Your company is merging, but data quality varies. How will you bridge the gap?

Evaluate the conceptual modeling, the business processes and data flow diagrams to understand how the data is generated: these documents indicate how the company understand the business world, and how address it with own solutions. Use data profiling on all storage that company will use you know how data is worked on their systems in reality; this way you can understand well what data quality problems will show you from business and systems perspective.

Merging companies with different data standards can be tricky. In my experience, the key is to establish a clear, unified set of data quality rules from the start. This means analyzing the data from both organizations, identifying discrepancies, and working collaboratively to define a common set of guidelines for data formats, naming conventions, validation, and error handling. This upfront effort might seem tedious, but it pays off in the long run by creating a solid foundation for a unified and reliable data environment that can support the newly merged company's goals.

When merging companies with varying data quality, I've learned that cleaning data is a crucial step towards integration. I start by identifying and addressing inconsistencies, errors, and duplicates. This might involve standardizing formats, filling in missing values, and removing irrelevant data. By ensuring the data is clean and consistent, we create a solid foundation for integrating the different systems, ultimately leading to a unified and reliable data environment for the newly merged company.

Bridging the data quality gap during a company merger requires a strategic and systematic approach to ensure consistency and reliability. Begin by conducting a comprehensive data audit to assess the quality of datasets from both companies, identifying discrepancies, redundancies, and gaps. Establish a unified data governance framework that defines clear standards, policies, and procedures for data management and quality control. Implement data cleansing and transformation processes to rectify inaccuracies, remove duplicates, and standardize formats across all datasets. Utilise advanced data integration tools and technologies to seamlessly merge and align the data, ensuring interoperability and consistency.