What challenges of data integration have you overcome, and how did you overcome them?

From my experience, I would say that Data integration presents a multifaceted challenge encompassing data variety, quality, volume, security, and real-time needs. When we do modernization projects, we have experienced legacy systems, governance issues, technical complexity, and cost further complicate the process. Data can arrive in various formats from diverse sources, often with inconsistencies or errors. Large volumes may strain resources, while security and privacy concerns demand robust protection measures. Integrating real-time data and legacy systems adds another layer of difficulty, requiring specialized solutions. Establishing clear governance and navigating the technical intricacies are crucial, as is managing costs effectively.

Overcoming these challenges necessitates a multi-pronged approach. 
- Thorough data profiling and cleansing are essential to understand and improve data quality. 
- Transformation tools help standardize data formats, while ETL tools automate integration processes. 
- Change Data Capture facilitates real-time updates, and a data catalog simplifies data discovery.
Strong governance, collaboration between IT and business stakeholders, and the use of appropriate technologies like cloud-based solutions and data lakes are all key to achieving successful and efficient data integration.

- Last, but not the least, Data virtualization and API integration offer flexibility, while Master Data Management ensures consistency.  

Ramki Krishnamurthy
Data Analytics Offering Lead
REI Systems Inc

The roadblocks here can be many, but include things like a lack of standard data formats, definitions, or quality standards.  There are many ways to address these issues, but a great starting point is to focus on developing some form of data governance policies which would allow you to create rules/policies to resolve differences across source.  These rules will then be configured into systems like Data Integration of MDM software to allow for greater levels of automation.  Trying to fix data differences at the source is also an option, but also the hardest to do.