What are the most common data quality issues during data migration?

In my experience, a common data quality issue found during a migration are related to inconsistencies in the business processes themselves - where the data may conform to basic quality standards, but what the data represents is inconsistent over time. For example, you could have a field defined as a customer field, but where end users start to 'morph' the meaning to possibly include other entities that are not customers. These types of quality issues are much harder to detect, and require extremely deep levels of knowledge on business processes - but are more common than you would think.

The most common errors one could see on data migrations are: Formatting errors - dates, currencies and currency separators ( , vs . ), spaces etc.,. Text vs numbers & and their character restrictions on source vs target systems Issues in designs caused by a lack of understanding of - use cases/system capabilities, consumers and their needs Timing - Source and target systems should be fully ready to send and receive data. Parallel programs and interruptions can truncate data flows All of the above can be addressed with proactive planning, strategic implementation, hyper care and coordination between members from start to finish

Lack of planning and execution in data migration projects , testing, validation, and data quality assurance measures could be the main reason. Regularly monitoring and maintaining data quality post-migration is also crucial for long-term success. Also, When converting data from one format to another, errors can occur in the transformation process as well.

Duplicidades, despadroniza??es, informa??es obsoletas,inválidas ou incompletas s?o os principais problemas encontrados durante um processo de migra??o de dados. Importante neste momento ter clareza de quais s?o as origens de entrada dos dados para garantir a corre??o definitiva. Caso contrário,após a migra??o, as bases voltarao a apresentar os mesmos problemas

1.Incomplete Data: Missing spatial or attribute information. 2.Inaccurate Data: Spatial misalignments or coordinate system issues. 3.Inconsistent Data Formats: Variation in date formats, units, or coordinate systems. 4.Data Transformation Errors: Mistakes in data conversion processes. 5.Duplicate Data: Occurrence of duplicate records. 6.Data Integrity Issues: Challenges in maintaining data integrity constraints. 7.Attribute Mapping Problems: Mismatched or incorrectly mapped attributes. 8.Data Quality Assessment: Lack of comprehensive pre and post-migration checks. 9.Data Volume and Performance Issues: Challenges with large dataset processing. 10.User Training and Adaptation: Issues arising from user unfamiliarity with the new system.

In my experience, data consistency often becomes an issue, particularly when there are inconsistencies in data formatting. Taking drilling data as an example, numerical values such as dates or measurements (metric vs. imperial units) can vary, necessitating careful conversion and standardization to ensure data integrity. It seems I should also emphasize Data Integrity alongside Data Consistency :)

I have experienced that there is a need for more time to spend on data cleansing activities. To often irrelevant information is migrated that will mean that unnecessary resources are spent on the migration. The solution is to try to identify and filter away irrelevant information!

Data consistency is very important to get the desired result from the ERP functions. Though you have configured ERP system to get many analytics by a click of button, unless the data is consistent the results are inaccurate. Any decision taken on the result may mis-lead the overall function of the organization.

I have found that if data is migrated from multiple systems that the data format and field lengths causes a lot of issues during load. Additionally ensuring the prefixes are aligned in all tables to remove duplicity. Something I found recently was that some organisations allows the creation of field values e.g. Org names. This just started a complete new working group to ensure the consistency in the naming convention.

Here again the understanding of source system in terms of data definition and data structure is very helpful for proceeding in the right direction. if high level of information is available then with help of SQL and advanced tools available these days redundancy and data cleansing is not much completed but could be time taken for huge data. One of the critical thing to minimize the problem at this stage is the decision. In case of multiple systems, either single system or specific entities/objects must be finalized from different systems as most reliable and only considerable data.

You can find a lot of data quality issues during a migration, but one of the most common is around data completeness, or missing data. You're trying to populate one database from another and that other database doesn't have some of the data you need. I've seen people remove restrictions requiring data so everything is optional. That gets them around missing data, but doesn't solve the problem. If the business said certain data is required, then the data is required. You have to solve the problem when you don't have the data. The business might be able to give you a default to use. The business might need to look at the data to determine what it should be. The business might even decide they don't need it, but that's their decision.

Data plays a critical role in every business operation, particularly when we depend on it to meet service level agreements and financial commitments. Therefore, it's crucial to categorize data accurately based on its type before transferring it to other platforms.

The definition of completeness varies from Organization to Organization. In many Organizations, as long as there is enough information to identify the item & can be procured within the required timeline from a Vendor is called complete. In the recent days, I have observed some organizations are looking for detailed technical information of an item which may not be provided by Manufacturer / Vendor. It is not worth putting an effort to get these information to call the data complete in all aspects.

For Data Completeness Target Systems understanding is must. in case of famous ERP's they are smart enough to validate the data and their application layouts are great starting point to figure out the minimum required information to have a record completeness. In case of custom application this could be a challenging task.

Data should be treated as Jigsaw picture and even if all the parts/components are migrated successfully from source to target, it may not form same picture, like it was in the source. So it is essential to set up rules and strategy to connect all the data components are in right order and form right dependencies.

Data Integrity is a major scope of IT team of any Organization. Making sure the data provided by various departments like procurement, finance, operations, stores, Purchase etc., are available for the specified users. There is proper user configurations and access given to the required modules only.

Data integrity refers to the accuracy, consistency, and reliability of data in its lifecycle, from creation to consumption. During migration, there’s a risk of data loss or corruption. Conduct data profiling and cleansing to identify and remove inconsistencies, duplicates, or incomplete records from the source data. Implement data validation checks to verify accuracy in both source and destination systems. Consider an incremental migration approach, transferring data in smaller batches to minimize the risk of data loss and enhance integrity. Keep an audit trail in case of unexpected issues. Preserving data integrity and security safeguards your valuable information and contributes to successful data migration.

This is one of the challenge I have seen with ETL tool where instead of giving a warning it was directly truncating the data. Not sure it was the tool or the limited knowledge of the developers but yes in such case one has to be very careful and that's why a cross check of migrated data is must. post migration , developers should be given enough access and tool to compare the migrated data at column and row level.

Data relevance is a crucial aspect during data migration, ensuring alignment with current and future organizational needs. It involves assessing data quality, timeliness, accuracy, and usefulness. Outdated or irrelevant data can hinder the migration's value and impact on business objectives. Data analysis, examining and extracting meaningful insights, and robust data governance, establishing policies and standards for management, are essential for ensuring data relevance. By addressing this issue, organizations can optimize their data for the new system and align it with evolving business goals.

The most significant data quality issue that will hit your migration will be the mindset of the organisation and the implementation project team towards data. The mindset will come from the Business Sponsors in terms of how much they drive cleansing by operations, invest in cleansing, improve data literacy as well as rewarding and recognising good data practices. This, in turn, will feed into the project team who will understand that not having the data migration on the critical path of the plan, last minute changes to functionality, not testing properly with a credible data set are more likely to create data issues far beyond specific data quality points - which are probably far more easily resolved after you have migrated anyway!!

Ensuring data quality in data migration projects can be challenging, as it involves dealing with various types of data issues, such as?inconsistency, incompleteness, duplication, corruption, invalidity etc.

DQ during data migration is double tricky as you need to show how the data in the source will be fit for purpose in the target. So you need to understand the quality requirements of the target and apply these rules to the source (or sources, which makes it even harder). You do need to be pragmatic and not focus on fixing everything as you will never have time or budget for perfect data. And remember that you may choose not to fix data issues at source as you have the option of fixing them in data migration pipelines (because there will be no data left in the source after migration).

In the voyage of data migration, a vigilant captain encountered a narrative of challenges. * The journey began with data inconsistency, akin to navigating treacherous waters filled with varying formats and standards. * Duplicates emerged as the protagonists, lurking beneath the surface and threatening data integrity. * Incomplete records became the storyteller's obstacle, hindering a smooth transition to the new environment. * Inaccurate data played the role of turbulent waves, causing disruptions in the migration process. This narrative where data inconsistency, duplicates, incomplete records, and inaccuracies converged, creating a map of challenges that required careful navigation in the dynamic landscape of data migration.

Common data quality issues during data migration include incomplete or missing data, inconsistent formats, inaccurate or outdated information, duplicate records, and mapping errors. These issues can arise due to differences in data structures, migration tools, or data sources. Inadequate validation processes, insufficient testing, and poor communication between teams can exacerbate these problems. Addressing these issues requires thorough planning, data profiling, and validation checks to ensure a smooth and accurate data migration process. Regular audits and collaboration between stakeholders are crucial for identifying and resolving data quality issues during migration.