How can you convert data from legacy systems to modern formats?

1 Assess the legacy data

The first step is to assess the legacy data and understand its structure, format, quality, and volume. You need to identify the source and destination systems, the data types and schemas, the data dependencies and relationships, and the potential issues or errors in the data. You also need to evaluate the business requirements and expectations for the data conversion, such as the frequency, the latency, the accuracy, and the security. By assessing the legacy data, you can plan and design the data conversion process more effectively.

2 Choose the conversion method

The next step is to choose the conversion method that suits your needs and resources. There are different methods for converting data from legacy systems to modern formats, such as manual, automated, or hybrid. Manual conversion involves manually extracting, transforming, and loading (ETL) the data using scripts, tools, or applications. Automated conversion involves using software or services that can automatically perform ETL tasks and handle complex or large-scale data. Hybrid conversion involves using a combination of manual and automated techniques to optimize the speed, quality, and cost of the data conversion.

3 Prepare the data for conversion

The third step is to prepare the data for conversion by performing some pre-processing and cleaning tasks. You may need to normalize, standardize, validate, or enrich the data to ensure its consistency, completeness, and accuracy. You may also need to filter, sort, aggregate, or split the data to reduce its size, complexity, or redundancy. You may also need to backup, archive, or delete the data that is not needed or relevant for the conversion. By preparing the data for conversion, you can improve its performance and reliability.

4 Execute the data conversion

The fourth step is to execute the data conversion by running the chosen method and monitoring the progress and results. You may need to use different tools, platforms, or services to convert the data from legacy systems to modern formats, depending on the source and destination systems, the data volume and complexity, and the conversion method. You may also need to test, debug, or troubleshoot the data conversion to ensure its quality and accuracy. You may also need to document, report, or communicate the data conversion to stakeholders or users. By executing the data conversion, you can deliver the expected outcomes and benefits.

5 Verify and validate the converted data

The fifth step is to verify and validate the converted data by comparing it with the original data and checking its format, structure, quality, and functionality. You may need to use different methods, tools, or metrics to verify and validate the converted data, such as data profiling, data auditing, data reconciliation, data quality assessment, or data visualization. You may also need to review, evaluate, or feedback the data conversion to identify any issues, errors, or gaps and to improve the data conversion process. By verifying and validating the converted data, you can ensure its compliance and usability.

6 Maintain and update the converted data

The final step is to maintain and update the converted data by performing some post-processing and optimization tasks. You may need to index, partition, compress, or encrypt the data to enhance its performance, security, or accessibility. You may also need to integrate, synchronize, or migrate the data to other systems, platforms, or services to enable its analysis, processing, or sharing. You may also need to monitor, review, or modify the data to keep it up-to-date, relevant, or accurate. By maintaining and updating the converted data, you can support its sustainability and value.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?