Data Migration Best Practices

According to Gartner, throughout 2022, 50% above data migration endeavors would outstrip their planned budget and timeframe, and will possibly end up hurting their business owing to poor strategy and execution. Reason? Data migrations are not just expensive but are highly complicated and risky endeavors, and any gaps in their implementation can lead to ‘unexpected’ occurrences and ‘unwarranted’ surprises. To avoid the pitfalls and steer clear from straying from the path, following the best practices in data migration is essential. This insight takes a closer look at it.


Table of Contents

  1. What Is Data Migration

  2. Migration Myths and Misconceptions

  3. Data Migration vs. Data Integration

  4. Data Migration Planning and Strategy

  5. Data Migration Execution

  6. Maintenance and Follow-ups

  7. Data Migration and Beyond

1. What Is Data Migration

In simplest terms, data migration refers to moving data from one system to the other, often involving a shift happening in data storage, application, format, or database. It is the journey of data from its existing environment to a newer environment as per the businesses’ needs. From the perspective of the Extract, Transform, Load (ETL) method, every data migration process at least involves transform and load stages. It essentially means that the extracted data is required to go via a string of functions, post which it is loaded in a chosen location. Sound data migration needs meticulous planning, prompt action, and proactive execution; it is further enabled by technological and analytical expertise.

1) Why is Data Migration needed?

The frequency and need of data migration can arise from several factors, such as mergers and acquisitions, system overhauls and upgrades, database up-gradation, building new data warehouses, introduction of new systems, application consolidation, modernizing of legacy platforms (as transferring data remains no longer viable). Whatever may be the nature of migration, the underlying objective is to boost performance, get a competitive edge, and future-proof themselves.

2) Biggest Challenges in Data Migration?

Transporting data into advanced applications while having an insufficient understanding of your data’s present sources and future location can magnify enterprise problems. In addition, many other challenges can occur along the way.

  • Incompatibilities in the software or hardware environment
  • Resource unavailability
  • Interoperability gaps
  • Protocol issues
  • Interface troubles
  • Compromised referential integrity 
  • Data repository concerns
  • Data dissemination challenges     
  • Higher than estimated time-consumption
  • Vulnerable security 

2. Migration Myths and Misconceptions

Data migration may mean different things to different people from the outside, often generating preconceived notions about the process

Is Data Migration to do with ‘Copying Data’ As different systems have separate data models, almost no migration is ever a simple copying process. Mapping and copying occur along with data transformation, which ends up improving and at times repurposing the data.
Data Migration is a ‘One-Time’ Activity Data Migration is a phased-out process that takes place in multiple successive projects. This way, data can be tracked, evaluated, tested so that improvements can be made in batches, and by the time all data is migrated, the process is perfected.
Once Data is Migrated, old systems are discarded

Enterprises may or may not choose to keep old legacy systems after migrating some data to newer systems. Sometimes newly migrated data may be rolled out only for a specific set of end-users, and data syncing between the old and new is required. This may turn into a multi-year project.

3. Data Migration vs. Data Integration

As discussed above, data migration involves moving data from one location to another, for instance, from a spreadsheet or legacy application to an advanced ERP. Its reasons can be various, such as attaining efficiency, getting better prepared to compete with your contemporaries, getting to know your data better, gleaning better insights, improving the data, or overhauling the entire system. On the other hand, Data Integration involves combining data from various sources into a single source and better-synchronizing data exchange (which can be one-directional, bi-directional, continuous, API driven, Trigger Dependent Connector integrations). It can have separate purposes, from providing a single view to enterprises or saving costs (as systems are not renewed but rather stitched together). Integrations are generally quicker to implement.

4. Data Migration Planning and Strategy

From a broad perspective, data migration planning entails comprehending the complexity, timeframe, and costs of migration. Teams must be made aware of the project’s magnitude in terms of effort and scope early on. Instead of superimposing best practices of data migration during the later stages, they should be woven into the plan from the start.

Similarly, the strategy must take into account all the phases of migration. It should put together a team with the right skills, focus on data quality from the start, formalize data and analytics governance roles, policies and processes, and define the metrics and tracking mechanism. Some of the crucial steps are discussed in this section.

1) Choosing Technologies for Data Migration

Oftentimes there is more than one technology is employed in a single project. There can be already existing technologies, newer systems, and essential systems in every migration project. Leaders must also bear in mind while choosing technologies how long or regular the migration project will be in the future.

  • ETL (Extract, Transfer, Load) is one of the most favored technologies
  • Hand coding is popular as well despite higher costs
  • Data management replication is also preferred at times
  • EAI (Enterprise Application Integration) is the least preferred 

2) The Migration Roadmap

Highest consideration is needed to ensure business managers and teams across the spectrum are on the same page when it comes to detailing the project plan, timeline, and deliverables that will help them succeed. Migrations happening in one-shot do not strictly fall under engineering’s best practices. Handling smaller chunks of migration is ideal, especially when data dependencies are involved. Sometimes migrating to a packaged application may imply that legacy data must be repurposed to suit a predetermined data model; many other times, migrating to a freshly developed homegrown system may imply additional effort in data modeling.

5. Data Migration Execution

Execution varies from project to project, and no two migrations are ever similar. However, every migration execution also involves some key steps, without which migration is inconceivable. 

1) Locating your data

Understanding your data sources is the most crucial element and demands total visibility of your entire data so that any potential challenges (with the data) may not stay hidden in later stages. Analysis of data has a significant role to play in the proposed implementation. Also, the assessment of migration code should be based on a comprehensive analysis of the data sources, as opposed to only analyzing a sample, so that guesswork is completely removed from the equation. Creating a detailed knowledge bank of the data sources facilitates accuracy and quickness in data transfers. It is believed that a complete data audit can decrease the expense of code amendments by more than 75%. After locating the data, a phase-wise approach is undertaken starting from data profiling, defining data quality, data cleansing, and data verification.

2) Executing the Migration

The process of execution may vary according to the type of migration depending on whether it is a Storage migration (i.e., moving data from its present storage to a more modern system), Cloud migration (i.e., moving data or applications from on-premises data center to cloud, or from one cloud to another), or Application migration (i.e., moving application program from an environment to a new one; this may be on-premises to cloud, cloud to cloud or app to app). The constant thread in most migrations remains ETL coupled with the business logic. However, sometimes conventional extract, transform, load tools may be inadequate in handling free text fields complicated project needs such as matching fuzzy logic. In these scenarios, data quality tools have ‘parsing’ and ‘matching’ features. It is mainly done by separating and restructuring the content and then delivering it to the target location.

3) Testing

Testing can attain high-level of complexity during data migrations and thus demands careful attention. Before getting a nod from key stakeholders, testing of units, volumes, systems, online apps, and batch applications must be executed. One of the critical goals should be to have the entire data volume uploaded and to get online application tested early on for every existing work unit; in many cases, various work units might be needed to be finished first to attain this before performing online testing.

This strategy helps avoid storage issues when error rectification gets costlier during later development stages. Also, a significant risk that persists is that migration still remains in the development stage while data gets changed in the source systems. However, since most migration processes build a profile and audit of sources, it’s relatively straightforward to re-run the audit whenever needed to evaluate the modifications and act accordingly.

6. Maintenance and Follow-ups

As data gets placed inside the IT infrastructure, data audits can be executed at the desired intervals within the migration cycles (to examine if the project is on track and is operating as per the plan). Data quality devices can be constantly used to sustain data in a perpetual high-quality form so that future needs can be fulfilled smoothly and seamlessly.

Regular maintenance follow-up schedules with acceptable downtimes must be created beforehand to continually assess any risks. Checklists must include vendor contract reviews, IT system health checks, equipment audits, decommissioning and disposal of equipment, and implementing newer controls.

Besides, server and storage maintenance, workload restructuring, and performance-led reviews are essential as well. However, due to exhaustive checks in early stages as well as throughout the migration, maintenance activities can be productive and streamlined.

7. Data Migration and Beyond

Data migration development is an iterative process that entails architecting the solution design, modeling data, mapping the data movement, transforming data, solution development, testing, and finally, deployment.

Once deployment takes place, data reaches actual end-users, the duty to administer the data is passed down to the IT, most likely to the database administrators or field IT professionals. As old and new platforms function together, data synchronization becomes important too, implying that both ETL and SQL technologies fit the needs.

Next comes monitoring, wherein data’s success, gaps (if any), and scalability are identified and assessed further. Lastly, the legacy platforms are gradually phased out, but not before the operational capability of the new platform is thoroughly verified and authorized.  

8. Use Case 

Pimcore Helped a leading, global IT products distributor in North America to consolidate and manage vast amounts of product data

The organization’s existing product information (PI) system was incapable of handling their dynamic product data structure and integrating data from multiple sources had turned highly inefficient for them. Their legacy PI system took a long time to reflect the changes made by users. High dependency was on data providers to display information in web portal, as they had no standardized data model. And due to lack of a consistent and dynamic data model, the consolidation of product data from different data providers had become difficult. 

Pimcore consolidated their data from multiple sources to create a single view of products using Pimcore PIM and DAM. Automated taxonomy for 1500 product categories was created, resulting in a dynamic data model to handle 60k attributes (having close to 1.7 million drop down values). 

Pimcore transformed the product data from all sources into a standardized data model while consolidating the data efficiently. Pimcore provided business users to set data consolidation rules through a priority rule engine.

As a result, data was consolidated successfully and was made available from all providers as well as vendors in their B2B web portal. All product and digital assets could be managed in one place. They now also have the flexibility to manage images and rich media in any required format.

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