Starting a Data Migration journey without thorough preparation can lead to unexpected detours, roadblocks, and even disasters. It’s like setting off on a road trip without a map or GPS, who knows where you might end up. To avoid going off the beaten track you need to first know where you’re going (the desired outcome) and what you’re working with (source data and systems). So to get started right, there’s one essential step that stands out: Data Profiling.

What is Data Profiling?

Data profiling is the process of examining and analysing the characteristics and quality of data within your source systems. It involves a systematic assessment of data to gain insights into its structure, completeness, accuracy, and consistency.

By using profiling tools and techniques you can uncover hidden patterns, anomalies, and discrepancies within your data. This knowledge helps you make informed decisions, and you can then use these to build a robust data migration strategy.

Benefits of data profiling

Access to the insights that data delivers is generally the primary driver behind a data migration and therefore it needs to be fully examined and understood right at the start of the project. Knowledge of the type, quality, volume and structure of your data will set you on the right path, but you’ll also need to overlay some key considerations to ensure the data is handled correctly.

  • Data Health:  Analysing the source data allows for the identification of inconsistencies, missing values, and outliers. This important first step allows you to go into your migration with eyes wide open.
  • Mapping and Transforming: Once you know what you’re working with you can assess how data elements in the source system correspond to those in the target system. This knowledge simplifies data mapping and transformation, reducing the risk of errors and data loss during the migration.
  • Cleansing Data:  Identifying data issues early allows you to clean the data in the source system prior to migration, preventing the transfer of inaccuracies into the new system.
  • Risk Mitigation:  Data profiling acts as a risk assessment tool. It allows you to foresee potential issues and bottlenecks that may occur during migration. Identifying and addressing these issues in advance can save time, resources, and headaches down the road.
  • Allocating Resources:  Once you’re aware of the complexity and volume of data you’re working with you can then allocate the resources required for a successful migration. This could include hardware, software and people resource, and will assist in setting realistic budgets.
  • Compliance and Security:  Industries like Healthcare hold data that is sensitive and confidential, meaning there are various regulations that apply to how the data is handled and stored. By profiling the data early, you can make sure that the data is protected in the right way during the migration.

Types of data profiling

There are three types of data profiling to consider:

  • Structural looks at the organisation of the data making sure everything is uniform and consistent. It uses basic statistical analysis to return information about the validity of the data.
  • Content assesses the actual data values, uncovering patterns, outliers and discrepancies with the data itself. It’s focused on the quality of individual pieces of data.
  • Relationship explores the connections, similarities, differences and dependencies between data sets.

Steps to Success

Now that we understand the significance of data profiling, let’s explore the steps to conduct effective data profiling before embarking on a data migration project:

  • Identify and Collect Data Sources: Begin by identifying all the data sources that will be involved in the migration. This includes databases, files, APIs, and any other repositories holding your data. Next, gather comprehensive samples of data from each source. Ensure that your samples are representative of the entire dataset to get an accurate picture.
  • Analyse and Document: Use data profiling tools and techniques to look for patterns, outliers, and discrepancies. Assess data completeness, accuracy, and consistency, and document your findings in detail. This will serve as a reference throughout the migration project and beyond.
  • Build a Data Map: Use the insights gained from data profiling to create a detailed data mapping plan. This plan defines how data will move from the source to the target system, including transformations, conversions, and validation rules. Be sure to also include a risk assessment with contingency plans so that you’re ready for any challenges that may arise.
  • Cleanse: Now it’s time to address data quality issues identified during profiling. This may involve deduplication, standardisation, and enrichment to consolidate and unify data sets from different sources.

What to watch out for

While data profiling is essential, it can come with its own set of challenges. Here’s a few to watch out for.

  • Data Volume: Profiling large datasets can be time-consuming and resource intensive. Be sure to use efficient tools and techniques to handle significant volumes of data.
  • Data Variability: Data can vary in format, structure, and quality. Profiling must account for these variations so that you can get accurate insights off the back of it.
  • Integration Complexity: Profiling multiple data sources with different formats and schemas can leave you tied in knots, so be sure to follow the steps so that integration challenges can be avoided when merging profiling results.
  • Data Privacy: Ensuring that sensitive data remains confidential during profiling can be challenging. Proper data anonymisation and security measures are essential.
  • Maintenance: Profiling results should be regularly updated to account for changes in the source data. Have a plan in place for ongoing maintenance and monitoring.

Data profiling is not just a preliminary step in data migration; it is the compass that guides you through the entire journey. By understanding the nuances, quality, and structure of your data, you can mitigate risks, allocate resources effectively, and ensure a successful migration. It also helps you set realistic timeframes and budgets, so if you need a specialist to help you profile your data and establish a clear path for migration get in touch. We’d love to help.