Article
23 May
2024

The Art of Successful Data Migration: Strategies, Challenges, and Best Practices (part 1)

The though of migrating your company data may look daunting to you. It's no easy task. Our guide to data migration will provide you with all the information you're looking for!
Anthony Allen
|
7
min read
the-art-of-successful-data-migration-strategies-challenges-and-best-practices-part-1

What is Data Migration?

From small startups to multinational corporations, businesses rely heavily on data to drive informed decisions, streamline operations, and maintain a competitive edge.

However, as businesses evolve, their data needs also evolve. In today's data-driven world, many organizations eventually face the task of integrating, converting, importing, or transferring data as a vital component of organizational growth.

At its core, data migration entails extraction, transforming, and loading (ETL) data assets from one system or storage infrastructure to another while safeguarding data integrity, security, and accessibility. Data migration is a common and fundamental process for any organization undergoing digital transformations, whether loading a new file, upgrading to a new system, consolidating databases, relocating to a larger data center or transitioning to the cloud. 

Let’s look at some common data migration use cases.

Common Use Cases for Data Migration

Importing a File

This can be considered the smallest-scale form of data migration since it meets the most basic definition: extraction, transforming, and loading data assets from one system or storage infrastructure to another. You probably did not realize you were already migrating data on a monthly, weekly, or even daily basis.

System Upgrades

System upgrades enable organizations to align with performance demands or enhance competitiveness. A data migration process may be initiated when upgrading or replacing legacy hardware or software. This process is sometimes called Data Conversion which simply refers to the process of transforming data from one format or structure to another. 

Cloud Adoption

Cloud adoption refers to the process by which individuals, organizations, or businesses transition their IT infrastructure, applications, and services from traditional on-premises environments to highly-scalable cloud-based solutions. 

Database Migration

Your organization may issue a directive to enhance performance or achieve cost savings by scaling operations. This could entail transitioning from one database vendor to another (such as Oracle, Microsoft, or MySQL) within your current data center.

Consolidation/ Integration

Data integration is the process of combining data from different sources into a unified view. It may involve consolidating assorted files scattered across various devices or integrating multiple internal databases into a centralized location by establishing a data warehouse, data lake, or data lakehouse.

Data Center Relocation

Your organization may need to relocate its physical hardware to a new data center.  Alternatively, you can backup your existing data and then restore it at a new, permanent, and more secure location. 

Potential Data Recovery

Backup processes can be developed to allow your organization to enhance its disaster recovery efforts.

Mergers & Acquisitions

Incorporating newly acquired data sources into an existing parent system is another form of migration. This process falls under the broader umbrella of Consolidation/Integration. However, unlike internal data sources, the data being migrated originates externally.

The examples above clearly illustrate the diverse nature of migration, including integrating a new data source, enhancing organizational competitiveness, centralizing data, or transitioning to cloud-based systems.

Despite the specific context, the fundamental goal of data migration remains the same: ensuring the precise, secure, and efficient transfer of data assets from one point to another, while minimizing disruptions to regular business activities.

Should I Stay or Should I Go?

Data migration is “almost, but not quite, entirely unlike moving into a new house” to paraphrase a well-traveled hitchhiker, (Adams, 1980).

At their most basic level, both endeavors involve assessing what needs to be moved, organizing belongings (assets), and making logistical arrangements.  Here are some familiar scenarios for moving into a new house or apartment that parallel the use cases for data migration listed above:

  • Your (parent) organization may issue a directive for you to move out to achieve cost savings by scaling operations (downsizing). 
  • You and a new roommate (or partner) will undergo the process of combining your assets into a “unified” home. This may involve consolidating furniture scattered across various family homes or integrating multiple small appliances into a centralized location by establishing a new kitchen/dining area (consolidation).
  • You and a new roommate (or partner) are splitting your assets into separate homes.  You will have to determine which asset belongs to whom and design a separate migration process for each new location. 
  • You are moving to a commune (cloud) where many tenants are living together and sharing possessions and resources.

Just like moving into a new house requires careful planning and preparation, a data migration project necessitates thorough planning to ensure a smooth transition. Whether moving your personal assets or migrating your data assets, there are several questions common to either scenario:  What needs to move? Where is it going? How will it get there? Who will be executing the move? In this article, we aim to highlight that migration is a familiar process.

"Preparing to move" is a life skill that many of us already possess. With this in mind, you'll observe that we intentionally interchange terms like data, asset, packed box, and furniture. This serves not only to underscore the metaphor but also to prompt reflection on the processes integral to orchestrating any successful move or migration. It all boils down to application, scale, and perspective. 

Common Challenges and Risks

Data migration projects can face many challenges, from unexpected technical complexities to organizational resistance. So, where should you start? Let's begin by examining common challenges associated with any data migration project. It's important to note that not all projects will encounter every challenge listed but being aware of these potential hurdles will aid in planning a successful migration strategy.

Resource Management

Data migration projects may require significant resources, including human, software, and hardware. Constraints, such as time, budget, expertise, and the current infrastructure, can present obstacles to your migration endeavors. Identifying key stakeholders, including business users, IT teams, and external partners, is important so you can understand the resources at your disposal and their capabilities to navigate challenges effectively.

Planning for Downtime

Minimizing downtime during migration to avoid disruptions to critical business functions is often a priority. However, achieving zero-downtime during migration may prove challenging depending on your project. Be prepared to communicate any delays to both stakeholders and users. Maintaining stakeholder engagement by providing regular updates, addressing concerns promptly, fostering collaboration, and offering end-user training on new systems or platforms facilitates alignment with business and project goals.

Expect the Unexpected

You may face unexpected challenges across various fronts. Transitioning from outdated or legacy systems may introduce compatibility issues as older systems often have insufficient or missing documentation, further complicating the process. Proper planning, including backup resources, both systems and engineers, can help mitigate the effect of unexpected situations and guarantee a project’s success.  Without adequate planning, formulation of risk management strategies, and establishing recovery mechanisms, the risk of data loss, corruption, or inconsistency during migration, regardless of scale, significantly increases.

Security and Regulatory Compliance

Data governance and security are often overlooked aspects of the migration process. Data security should be integrated throughout migration incorporating measures like encryption, access controls, audit trails, and other security protocols to protect sensitive data during transit and while at rest. Organizations can proactively establish these policies to safeguard sensitive information, comply with regulatory mandates, and reduce the risks associated with data loss or breaches. 

 

In the context of a Real Estate transaction, a Use and Occupancy (U&O) permit is issued by local authorities to certify a property's compliance with zoning, building, and safety codes, allowing it to be occupied and used for its intended purpose.  Similar policies apply in certain data migration cases.  FOr example, compliance with a government’s regulatory requirements like GDPR or HIPAA is mandatory.  Just as neglecting safety concerns on a property could result in lawsuits due to safety issues that should have been addressed, any security lapses during migration can lead to financial consequences, potentially severe, for your organization.

Preparing for Data Migration

Before moving into a new house, it's essential to understand its current condition and layout.

Similarly, in a data migration project, understanding the current state of data, including its structure, quality, and dependencies, is crucial for planning the migration process effectively. Your "Moving Day" may still seem to be in the distant future. However, that does not mean you should postpone preparation. 

What steps should you take? While time is still on your side, take a moment to sit down and sketch out, in broad strokes, what you aim to achieve.

Then, begin thinking about the steps required to reach your goals. As a data migration project begins, you’ll encounter many questions. The most important will include: What data assets must be transferred? Where is their destination? Is the destination hosted on-premises or in the cloud? These questions should be followed by considerations like: What software/hardware tools are at our disposal? How will data be transported (e.g., ETL, batch processing, real-time replication)? And how can we confirm successful data ingestion?

Providing comprehensive answers to these initial questions will lay the groundwork for your migration project.

Taking stock of which data assets must be transferred

Surveying the Data Landscape

With the move date set on your calendar, now you need to start figuring out how to transport all your furniture (data assets) into a moving van (ETL tool) and arranging it to fit into your new home (database / lakehouse / system).

In his book "The 7 Habits of Highly Effective People," Stephen R. Covey emphasizes the principle of "Begin with the end in mind" (Covey, 2013). From a data migration perspective, this principle underscores the importance of understanding both your current assets and the ultimate destination.

Part of the preparation process should cover key factors such as data volume (how much data exists), structure (its diversity), quality (its condition), interdependencies (other processes relying on this data), what data will be migrated, and determining what can be left behind (archivable, outdated, or redundant data).  

Surveying the data landscape

Inventorying Data Assets and Gather your Metadata

Inventorying data assets involves systematically cataloging and documenting all data assets earmarked for migration within an organization. We need to identify all potential data sources, including databases, file systems, applications, spreadsheets, and any other repositories where data is stored. It can be helpful to document the fine details of the metadata (i.e. data that describes your data) for each asset.  Include details such as data type, format, size, location, owner, creation date, and usage. While not necessarily required for every migration, this step can provide valuable insights into the characteristics and context of each asset. 

Gathering and organizing your metadata via Data Governance and Master Data Management serves multiple purposes—it informs risk assessment, guides resource allocation, and aids in creating customized ETL strategies. Moreover, it streamlines decision-making regarding what data stays, what gets discarded, how it should be packaged and transported, and how the data should ultimately be used. Your metadata documentation will also serve as a reference for future data management activities (for the next time you move) and can help establish a robust foundation for decision-making processes (just like realizing you only needed 1 rainbow-colored WiFi-enabled spatula).

House Cleaning: Data Quality through Transformation

As you pull the glasses off the shelf, you can see that some are slightly chipped while others look like they need to go through the dishwasher one more time before being packed away and moved. 

Your organization's data assets may be in a similar state. Assessing data quality involves profiling the data to detect inconsistencies (spots), errors (chips), or duplicates (extra spatulas).  You can also test for completeness (do you have 8 matching sets of holiday place settings) and consistency (does all your silverware have the same pattern). Now is an excellent opportunity to do some scrubbing and polishing of your assets.

Data scrubbing, also known as data cleansing or data cleaning, is the process of identifying and correcting errors or inconsistencies in a dataset to improve its quality, accuracy, and reliability. The primary goal of data scrubbing is to ensure that the data is consistent, complete, and free from any issue or inaccuracies that could negatively affect its usability for analysis, reporting, or decision-making purposes.

Put simply, the implementation of data cleansing techniques ensures accuracy and reliability. However, any alterations made to the data must be documented and sanctioned by stakeholders to avert unforeseen complications in the future, such as the absence of important data when needed.

Evaluating data quality not only reveals areas for enhancement but also guarantees the migration of high-quality data (maybe you can ditch that unmatched spoon you accidentally took home from the restaurant one night). You should be prepared to create validation tests to address inconsistencies, errors, duplicates, and inaccuracies. Other steps may involve data standardization, deduplication, enrichment such as incorporation of default values for new fields not existing in the source system, and normalization techniques aimed at enhancing overall data quality. 

House Cleaning: Removing Obsolete Data

Descending into the dark and damp basement of your data center, you uncover stacks upon stacks of old, forgotten newspapers hidden beneath tarps—items that should have been recycled long ago. Surely, you have no intention of covering the cost to move these relics to your new home, nor do you wish for them to occupy any space in your presumably pristine, uncluttered, fresh-smelling data center. 

From a data migration standpoint, obsolete or outdated data adds unnecessary bulk to the dataset, resulting in extended migration times and heightened resource consumption. In essence, managing a smaller dataset is inherently simpler and less complex than managing a larger one. Furthermore, migrating obsolete data incurs supplementary costs associated with storage, processing, and management. 

Eliminating obsolete data will conserve expenses by directing resources exclusively towards relevant data. Additionally, retaining unnecessary obsolete data may pose compliance and security risks, particularly if it contains sensitive or personally identifiable information. Properly purging such data before migration aids organizations in adhering to data protection regulations and enhances overall data security. Additionally, by preemptively removing obsolete data, the migration process becomes more streamlined and efficient, paving the way for improved decision-making and analysis post-migration.

Standardizing Formats

Have you ever reached into a drawer expecting a teaspoon, only to pull out a tablespoon or, worse yet, a fork? Your entire morning routine disrupted! Consider the wisdom of the old adage: "A place for everything and everything in its place." The same principle applies to your data assets. Data standardization involves systematically cleaning and organizing data according to predefined rules and conventions, akin to sorting brights, whites, and dark colors separately when doing laundry (Yankovic, 1990). Adhering to consistent structures and guidelines significantly reduces discrepancies, facilitating accurate mapping and transfer of data during migration processes. Standardizing formats and schemas fosters interoperability among systems and can even manage expectations, as you know what to expect each time you reach into the drawer for a teaspoon.

Identifying Dependencies 

After meticulously packing up all the pots and pans, including an itemized list, and sealing the boxes, your friends cart them away and pack them in the back of a rented U-ETL van.  Naturally, later that day, after packing away two other rooms, you stumble upon an overlooked drawer brimming with glass lids. While you could simply pack them separately, it would have been better if they were packed together and clearly marked in a way that everyone knows they should be kept together. 

Similarly, understanding dependencies on interconnected systems or processes is important to safeguarding the integrity of data post-migration. Neglecting these dependencies could result in broken links or incomplete data, leading to errors or inconsistencies (such as pots missing lids) in the migrated data. It's essential to account for all interdependencies to ensure a seamless transition and maintain data integrity throughout the migration process.

Data Validation

By validating data integrity beforehand, organizations can prevent the transfer of corrupt, incomplete, or inaccurate data during migration, averting potential issues afterward. In addition, utilizing the same, if not similar, validation tests post-migration will ensure that valuable information was not lost during the transfer. Devoting effort to data quality assessment can avert future complications (like at Holiday dinners when your mother-in-law frowns over mismatched forks), minimize disruptions, and facilitate a smoother transition to the new environment all the while preserving the value and reliability of migrated data for ongoing operations and initiatives.

Developing a Migration Strategy

At this stage, you've completed house cleaning, meticulously itemized and labeled your assets, and earmarked certain items for long-term storage. With a clear grasp of the contents in each packed box, you can now determine the responsible party for transportation, the most suitable transportation method, and the optimal placement in the new setting. For instance, contemplate who you’re entrusting to transport important or delicate items.

Sensitive data assets might require handling by a specialized team employing a more secure method, especially in instances regarding mandatory regulatory compliance. 

Formulating A Strategy

Whether formulating a data migration strategy or preparing to move into a new home, three "S"s come to mind: Sequence, Supplies, and Structures.

Sequence involves organizing the order in which assets will be migrated, much like the meticulous approach taken when packing for a move. "Put first things first" and “Begin with the end in mind” as emphasized by Covey (2013), are good habits to keep in mind when formulating a migration strategy. Just as you would methodically pack up your old kitchen, there's a corresponding order for unpacking small appliances, silverware, etc at the new location. Recognize that certain assets may hold greater business significance and may need to be accessible sooner before the entire migration process is completed. 

Before starting to box up belongings (assets), you would probably make sure you have enough supplies including boxes, wrapping material, and tape. If not, you will need to delay to take an unplanned trip to a supply store. 

Likewise, we want to assess whether any 3rd party software or other hardware purchases are necessary before diving into the details of the ETL process we may be performing. Keep in mind that acquiring these resources may require navigating approval channels, which can take time. Ensure your resources are secured and available before starting development to avoid unnecessary rewrites caused by sudden changes in tooling. Failing to do so would be similar to running out of cardboard boxes (i.e. best practices) and resorting to trash bags (less than best practices) mid-packing. 

Just as you wouldn’t move and unpack into a kitchen that hasn’t been built yet, you want to ensure that the necessary structures are in place to support your existing data assets before starting your project.

This may involve setting up at least one test environment with the same degree of hardware and/or software customization as the intended target destination. However, success in a data migration project hinges not only on physical infrastructure but also on well-established organizational frameworks. Establishing a robust project management framework is paramount, ensuring clear communication, defined roles and responsibilities, timelines, milestones, and risk management strategies. Additionally, implementing a change management process is crucial for managing stakeholders' expectations and addressing resistance to change.

As discussed earlier, data governance is another framework that may be necessary to maintain data security and compliance requirements throughout the migration process. By laying down these structural and organizational foundations, your organization can effectively mitigate risks, uphold data integrity and security, and achieve successful outcomes in its data migration efforts.

Stay Tuned for the Next Steps…

In the upcoming article, we'll explore various migration approaches, such as the Big Bang Migration, which entails transferring all data simultaneously, the Phased Migration, which divides the process into manageable stages, the Manual Migration, relying on human intervention for data transfer, and the Fully Automated Migration, utilizing automated tools for a seamless transition.

Afterwards, we will discuss several factors that will help you determine the final migration destination: Cloud or On-Premises. Cloud Migration offers the allure of the "Great Commune in the Sky," offering scalability and flexibility, while On-premises Migration maintains control and security within your existing physical infrastructure. Additionally, we'll delve into selecting the appropriate tools and technologies to aid migrations, including options like SQL Server Integration Services (SSIS), Azure Data Factory (ADF), Power Automate, and Robotic Process Automation (RPA). Lastly, we'll address several post-migration activities, including thorough data verification, validation, and reconciliation, alongside continuous monitoring and the implementation of alerting mechanisms to promptly tackle any arising issues.

Sources:

  • Microsoft Learn: Create a Data Migration strategy for Dynamics 365 solutions

https://learn.microsoft.com/en-us/training/modules/data-migration/

  • Microsoft Learn: Prepare data for migration to finance and operations apps

https://learn.microsoft.com/en-us/training/modules/prepare-data-migration-finance-operations/

  • What is Data Migration?

https://azure.microsoft.com/en-us/resources/cloud-computing-dictionary/what-is-data-migration

  • Microsoft Learn: Design migrations

https://learn.microsoft.com/en-us/training/modules/design-migrations/

  • Covey, Stephen, R. (2013). The 7 habits of highly effective people: Powerful lessons in personal change. New York, NY: Simon & Schuster

https://www.franklincovey.com/the-7-habits/

  • Adams, Douglas. (1980). The hitchhiker's guide to the galaxy. New York: Harmony Books

https://en.wikipedia.org/wiki/The_Hitchhiker%27s_Guide_to_the_Galaxy

  • Yankovic, Alfred. (1990) Laundry Day

https://www.youtube.com/watch?v=eTxS_8OfSAI 

  • 1stDibs (2022) Are Fabergé eggs fragile?

https://www.1stdibs.com/answers/are-faberge-eggs-fragile/

How can The Virtual Forge help?

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Our professionals can provide expert assistance in various services, such as data migration, data quality, AI & ML, Data Visualization, Data Governance and Data Strategy.

Feel free to get in touch with us.

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