With this transition from legacy to cloud platforms, some tech execs are struggling with the integration of legacy data with newer cloud-based application data. This is where cloud-based AI and machine learning tools can help.
Businesses still rely on legacy systems that contain valuable data, and don’t necessarily want to incur the cost of migrating all this data, which presents a challenge to integrate this data with modern cloud application data. There are best practices that can help effectively integrate legacy data using cloud-based AI and ML tools for maximum efficiency and accuracy. Those steps include:
- Understand the data – Before integrating your legacy data using cloud-based AI and ML tools, it is crucial to have a thorough understanding of the data.
- Choose the right integration approach – Depends on the volume, complexity, and sensitivity of the data. Choose batch, real-time or hybrid integration approaches.
- Ensure data governance – Establish proper for data ownership, access controls, and data security protocols.
- Leverage Automation – Use automation to streamline data migration, transformation, and synchronization processes.
- Monitor Performance – Ensure tracking data quality, accuracy, and timeliness.
Tools are enablers, and data is critical to the success of your AI/ ML frameworks. A well-thought-out plan on how your data will be ingested will add to the success of your initiative.