You're facing a data system merger. How do you align diverse systems without causing chaos?

One thing I’ve found helpful at the beginning of any system merge initiative is to establish the business objective. In my experience there are numerous way to merge 2 or more systems. The best scenario is to adopt 1 system as the common process, and the rest of system transform to follow the process. The most complex scenario is inclusive of several different processes in the target system. This approach usually ended up with inefficiency, low user adoption and high technical debt. It’s should avoid at all cost. In any case, initial data audit or data discovery process allow you to take inventory of data elements to simplify the scope. You can take action remove/retire unused data, transform similar data to common data structures.

Start by mapping out each system's data structures, formats, and workflows. Identify commonalities and discrepancies to create a unified data model. In my early career, I led a data system merger for two merging manufacturing companies. I began by auditing inventory management, production schedules, and supply chain data across both organizations. This showed incompatible product codes, conflicting inventory counting methods, and misaligned production capacity metrics. By identifying these issues early, I developed a thorough integration plan. This included creating data translation matrices, establishing new unified coding standards, and designing a phased rollout to minimize disruption.

Data mapping is the next step after the data discovery and identified what data elements need to be merged. During the data mapping, you may experience some data elements from the source need to consolidate/aggregate/transform to target data mapping. Some may need to trim/reduce/transform to target data. Or same source data need to be populated to multiple target data elements. It's crucial to understand the expectation for each scenario and documented as detail as possible in the data mapping documentation.

Early in my career, I worked on merging customer databases for two regional banks. We initially thought it would be straightforward until we started mapping. We discovered that 'Account Number' in one system was a 10-digit code, while the other used a 12-digit alphanumeric format. Also, 'Transaction Date' was stored in different formats, and 'Customer Risk Ratings' used incompatible scales. We created a mapping document, detailing how each field would be transformed. We also built a custom script to generate new unified account numbers and standardize risk ratings.

n my experience, a well-defined integration plan is key to a smooth data system merger. I start by mapping out the existing systems, identifying overlaps and discrepancies. I then prioritize the integration of critical data sources, ensuring minimal disruption to ongoing operations. I create a phased approach, gradually merging systems and thoroughly testing each step before moving on. By following a structured plan, we can avoid data loss or corruption and ensure a seamless transition for end-users.

While working for a major oil and gas company, I led the integration of our exploration data systems with those of a recently acquired smaller firm. Our plan began with a audit of both systems, focusing on geological data, seismic interpretations, and well logs. We developed a phased approach, starting with harmonizing coordinate systems and units of measurement. The plan included data validation steps, creation of a unified data model, and development of data transformation scripts. We scheduled integration during low-activity periods to minimize operational disruption. The plan also incorporated extensive testing phases and a rollback strategy for each stage.

On a project with a financial firm, I was faced with compatibility issues when integrating a new fraud detection system with the existing tax processing infrastructure. The new system used advanced machine learning algorithms but was built on a modern cloud platform, while the legacy system ran on mainframes. We addressed this by developing a middleware layer that could translate data formats and protocols between the two systems. We also implemented a data lake to store and process large volumes of data in a format accessible to both systems. The project required careful planning to make sure that real-time data sharing was possible without compromising the security and integrity of sensitive data.

Data lineage possess a variety of benefits towards guaranteed data integrity is achieved. It provide transparency that making it easier to detect any probable errors or tampering with the data, data lineage helps in understanding where data came from, how it has been changed and the direction where it has taken. It enable traceability and monitoring through data lineage allow for checking alterations and access to data, as a result helping in identifying and curbing any possible tampering that may occur. By ensuring data validation, data lineage aids in verification to data compliance with business rules.

You must check that data quality aspect; like validation, consistency or temporality; isn't negative affected because all consumers of the previous data systems will use the new merged data system on theirs consuming application to work: if they consistency changed in their dashboard, you will have a problem!