supports homogeneous data migrations.

Key features:

• It supports data migration between different operating systems like UNIX or Linux etc.
• It also carries out data migration from one server to another.
• It is capable of performing high-speed data checking transfers with the help of utilities like db export & db import utility, db load utility, on load and on unloading utility, Non-logging tables, high-performance loader etc.

For data imports from non-Informix sources, IBM has special tools/utilities to facilitate that too like – External tables, High-Performance Loader (HPL) and IBM Informix Enterprise Gateway products.

6. Azure DocumentDB

Azure Document DB Data Migration Tool is owned by Microsoft. It is an excellent tool to be used for data movement from various data sources into Azure Document DB.

Key features:

• It can successfully import data from any of the mentioned sources: CSV files, SQL, MongoDB, JSON files, Azure Table storage, Azure Document DB, Amazon Dynamo DB, HBase.
• It supports a wide range of Windows operating systems and .NET frameworks 4.5.1 or higher versions.

7. Rsync

Rsync is a data migration tool for transferring data across computer systems efficiently. It migrates data based on the time stamp and file size.

Key features:

• It best works with Unix-like systems and acts as a file synchronization and data transfer program.
• Rsync processes act as a sender and receiver to establish a data transfer connection between peers. It is capable of performing local and remote data transfers by forming peer connections.
• It uses SSH to connect to the remote system and invokes the remote host’s Rsync to determine which parts of data need to be transferred over the secure connection.

8. EMC Rainfinity

EMC Rainfinity File Management Appliance (FMA) is a product of Dell EMC Corporation. It is designed to help organizations to reduce storage management costs.

Key features:

• It implements automated file archiving algorithms that can perform data migration across heterogeneous servers and NAS environments.
• It comes with easy to use wizards to transparently move files across NAS and CAS.
• Rainfinity introduces files into the environment through simple and light-weight solutions offering an excellent solution to its customers.
• Its prime features include scalability, availability, and flexibility.

9. Configero Data Loader

Configero’s Data Loader for Salesforce is a web-based data loader application. It speeds up the activities of inserting, updating, and deleting Salesforce data. It has much-improved error handling as errors are displayed in the grid, thereby allowing direct editing of errors.

 Key features:

• External ID support and ability to save field mappings.
• Comes with integrated error handling and provides basic support for mass editing.
• Powerful multi-column filtering allows users to perform final edits prior to data loading.

10. Brocade’s DMM (Data Migration Manager)

DMM by Brocade is an innovative, powerful and performance-oriented solution for high – performance data migration activities. It works efficiently in heterogeneous storage environments. It stands among the top 15 products of the year.

Key features:

• DMM is a rapid and easily deployed data migration software for enterprise environments
• It gives a high-throughput speed of up to one terabyte (TB) per hour.
• It optimizes migrations across Storage Area Networks (SANs) with an easy setup facility.
• It has a unique feature to determine the migration time in advance. This feature allows the clients to plan time, budget, and resources associated with the migration activity.

Avoid these 3 Data Migration Mistakes

1. Not involving business users from the start

The IT team may be accountable for data integrity after a migration, but business units are the data’s ultimate end users. By not keeping them informed and listening to their feedback about merging, cleansing, or restructuring the data they work with every day, business users may use data incorrectly or flood IT with support requests after the migration occurs.

A thorough assessment of your current environment informs the important decisions you’ll need to make about the process, such as when and where to involve stakeholders. Having a deep understanding of your infrastructure, the data you need to migrate, the order in which to do it, and compatibility issues will give you a better idea of the project scope.

2. Completion of the migration at once

Migrating to the cloud is a lot more complex than it sounds. While the majority of cloud providers make the process seem easy, it actually requires significant work like additional training for staff, buy-in from executive leadership, and months of planning and preparation.

Instead of migrating your entire data ecosystem to the cloud at once, we suggest an incremental transition to the cloud. This helps avoid major problems if something goes wrong during the migration process and allows you to ensure the process is as seamless as possible. Of course, time constraints and external pressures can mean an incremental migration isn’t possible.

3. Not testing and validating

It’s not realistic to expect that you won’t encounter issues during your migration. Like in any other IT project, testing should be a critical-path activity throughout the process, not just at the end. Define who will test and evaluate data as well as sign off on the tests’ results.

You can conduct your testing in development environments to minimize downtime, a strategy Teradata’s Consulting team often takes when migrating our enterprise customers’ data. Once the migration is complete, keep testing. Schedule follow-up meetings with your stakeholders to discuss issues, lessons learned and action plans going forward.

While creating a data migration plan can be a daunting prospect, customers are seeing the many benefits of transferring data from their legacy systems to more modern infrastructures. By keeping in mind the strategies for avoiding the mistakes above, you’ll set your systems and teams up for success and enable your data migration to positively impact your business.

Zero Downtime Data Migration

The zero-downtime migration model depends on having enough storage to create and run two complete environments. A full copy of a company’s data is taken into the new environment and tested while employees stay in the old environment.

The bugs are worked out of the new system, ensuring that all the applications still work and everything is where it should be. After testing is complete, a fresh copy is brought in, and all the employees are switched over to the new environment.

The old data environment is sometimes left open for a period of months so that employees can get files from the old data system but not write new data to those servers. In all data migrations, a post-migration data audit is done to check for data loss.   

Improving Data Migration

One thing that can improve a data migration is to clean out and standardize data practices prior to migration. The organization of a company’s data is often a reflection of the different filing habits of its people. Two people with the same role may well use completely different practices.

For example, saving contracts by vendor in one case and by fiscal year and month in another. Unifying data practices can be a much bigger job than actually migrating the data, but clean, coherently organized data backed up by clear policies helps to future proof a company’s data for many migrations yet to come.

What Are The Challenges of Data Migration?

Understanding common data migration problems can help organizations to better prepare themselves for a technology transfer and take advantage of their new IT environment when they get there.

1. Confusion and Disorganization

This may not sound like a technical issue, but most data migration problems can be traced back to confusion surrounding the migration plan (if one is in place) and a failure to adequately prepare for the move. Modern technology transfers are massively complex undertakings.

When the people responsible for making those moves fail to inventory systems and data, underestimate the time and effort it will take to relocate them, and fail to identify what resources will be needed in the target environment, they’re laying the groundwork for disaster.

2. Data Loss

When so much data is being transferred from one location to another, there’s always a possibility for some of that data to be lost. Some amount of data loss may not be consequential, especially if it’s “junk” or other “non-essential” data that won’t be missed. Similarly, some lost data can easily be restored from backup files. But some types of data loss are much more serious.

Even setting aside the potential disaster of losing confidential or private information that needs to be protected, data loss could create a ripple effect that terminates portions of the migration process. If the data loss escapes the attention of IT personnel, no one may realize essential data is missing until an application crashes due to missing data.

3. Compatibility Issues

Shifting data and applications from one environment to another is theoretically a simple process, but in practice, things are much more complicated. Even though some assets can be “lifted and shifted” without too much difficulty, this can create some compatibility problems down the line due to poor optimization.

Changing operating systems can render some files inaccessible because they’re no longer in a readable format. Access controls may not make a smooth transition from the source environment to the target system, leaving people unable to access key applications when they need them. In an absolute worst-case scenario, the entire system may crash once it’s removed from its legacy environment.

4. Hardware Challenges

Software compatibility issues are complicated enough, but sometimes the destination environment simply isn’t capable of handling the amount of data and applications being migrated.

While overestimating capacity can lead to needless (and costly) waste, it can be dangerous thinking that all assets will transfer to their new environment on a “1 to 1” basis due to differences in operating environments and the way different hardware deployments utilize resources.

And, of course, there’s always the nightmare scenario of a server being damaged during transfer to a new location or pulling up to the loading dock only to discover that the new server cabinets won’t fit through the door.

Why do Data Migration Projects Fail?

Industry experts agree that Data Migration poses the largest risk in any implementation. While there is a common misconception that Data Migration is simply moving data from Point A to Point B, the reality is almost always much more complicated.

1. Poorly understood / undocumented legacy systems: Every company’s data landscape is unique. It may encompass everything from decades old mainframes to homegrown one-off databases, each with its own level of support. Documentation may be non-existent, institutional knowledge may be limited, and key staff may be nearing retirement.

2. Incorrect / incomplete requirements that don’t reflect reality: Data Migration requirements are often developed based on assumptions around the data, rather than actual fact. Mappings and translations based on assumptions may miss key values.

Duplication between or across legacy data sources may not be expected or accounted for. Data structure disparity between legacy data sources and the new target system may not be fully understood.

3. Poor Data Quality / Incomplete Data: Your new system is only as good as the data underpinning it. Missing, invalid or inconsistent legacy data can cause ripple effects when it comes to the new system.

While data may never be 100% clean, lack of attention to Data Quality can cripple even the most straightforward projects, leading to last-minute cleansing initiatives. How do you ensure that any data gaps are filled and that you are not migrating too little (or too much) data?

4. Lack of Attention to Detail: It is very easy to overlook seemingly innocuous changes between source and target systems. Fields with the same name may mean different things, both within and across systems.

A different field name may be used for the same purpose across systems or multiple values may have the same underlying meaning. Date and time formatting and field length differences can also easily be overlooked, with disastrous impact.

5. Constant Changes: Data Migration projects are all about the changes. Even the most thought through business requirements start to change once testing gets underway and the business sees how the new system performs. Changes can easily number in the hundreds and need to be applied and tested quickly to avoid putting the schedule at risk by delaying test cycles.

6. Lack of Planning / Testing: Data Migration is a complex process, but comprehensive testing often takes a backseat to other concerns. Testing needs to be done throughout the project, and not just by the developers. The functional team and the business need to be engaged with testing to ensure that all requirements are properly met and that all rules are properly applied.

Bypassing or delaying testing can allow corrupted data to sneak into the new system or result in Go Live delivered data which does not meet the needs of the business. Just because a record loaded doesn’t mean its correct.

7. Poor Communication: Data Migration is generally part of a larger project and must coordinate reams of changes to complex technical requirements while also engaging the business functionally. Without strong IT and cross-functional team communication, unintended results are bound to occur.

What is The Best Approach For Data Migration?

Here are 10 data migration best practices that will help you turn your data transfer into success.   

1. Back up Your Data

Sometimes things don’t go according to plan, so before you start to migrate your data from one system to another, make sure you have a data backup to avoid any potential data loss. If problems arise, for instance, your files get corrupted, go missing or are incomplete, you’ll be able to restore your data to its primary state. 

2. Verify Data Complexity and Quality

Another best practice for data migration is verifying data complexity to decide on the best approach to take. Check and assess different forms of organizational data, verify what data you’re going to migrate, where it sits, where and how it’s stored, and the format it’s going to take after the transfer. 

Check how clean your current data is, does it require any updating? It’s worth conducting a data quality assessment to detect the quality of legacy data and implement firewalls to separate good data from bad data and eliminate duplicates.  

3. Agree on Data Standards

As soon as you know how complex the data is you must put some standards in place. Why? To allow yourself to spot any problem areas and to ensure you can avoid unexpected issues occurring at the final stage of the project.

Furthermore, data is fluid, it changes continuously, therefore putting standards in place will help you with data consolidation and as a result, ensure more successful data use in the future. 

4. Specify Future and Current Business Rules

You have to guarantee regulatory compliance and this means defining current and future business rules for your data migration process. They must be in line with various validation and business rules to enable you to transfer data consistently, and this can only be done through establishing data migration policies.   

You might have to come up with a set of rules for your data prior to migration and then re-evaluate these rules and increase their complexity for your data after the migration process ends.  

5. Create a Data Migration Strategy

The next step to successful data migration is agreeing on the strategy. There are two approaches you can take: “big bang” migration or “trickle” migration. 

If you select the big bang migration, the entire data transfer is completed within a specific timeframe for example in 24 hours. Live systems are down while data goes through ETL processing and is moved into a new database. It’s quicker but riskier.

The trickle migration splits data migration into stages. Both systems – the new one and the old one run concurrently. This means there is no downtime. While this approach is more complex, it’s safer, as data is migrated continuously. 

6. Communicate Your Data Migration Process

The data migration process will most probably require the involvement of multiple teams.  Making sure you communicate the process to them is an important data migration best practice. They have to know what’s expected of them. This means you need to assign tasks and responsibilities.

List all the tasks and deliverables and assign roles to activities. Verify if you have the right resources to complete each task. 

You have to think about:

• who has the final say over the data migration process
• who has the power to decide whether it was successfully completed
• who is responsible for data validation after the migration 

If you fail to implement a clear division of tasks and responsibilities then it might lead to organizational chaos and delay your migration process or even lead to failure. 

7. Use the Right Tools

Can you imagine migrating scripts/data manually? Not the best idea. Using the right tools during data migration will make the process faster and more efficient. You can use the tools for data profiling, discovery, data quality verification and for testing. 

Consider choosing the right migration tools as one of the key components of your planning process. Your choice should be based on the organization’s use case and business requirements. 

8. Implement a Risk Management Strategy

Risk management is something you must think of while conducting your data migration process. There are many problems that could potentially occur. Listing them and coming up with ways to either resolve them or prevent them from occurring in the first place, will make the process more successful. Think of depreciated data values, security, user testing, application dependencies, etc. 

9. Approach It With an Agile Mindset

Using agile during data migration will help you maintain high data quality thanks to frequent testing, spot and eliminate errors as they occur and overall make the process more transparent. It will also make the costs and schedule more predictable, as it requires a clear division of tasks and responsibilities and sticking to deadlines. 

10. What You Should Remember About Testing

Waiting until your data transfer has been completed to test it could cost you dearly. Test your data migration during each phase: planning, design, implementation, and maintenance. Only then you’ll be able to achieve the desired outcome in a timely manner.

How do You Handle Data Loss During Data Migration?

The more complex the data migration project, the greater the margin for error. That’s why it helps to follow best practice to reduce the likelihood of data getting lost or corrupted during the migration, and to minimise the potential impact on the business.

Here are our tips for helping to reduce data loss in complex data migration projects:

1. Define the data that is required for the migration.

     a) Do not migrate data that is no longer required.

          i. Fields required in the source system may be superfluous to business needs and benefits.

          ii. There may be fields of inaccurate data that are mandated but not required, and therefore do not need to be migrated. These fields are often filled with “.” or other irrelevant characters that were input to satisfy source system constraints when users entered the data.

     b) Do not migrate data that is out of date, as this may affect marketing campaigns or statistical analysis within the new system.

     c) Do migrate data to give business advantage – but make sure you define beforehand which data is business critical.

2. Use a tool to profile the data so you have a full picture of the current quality of the data, and where the gaps may be.

3. Cleanse the data where required. You can do this manually before the data migration starts, or you can do down the automated route prior or during migration.

4. Define the quality rules the data has to abide by early on in the process to give a robust framework for the migration. You can define these rules from system analysis, business analysis, and via gap analysis between the source and target systems.

5. Get sign-off on the rules. The complete set of quality rules needs to be reviewed and approved by the domain and system experts for both the source and target systems before you begin the data migration.

6. Verify the data against the rules. It is best practice in complex data migration projects to make this an ongoing process, where possible. The rules should continue to be defined, so you can maintain a constant view of the data and ensure that its quality is fit for purpose. That way, you can raise any issues early on, mitigate risks, and lower the chance of losing data during the migration.

7. Define a clear flow for data during migration, error reporting and rerun procedures.

8. Use a tool for data migration to professionalize and automate the process, thus further reducing the risk of error and data loss.

How do You Validate Data Migration?

Data validation is a method for checking the accuracy and quality of your data, typically performed prior to importing and processing. It can also be considered a form of data cleansing. Data validation ensures that your data is complete (no blank or null values), unique (contains distinct values that are not duplicated), and the range of values is consistent with what you expect.

Step 1: Determine data sample

Determine the data to sample. If you have a large volume of data, you will probably want to validate a sample of your data rather than the entire set. You’ll need to decide what volume of data to sample, and what error rate is acceptable to ensure the success of your project.

Step 2: Validate the database

Before you move your data, you need to ensure that all the required data is present in your existing database. Determine the number of records and unique IDs, and compare the source and target data fields.

Step 3: Validate the data format

Determine the overall health of the data and the changes that will be required of the source data to match the schema in the target. Then search for incongruent or incomplete counts, duplicate data, incorrect formats, and null field values.

You can perform data validation in one of the following ways:

Scripting: Data validation is commonly performed using a scripting language such as Python to write scripts for the validation process. For example, you can create an XML file with source and target database names, table names, and columns to compare. The Python script can then take the XML as an input and process the results. However, this can be very time intensive, as you must write the scripts and verify the results by hand.

Enterprise tools: Enterprise tools are available to perform data validation. For example, FME data validation tools can validate and repair data. Enterprise tools have the benefit of being more stable and secure, but can require infrastructure and are costlier than open-source options.

Open source tools: Open source options are cost-effective, and if they are cloud-based, can also save you money on infrastructure costs. But they still require a level of knowledge and hand-coding to be able to use effectively. Some open-source tools are SourceForge and OpenRefine.

What Are The Three Data Migration Tools Available?

There are three primary types of data migration tools to consider when migrating your data:

• On-premise tools. Designed to migrate data within the network of a large or medium Enterprise installation.
• Open Source tools. Community-supported and developed data migration tools that can be free or very low cost.
• Cloud-based tools. Designed to move data to the cloud from various sources and streams, including on-premise and cloud-based data stores, applications, services, etc.

On-Premise Data Migration Tools

On-premise solutions are designed to migrate data between two or more servers or databases within a large or medium enterprise/network, without moving data to the cloud.

These solutions are optimal if you are performing tasks like changing data warehouses or moving the location of your primary data store, or if you are simply bringing together data from disparate sources on-premise. Some companies prefer on-premise solutions due to security restrictions.

Below is a list of popular on-premise data migration tools:

• Centerprise Data Integrator
• CloverDX
• IBM InfoSphere
• Informatica PowerCenter
• Microsoft SQL
• Oracle Data Service Integrator
• Talend Data Integration

Open Source Data Migration Tools

Open source is software you can use, modify, and share because its design is publicly accessible. Typically, open source solutions are free or lower in cost than commercial alternatives. Sometimes commercial products are built on open source products and/or offer an open source, limited version for download.

Open-source data migration tools can be a practical option for migrating your data, especially if your project is not large or complex. However, to work with open-source, you may need some coding skills.

The following list shows some popular open source data migration tools:

• Apache NiFi
• Myddleware
• Pentaho
• Talend Open Studio

Cloud-Based Data Migration Tools

Cloud-based data migration solutions are the latest generation and are designed to move data to the cloud, either from an on-premise store, an application or stream, or another cloud-based store. Cloud-based solutions are optimal if you are already storing your data in the cloud or if you intend to move your data to the cloud.

Many companies see cost efficiencies and increased security in moving data from on-premise to the cloud and need a data migration tool to help with this process. And, cloud-based data migration tools tend to be very flexible about the types of data they can handle.

The following list shows some popular cloud-based data migration tools:

• Alooma
• Fivetran
• Matillion
• Snaplogic
• Stitch Data

What is The Process of Data Migration?

Migrating data is a specialist activity that demands a detailed plan – especially if the project involves complex data. Data migration service uses a clear process to mitigate risk and maximize the opportunity for project success. This process has been applied by our consultants to migrations of even the most complex data. Here are the six stages that we consider.

Stage 1: Project scoping

If the parameters of the project are unclear, or if you haven’t conducted a data migration before, you will benefit from a scoping exercise. Draw up a plan before the project starts that sets out critical areas of the project’s structure. Elements to include are:

• Stakeholders and their required deliverables
• Business domain knowledge, system expertise and migration expertise
• Communication plans and reporting requirements
• Budget and deadlines.

If using an external provider to run the project, be clear about your own dataset or you could end up changing requirements and incurring additional fees. Be honest about the scale of the project and have a clear understanding of its scope before agreeing costs with a supplier. This also aids budgeting of an internally-run project.

Our consultants also carry out a review of the migration itself to ensure that all its aspects are functionally correct. This includes:

• Ensuring the routes of communication are defined
• Ensuring storage and versioning of project artefacts is available
• Ensuring hardware is available and accessible
• Ensuring that our consultants have access to any required sites or buildings.

Stage 2: Resource evaluation

A clear methodology is essential if you want a staged, well-managed and robust approach to data migration. Our proven methodology includes thorough assessments of the project and a core migration process. Consider incorporating standards into your project.

Standards are used to identify problem areas early on, making sure that you don’t reach the final stages with a hundred different issues to sort out. For instance, at ETL Solutions we have the Prince2 management standard, and use ISO standards where appropriate to underpin our data migration methodology.

You will also need to evaluate the migration tools available to you. Apart from evaluation of its features, the critical questions to consider about the data migration software used for the project include: how flexible is your preferred migration tool? Is there a fit with the skills of the people working on the project? If using an external company to manage the project, confirm whether the software is included in the cost, or whether there will be an additional fee.

Initial assessment of staff competency and training requirements can reduce reliance on external experts and boost the confidence of the project team. Will the people carrying out the project be there for the duration?  Are they skilled and knowledgeable in the toolsets and methodology they’ll be using? For individuals, this process provides clarity about their role within the data migration.

Stage 3: Migration design

This stage plans the extraction, verification and transformation of the data. These core steps are included in our bespoke data migration methodology to enable an uninterrupted flow of data during the migration.

The migration itself is dependent on key artefacts being available at this point in the migration project. The migration design should include:

• How the data is extracted, held and verified
• Mapping rules
• How data is loaded into the new system
• Recovery plans for each stage of the migration
• A schedule of the actions required to go live.

Stage 4: Testing design

The testing design stage defines an overall test plan for all stages of the migration. An initial overview should assess the tools, reporting, structures and constraints involved with testing. The overview usually includes how each stage will be tested at unit level, followed by how the entire migration will be tested from start to end to ensure that the data flows accurately.

Unit test specifications will define test groups, which contain individual tests for that particular area of the migration. Each test should be broken down into its component steps, including description and expected results.

Stage 5: Development

Our consultants use an agile methodology to develop a data migration project in stages. This has proved particularly successful in migrations where a number of stakeholders are involved. An agile approach, which is clearly visible across all teams, ensures that risks are mitigated as soon as they occur. It also provides test data relatively early in the process.

We also create a test framework. This framework allows tests to be run regularly down to unit level, highlighting any potential issues.

Stage 6: Execution

Dry runs are often carried out to test the go-live strategy, enabling the go-live plan to be adjusted if necessary. When you’re ready to go live, you might consider implementing the migration at the weekend to reduce disruption to the organisation.

Alternatively, you could run the old and new systems concurrently and transfer data piece-by-piece. Our preference, if business objectives allow, is a parallel migration. This can increase budgets and timescales, but enables your team to address any issues that occur, with minimal disruption.

This process helps ensure that the project is delivered successfully with minimum risk.

How do You Create a Migration Plan?

A detailed data migration plan is the essential first step in a successful data migration project to select, prepare, extract, transform and transfer data of the correct form and quality. Below we outline seven steps to a successful data migration.

1. Identify the data format, location, and sensitivity

Before you begin the data migration process, identify what data you’re migrating, what format it’s currently in, where it lives, and what format it should be in post-migration. By identifying this information, you’ll be armed with knowledge going into the project.

During this pre-planning process, you may spot potential risks that you’ll need to plan for prior to the move, or realize that certain security measures must be taken as you migrate specific data. This pre-planning step can save you from making a critical error during the actual migration process.

2. Planning for the size and scope of the project

Once you have an understanding of the data being moved, define the scope of the data migration plan. Plan out the resources you’ll need to use during the migration and put a realistic budget in place. 

Conduct advanced analysis of both the source and target system, and write out a flexible timeline for the project. Consider whether the data migration will interfere with normal business operations, or contribute to downtime. You may be able to plan the migration to take place after hours or on weekends to avoid interrupting business continuity.

3. Backup all data 

Prior to the migration, make sure that all of your data is backed up, especially the files that you’ll be migrating. If you encounter any problems during migration, such as corrupt, incomplete, or missing files, you’ll have the ability to correct the error by restoring the data in it’s original state.

4. Assess staff and migration tool

Data migration can be a big job, especially if you’re moving a large number of files, the migration is complex, or you’re migrating sensitive information. Refer back to the size and scope of the project and use this information to determine:

1. If your team has the knowledge and skills necessary to accomplish the project, or if you will need to consult an outside expert

2. If your team has the time and resources available to tackle the project in your designated time frame,

3. Who you could bring on to help you accomplish the project. If you’ve determined you will be using a data migration software, reassess its features and flexibility to ensure it will meet the requirements you need to accomplish the migration.

5. Execution of the data migration plan

With your plans to guide you, ensure the right system permissions are applied to allow for successful data migration and extract all data migrating to the target, from source system. Ensure this data is cleaned to protect target system, then transform it into the proper format for transfer.

Finally, load your cleaned and deduplicated data into your target system data migration rules and map you’ve already laid out. Closely monitor your data migration during the process, so that you can identify and resolve any problems that arise.

6. Testing of final system

Once the migration is complete, ensure there are no connectivity problems with source and target systems. The goal is to ensure all data migrated is correct, secure, and in the proper location. To verify this, conduct unit, system, volume, web-based application and batch application tests.

7. Follow-up and maintenance of data migration plan

Even with testing, it’s always possible that an error was made during migration. To account for this, conduct a full audit of the system and data quality to ensure everything is correct once the data migration process has been completed. If you notice errors, missing, incomplete, or corrupt data, restore these files from your backup.

How do You Prevent Data Loss When Migrating to The Cloud?

The specific methods you use depend on your specific IT infrastructure. Companies that are using cloud applications, such as G Suite and Office 365 for example, really do need a CASB to enable best DLP practices in the cloud.

Here, we’ll take a look at the top six data loss prevention methods you need to include in your DLP strategy for secure cloud computing.

1. Backup Your Data!

Cloud computing has made data backups very easy. If your company is a G Suite or Office 365 customer, you should already have the ability to set up automatic data backups to Google Drive or OneDrive.

There are also many third-party data backup solutions available on the market for those companies that either don’t already have a solution or are extra vigilant in their data loss prevention backups and would like to use an additional resource.

2. Set Up Data Loss Prevention Policies

Setting up data loss prevention policies usually starts with classifying the different types of data you have and determining what level of protection each needs. For example, you may separate your data into two or three categories ranging from “open source” to “critical.”

Next, you will want to create policies around how information in each classification can be accessed and shared. For example, “critical” data may be that which only upper management in HR and financing can access. On the other hand “open source” contains files and information that, say, marketing and sales are creating to share outside of the organization.

Once you’ve classified your data types and set up policies around who can access them, and how they can be shared, you’ll want to monitor and audit each policy’s effectiveness. The rule of thumb when it comes to policy-driven data loss prevention methods is to start with very strict restrictions on access (particularly for those skewing toward the “critical” side of the spectrum), then open access slowly to those employees who really need access to them.

Auditing your DLP policies on a regular basis will also help you identify if there are certain types of data that you’ve missed or if you have misconfigured any rules in the process.

3. Use Data Loss Prevention Software

Software enables data loss prevention methods by allowing you to automate policies, monitor use, and detect risks. The right type of data loss prevention software for you will depend on the technology your team uses to store, access, and share data. There are three main types of data loss prevention software: endpoint, network, and cloud DLP.

Just about every organization should be using Endpoint DLP. This is because, well, everyone has at least one endpoint per employee-most have many, many more. Endpoints include laptops, desktops, on-prem servers, smartphones, tablets, and basically anything that connects to your network.

Most companies also know that they need some sort of software to control network DLP. Your network has long been the single access point between the internet and your internal information. However, that has fundamentally changed for most businesses and organizations in the last five to ten years or so.

Now, employees bring their own devices to work and expect to be able to use them. SaaS applications have also become prolific in workplace productivity and communications. These changes are what have created the need for cloud DLP software.

When information is stored, accessed, and sent or shared in cloud applications, traditional network and/or endpoint DLP technology doesn’t cover all the bases. It was developed to protect access to the information. But it doesn’t secure the actual data once authorized access is gained (whether it’s from an internal, actual authorized user or not).

Cloud DLP software, often available in the form of a CASB solution, provides InfoSec teams with the ability to monitor and detect activity within cloud applications so that data, not just access to it, is secured.

4. Monitor for Improper Use of Data

Data loss stemming from employees are more common than external attacks (though they get far less attention). For the most part, these incidents are accidental. They can range from an employee spilling coffee on their laptop to having it stolen from their car. Most often, it’s from sharing information with someone that shouldn’t have access to it without realizing they’d made a mistake.

There are also instances of employees stealing information from a company. Because they have authorized access to data, it is notoriously difficult to detect these incidents until well after they’ve occurred.

It could be a case of an employee who has been let go or quite who takes customer and/or company intellectual property information to bring to their next job or to sell to a competitor. There are also cases where employees take employee and/or customer information to steal their identities or sell information on the dark web.

While the intent of internal data loss creates vastly different outcomes, both can be problematic for any company. Even accidental data loss can set an organization back in terms of cost spent creating the information (both financial and/or time), as well as the cost of trying to regain it. Accidental incidents can also create a vulnerability for malicious attacks if left unnoticed and un-remediated.

5. Monitor for Account Takeover Behavior

Monitoring for account takeovers is a next-level data loss prevention method that is difficult to accomplish without the right data loss prevention tools. But, it’s a critical capability in your data security strategy and relatively simple to accomplish with the right technology.

The majority of account takeover attempts (and successes) have the same basic “signatures”. The easiest way to identify one is by monitoring and controlling login locations. A simple example of this is: if all your employees are based in the U.S., you know that any logins coming from another are unauthorized. You can set up a DLP policy to reject any logins coming from other countries outside the United States.

Monitoring for account takeovers should also take into account the number of login attempts. If you’re able to see a sudden and suspicious spike in the number of login attempts over a few hours or a couple of days, you know that that account is being targeted. You can take proactive action in these cases by re-setting the account password and requiring a stronger one.

Finally, using a data loss prevention CASB allows you to detect other types of suspicious behavior, such as massive file downloads stemming from a particular user, abnormal sharing outside the domain behavior, and/or uploading files or sending emails containing malware or phishing links.

6. Regularly Audit Your Data Environment for Risks

One of the best data loss prevention methods available is to continually audit your data environment for new vulnerabilities and risks.

These could come from an employee using a new, unsanctioned SaaS application, new patch updates in existing apps, new types of sensitive data entering the environment, and more. InfoSec teams are trained to see vulnerabilities everywhere. A good data loss prevention tool will help you and your team monitor and audit for new risks 24/7.

As you can see, there is a wide variety of data loss prevention methods available for IT and InfoSec teams. Choosing the right DLP solution (or solutions) largely depends on your company’s IT infrastructure, compliance requirements, and budget.

For teams that are using popular cloud applications, such as Google G Suite, Microsoft Office 365, Slack, and more, using a reputable CASB with easy-to-use data loss prevention tools is no longer a luxury — it’s a must-have.

How do Databases Handle Data Loss?

If your company operates an SQL server, here’s some information as to what you could be dealing with should the worst happen and the solutions available. The important elements of an SQL server.

The overarching parts of an SQL server which could be affected by data loss are:

• Database file; the actual file containing managed data. Incredibly complex and highly organized
• Relational Database Concept; the way the data is organized – algorithms are used to carry this out in the most efficient way to ensure the speed of the database server is optimized
• Client/Server system; the main communicator with the operating system and usually manages many databases simultaneously
• Database Management System (DBMS); the heart of the system and manages all the above elements

Data recovery…
If the unthinkable happens, all may not be lost. Data recovery specialists can now recover a great number of files and possibly even take the server back to the point just before crash or corruption occurred. Hard drives can be repaired in clean rooms off site but remote repair is often the first method attempted because of the array of electronic tool kit aids the experts now have.

Recovery will begin with locating the problem database file. If this is inaccessible because the issue is the storage device itself, work will be undertaken to extract all the data from it. If it emerges the file has been deleted or truncated, repair will take place so the file system is then correctly pointing to the data stream. If there are no pointers, the task is to search all the drive to try to find the data.

The next step is to analyse the database file and to find what can be recovered. A report is produced which will then be used to copy the data onto another SQL server database. Backups will then be created.

Be prepared…
If you’ve suffered data loss on your SQL server, you’ll want to ensure there’s as little chance of it happening again as possible. The most important steps to take are

• Know – or if you don’t know – identify where the most recent backups are kept
• Ensure the backup schedule and frequency is in line with general business operation
• Take the backups offsite
• Make sure your monitoring software is watching the server and is sending alerts

What is Business Continuity in Cloud Computing?

Business continuity helps the entire business persist in a crisis. Disaster recovery is the first step in business continuity and ensures that IT and communications work. Disaster recovery may rely on cloud service models like IaaS and SaaS.

Whether your company is large or small, the first step in writing a disaster recovery plan is to evaluate your business’s IT ecosystem.

Here is a summary of how cloud computing supports business continuity:

• Provides regular backups and easy failover (equipment that assumes the work when primary systems fail)
• Reduces downtime
• Provides better network and information security management
• Scales to suit your business needs; for example, keep critical data on-premise and back up the rest to the cloud 
• Helps reduce impact in disruption of service (DoS) attacks
• Removes the need to stand up and maintain a costly physical mirror site of your infrastructure
• Eliminates the need to sync software on two sites
• Reduces recovery time to as little as a few minutes — potentially 
• Eliminates the need to travel to a remote site in potentially difficult or dangerous circumstances

Data Migration Compatibility Issues

You might think that it is very easy to shift data from one IT infrastructure to another. However, it is a very complicated process in practice. You can directly lift and shift some data easily. But, there can be many compatibility problems that you need to deal with. These problems mainly occur due to poor optimization.

If you are changing your OS, then some files will eventually become inaccessible. Similarly, it is difficult to implement some access controls in the target system. Thus, your employees can’t access your important applications. In rare cases, your whole system might crash. This can occur if you are moving your data from legacy systems to modern systems.

Make sure that your data migration plan contains an assessment of your current system requirements. You should check if they are compatible with the new environment. Many companies will directly move their data to the new environment.

Thus, they will face compatibility issues during the migration process. You need to document all the important system requirements. Also, you should closely monitor your systems during the migration process. You should first perform tests in your system for checking your application performance.

Common Issues With Data Manipulation

Data manipulation is the process in which scientific data is forged, presented in an unprofessional way or changed with disregard to the rules of the academic world. Data manipulation may result in distorted perception of a subject which may lead to false theories being build and tested. An experiment based on data that has been manipulated is risky and unpredictable.

In the modern world we encounter data manipulation every day. Arguably the most common kind of data manipulation is misuse of statistics – many click-bait article titles on the internet are based on misuse of statistic as are some political and economic arguments. It is these examples that we will concentrate on in this chapter.

Misuse of statistics does include data forgery – the process in which data is created without any connection to the object of the data but the most important kinds of misuse of statistics are those that involve real data that is presented in a manner that may be misleading and even dangerous.

Data Migration Risks

System migrations are costly, time-consuming, resource intensive, and often fraught with risk. Below are seven risks that should be on your radar during any and all migration projects:

1. Depreciated Data Values

Many information system migrations involve data and information that may have been stored or archived for decades. As businesses evolve, so do business data values. Information that was valid ten years ago may no longer be valid today.

For example, think about the internal forms you use today and their associated form numbers. It’s likely that many of the forms you used in the past are no longer used today.

If you don’t factor some type of data validation and cleansing process into your migration, what will the new system do when it encounters a form number that is no longer valid? And if you encounter a retired form number during the migration, what are you going to do with it?

2. Security

As existing information systems have been modified over the years, it’s common for security issues to be fixed in a patchwork-like manner as they’re identified. The risk of re-exposing these security risks because they were overlooked or not well documented is a very real possibility that could challenge the success and validity of any migration project.

3. Roll Out Strategy

It’s rare these days that complete system migrations occur overnight or over a weekend. When migrations are staggered over time, careful consideration must go into planning how the migration will proceed with the least amount of business disruption.

If the migration is not company-wide, how will business units interact with each other when one unit is on the old system and another unit is on the new system?

4. Roll Back Strategy

To paraphrase poet Robert Burns, “the best-laid plans of mice and men often go awry.” That is, no matter how carefully you plan your migration, every organization needs a rollback strategy just in case things do not go as planned.

A rollback strategy requires as much thought and planning as the migration itself, yet there are no guarantees that your rollback strategy will be successful. Returning systems to their current state once a migration has been attempted is problematic at best given the number of moving part often involved.

5. Application Dependencies

If the information system you are migrating from is antiquated, it’s possible that you’ve lost track of all the applications that integrate with that system or the information and data created and maintained by it. There’s nothing more disheartening than discovering that you’ve basically broken a mission-critical system far downstream as a result of your migration initiative.

6. User Training

System migrations often result in not only changing the way users access information but also may change the way they do their jobs. This requires training. That means redirecting workers away from their productive tasks in order to learn how to perform their job functions with the new system. Taking workers away from their primary functions for training can be disruptive to the business.

Also, depending on the magnitude of the migration and how fast you can train your employees, you may risk a latency in instances where the training delivered is forgotten by employees by the time the new systems are deployed to a particular business unit.

7. Resistance to Change

All this can lead to resistance to change. Users may feel pressure to meet performance expectations or may be set in their existing ways of doing things, leading to cutting corners and potentially undermining the benefits of the migration.

Solution For Successful Implementation of Data Migration

The following four key areas should be addressed in order to satisfy the requirements of a fully integrated migration process:

Planning

Data migration is not the sexy part of the project. Consequently, data migration planning is often given less priority and seen as an administrative ‘burden’ instead of a necessary and essential component of the project.  A well thought-out strategy must be in place before attempting the data migration, with input from all the business stakeholders.

Given a broad enough scope, the data migration strategy can address issues of scope, timeline, and  resources, as well as itemizing the administrative steps to be covered in the migration.

Conceptualizing the data

All corporate data is a business asset. It belongs to the organization and is one of the most important ‘tools’ of the business. If data is conceptualized in this manner, the idea of engaging and achieving a robust commitment from business management committed to the success of this project should not be difficult.

Participation from management will ensure a process that is empowered to make the necessary decisions that will drive the required actions.

Allocating proper time and resources

Proper allocation of resources and a timeline are critical aspects of the data migration project and contribute to the overall success or failure of the project. Refining and clearly defining the scope also makes it easier to determine the size of the budget and the eventual agreement from management.

Conducting an analysis of the source and target systems, in consultation with the business users (directly impacted by the data migration), will ensure a transfer process that is both fully functional and that has minimized the amount of data to be migrated. 

Addressing data integrity

Data integrity problems (as previously noted) begin as a failure to treat data as a strategic business resource. Data integrity requires the prioritizing of its maintenance and upkeep in a business strategy, with an appropriate budget allocation. This perspective should also be supported when dealing with data migration. A breakdown of data to-do’s is as follows:

• It is important to examine data and rationalize it prior to migration. This will determine the level of source information to be included in the migration. Historical data can be very costly to transfer and not always necessary. There are legal requirements which dictate the length of time some data must be retained (e.g. accounting data), but there is also obsolete data that can be eliminated.  
• Data validation, prior to migration, is a crucial part of data integrity. Transferring inaccurate data to the new system would compromise its efficiency and negate the value of the investment in money and time. Plans to correct and prevent ‘dirty’ data must be taken.
• Finally, the testing or verification stage of the migrated data is equally critical. A simple way to ensure the data migration was successful is to create a test sample on a test database – not the live system. A strategy of testing at various points of the installation can prevent storing up issues and dealing with them late in the cycle, making them more expensive and difficult to correct.

3 Challenges Associated With Migration of Data From One EHR Product to Another

1. Data Migration

It is a logistical nightmare for the staff to export paper-based documents to date to the digital records. There will be large chunks of documents about the medical history of hundreds of patients and data entry might become a tedious and time-consuming task for the staff. This is a major EHR implementation challenge for hospitals and effort is doubled if there is no proper format in the former system.

2. Data Privacy

Another major EHR implementation challenge is the data privacy concerns of the patient community as well as the provider. The stakeholders often voice concerns over the risk of data leakage due to a natural disaster or a cyber attack. The federal rule has imposed a national policy to protect the confidentiality of personal health data.

In case of a security breach, the organization may get into a legal hassle and have to spend millions of dollars to settle the dispute. Hence, it becomes a major responsibility of the provider to ensure data security of the EHR.

3. Lack of Proper Planning

More or less, EHR implementation brings in a cultural change in the organization than a mere technological upgrade. Hence, the change management aspects of EHR implementation become a real challenge.

It needs to be strategically planned in advance and commitment is expected from all stakeholders. The successful implementation and sustainability of the EHR system will be a far-fetched dream without a great amount of planning involved.

Data Migration Failure

Here are the “8 data migration failure” to steer clear of.

1. Poor Knowledge of Source Data

This knowledge gap includes not being aware of the problems that exist in your data, such as duplicates, missing information, misspellings and erroneous data.

It can be all too easy to get complacent and assume that your data can easily be configured into the parameters of a new system however the reality could mean critical failures when it comes to user acceptance. So to ensure success, you need a good understanding of the source data.

2. Underestimating Data Analysis

Due to constraints in computer systems, information can be hidden in obscure places because often there aren’t specific fields to hold all elements of the data or users may not be aware of the purpose of the available fields.

This will result in incomplete, inaccurate and outdated information being transferred during the migration, often discovered very late in the day, even after the project has been completed. The outcome can mean not having enough time or the right resources to be able to identify and correct this data.

Performing a thorough data analysis at the earliest possible occasion, usually when planning and designing your data migration can help you uncover these hidden errors.

3. Lack of Integrated Processes

Data migrations typically involve a disparate set of people using disparate technologies. The classic example is the use of spreadsheets to document data specifications, which is prone to human errors and cannot be easily translated when analysing data or performing data transformations.

Using disparate technologies can lead to failure in the transfer of data and its design between the analysis, development, testing and implementation phases. Things can get lost in translation, resulting in increased costs and wasted time.

Organisations must look to utilise a platform that successfully links the critical inputs and outputs from each of the stages to help reduce error and save time and money.

4. Inability to Validate a Specification

While you may well have an understanding of your source data, this will not necessarily result in a strong specification for migrating and modifying data into a target system. As this is early in the stage of the migration, critical misses can have repercussions later in the chain of activities.

Validating your data transformation specifications early on with actual data, rather than just documented aspirations can increase the confidence in executing the rest of the steps.

5. Failure to Validate the Implementation

Like before, where your knowledge of source data is evident, you can still hit a brick wall because of a lack of test cases. If you don’t explore various scenarios, you run the risk of developing problems when it is often too late.

Testing your migration using full volume data from the real world helps cover a wider range of possibilities and tests for the worst case scenario, which could be missed when using more convenient samples of data.

6. Late Evaluation of the Final Results.

This problem can occur in the testing stage, where users only see the actual data that will be loaded into the new system at the end of the design and development. At this point, one of the worst outcomes can arise – incompatibility of the data in the new system. While an organisation is capable of working without remedying the problem, this is not best practice.

Time, money and the embarrassment of a delayed project can be avoided by introducing early and agile testing phases and getting your users involved in evolving the test cases as they see actual prototypes of the data output.

7. Lack of Collaboration

It has already been mentioned that data migrations involve disparate people, using different technologies, and in some cases a mix of internal employees and external contractors. Some of these people may not even be in the same location. Working in silos can reduce efficiency, create more data silos and sometimes lead to misinterpretations.

Working together can be difficult and when things start to go wrong most try to avoid blame rather than resolving the issues. Collaborative tools enable all parties invested in a migration to see the same picture of data as it moves through the project stages, leaving little room for assumptions and misunderstandings.

8. Inappropriate use of Expertise

It makes sense to source experts, and usually this is applied to the management and technical aspects of a data migration. However, often the experts on data, usually hidden in the business do not make an appearance until late in the day.

All too often those with access to data are unable to decode it, while those that can are unable to obtain access to it, sometimes until the new system is ready.

Introducing data experts into your migration projects right from the beginning will ensure they make sense of the disparate data sources, but also guide the data transformation to suit the audience who will use it in the target system.

Challenges to Migrating to a New Automated System

In this fast-paced, digitally-enabled world, enterprises have no choice but to move from legacy systems to modern systems to be productive and efficient. However, enterprises cannot simply abandon a system and move to another. Moving from one system to another has its own set of challenges, including retention of old data, and compatibility of the new system with the existing ecosystem.

All data from the old system either needs to be carried forward to the new system or synchronized between old system and new system in a way that the teams have access to it from the new system itself. Data migration/synchronization, therefore, becomes the first step towards creation of a modern, agile ecosystem.

Enterprises choose either of the following ways of data migration:

• Exporting data from legacy system using excel sheets (if the new system provides that option)
• Transferring data using manual migration processes
• Transferring data using automated migration solutions

Most of the enterprises look for migration solutions that help them migrate data with intact models, structures, and history. Therefore, there are multiple factors that one must consider before settling for a migration process/solution.

Read Also: Save your Business by Outsourcing your Accounting Needs

By the time a company decides to migrate data from one system to another, they have a clear idea about the primary reasons for data migration. They also have a tentative budget in their mind.

However, the challenge lies in choosing the right migration solution. While manual migration might sound enticing at the onset as it costs less, the long-term impact of manual migration could be disastrous.

Data Migration Testing Challenges

Mentioned below are some of the challenges that are commonly faced in Data Migration Testing:

1. Data Quality

You can identify that your data does not hold the same quality after migration to the upgraded or new application. In such scenarios, the quality of the data needs to be enhanced for meeting the business standards.

2. Data Mismatch

You may end up finding that the data does not match after being transferred from the old application to the new one. This might be because of potential changes in the data storage format, and data type.

3. Data Loss

You may end up detecting that some data has been lost during the migration process. This can be with the non-mandatory or mandatory fields.

Last Words

Data migration can be a complex process, but it’s something your company will have to go through at some point. Backup your data to avoid losing it if things don’t go as planned and they frequently don’t. That’s why it’s worth having a risk management strategy in place — figure out all the potential problems and try to come up with solutions to quickly resolve them.