As technology develops many charities are considering whether to move their data from a legacy system


Data is a valuable tool for raising your charity’s effectiveness and impact. When dealing with sensitive data or outdated, error-prone legacy systems, data migration can be exceptionally challenging. Incorrect data migration might hinder your progress and expose your charity to security threats or donor uncertainty.

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What is digital transformation in charity?

Digital transformation in charity involves leveraging digital technologies to modernise operations, enhance donor engagement, and optimise resource allocation. This encompasses the adoption of digital tools like online donation platforms and crowdfunding campaigns to facilitate fundraising efforts. Additionally, ensuring high-quality data management is integral to this transformation. Charitable organisations utilise advanced analytics to gain insights into donor behavior, measure outcomes accurately, and make data-driven decisions. By maintaining data quality standards, charities can effectively assess their impact and tailor their strategies to better serve their communities. Moreover, digital technologies facilitate seamless communication and collaboration within the charity sector, fostering transparency, efficiency, and innovation. Ultimately, digital transformation empowers charities to reach a wider audience, foster stronger connections with supporters, and make a more significant impact in addressing social and humanitarian challenges.

Data errors make migrations difficult

Data migration is an important task for those charities experiencing challenges managing large data sets across multiple systems and databases. It can be difficult to understand the scale of potential data inaccuracies, and to choose the best method to address these.

Potential data problems can include test data entry errors, duplicate records, incomplete records, and data and record duplication. Administrators frequently deal with errors manually, but this can still leave a big vacuum in knowledge about which problems are most widespread and detrimental, not to mention the time and resources manual intervention require.

Take these examples:.

  • Non-unique descriptors as account identifiers can lead to unclear and confusing records
  • Data entered into incorrect fields
  • Data that is concatenated or entered incorrectly into a single field (eg no spaces in between first and last names)
  • Datasets describing information differently (one source could say ‘client name’ while the other source says ‘account ID’)

Even if one system doesn’t have any anomalies or inconsistencies, that doesn’t indicate another won’t. Further, it doesn’t mean that those sources describe data in the same way.

You might find yourself starting over due to inconsistent formatting and inaccurate data.

Your first step should be to evaluate the format and authenticity of your data across all systems. This is essential to the success of your relocation and could help you identify a variety of pre-existing problems. For example:

  • Formatting could be consistent across your data sources, but that doesn’t mean the data is right
  • An address could be right, but have no spaces, making it unusable
  • Date of birth could be out of range, or a unique reference number could be all zeros

Before you transfer the records to another source, you should have a clear understanding of your data’s structure. Otherwise, you risk being unclear on the current formats being used, uncertain on how to standardise the data, and unsure whether the data is accurate. Not only are you wasting time and money in this situation, but you also run the risk of flooding the new storage with unsuitable data, which could significantly impact business operations.

Data profiling pays dividends

Successful data transfers involve meticulous planning, forecasting, hazard identification and resource allocation. With the help of data profiling, you are able to identify problems before they materialise and deal with them effectively.

Everyone’s duties will be easier as a result, even those of your business users who are crucial to the success of the project as a whole. Consider this: Technical users can inform you of formatting and character difficulties, but business users are the ones who interact with the data on a daily basis and can inform you of the connections between the fields.

Pair this collaboration through data profiling and you are setting up your migration for success—and have clear next steps like standardisation, deduplication, and validation.

As you make progress on your migration, keep in mind the importance of quality control. This will be vital to maintain your data’s cleanliness and usefulness post migration. Remember: Your new system will only be as good and useful as the information it holds.

How to find the perfect solution

Data migrations can be difficult, and demand careful preparation, implementation, and continuing supervision. To guarantee a successful migration, charities should also consider challenges such as data compatibility problems, cybersecurity threats, staff learning curves, and ongoing support and maintenance requirements. The key to safeguarding a data migration is using the appropriate technology. Experian’s data quality platform is intelligent and user-friendly, with data profiling, monitoring, and validation capabilities that can be adapted to your unique needs in order to set up your migration for success.

How can we help?

Aperture Data Studio is designed for the modern data migration leader who needs to ensure that the target environment is populated with high-quality data while maintaining effective coordination and collaboration, all to a tightly controlled budget and timescale.

The solution can help you build a trusted data source for the new target system, profile your data and measure the impact of migration, improving acceptance of the new system, as well as mitigating costs.

Get in touch

Speak to one of our experts about how Aperture Data Studio can transform and enrich your data.

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Post tagged in: Data Quality