A well-planned data integration strategy is essential for a successful data merge, especially when dealing with different data sources and formats... Data profiling: Perform thorough data profiling to understand the structure, content and quality of the two data sets. Identify inconsistencies, missing values and data quality issues. Data transformation: Apply appropriate data transformation techniques such as data cleansing, normalization and standardization to ensure data consistency and compatibility. Data integration tools: Use robust data integration tools to automate the process of data merging. These tools can handle complex data transformations and mapping rules, reducing manual effort and increasing efficiency.

Merging data will have multiple aspects like 1. Data Profiling and Quality Assessment: Understand the data's strengths and weaknesses. 2. Data Cleansing and Standardization: Ensure consistency and accuracy. 3. Data Mapping: Connect corresponding fields. 4. Data Transformation: Modify data to fit a unified structure. 5.Data Validation and Testing: Verify data integrity. 6.Data Integration: Combine datasets efficiently. 7. Data Governance: Maintain data quality and consistency.

My perspective to think beyond the prompts is: 1. Build an industry standard data mapping. Identify your sources attributes and create a documentation to align with industry standard metrics. 2. Cross cloud platform integration : Make sure your unified cloud platform is compatible with variety of formats and structure of the data including relational data. 3. Define unified strategy:Leverage Uniform from Databricks. 4. Define your enterprise common data model: CDM will help you with data definitions and data mapping. Leverage mapping and modeling tools to ensure cross company models speak the same language. 5. Ensure business validations: involving business during the modeling phase will ensure data mapping accuracy and data reliability.

Data is critical to every organisation be it financial, healthcare or manufacturing. Processing data using right tool and at right time should be priority to get more insights when needed. Modern data warehousing now a days providing a vide range of tools to ingest, process, computer and report it to right platform. I found Synapse and ADF tools now a days which are playing critical role here with the help of ML and other analytics. Currently we are also working on such requirements only, dealing with huge unstructured data to process, compute and generating insights and reporting it via BI for proactive measures and decision making.

You need a common language at the business level - ideally a formal ontology with all the relevant business concepts. Then map data sources to that. You will need a 'shim' for datatypes, to map ontological quantities (dates, measures etc.) to the relevant typing schemes, covering some of the other answers here on e.g. date formats. Note the business-level ontology is not quite the same as an OWL ontology for RDF data - that comes later.

To align different data formats and structures during a merger: 1. Standardize Formats: Convert data types, units, and structures to a common standard. Example: Align date formats (MM/DD/YYYY vs. DD/MM/YYYY) and currency units. 2. Map Fields: Match corresponding fields across datasets. Example: If one dataset uses "Customer_ID" and another "Client_ID," create a mapping table. 3. Resolve Duplicates: Identify and merge overlapping records using unique identifiers. Example: Deduplicate customers using email or phone. 4. Handle Missing Data: Impute or flag gaps based on the business context. 5. Validation: Run consistency checks to ensure alignment. Accurate integration fosters trust and insights.

Merging data from two organizations requires a structured approach to align formats, structures, and systems. The process begins with an analysis to inventory sources, identify overlaps, and assess quality. Data standardized principals and standards should be defined using industry standards and best practices from each company. Schema mapping to align structures, and conflicts such as duplicates or inconsistencies are resolved, ensuring clear reconciliation rules. The merged dataset is integrated, tested for accuracy, and governed with access controls and documentation. Collaboration between stakeholders ensures alignment with business goals, while tools can be leveraged to capture metadata and streamline the technical process.

Conduct a thorough assessment of both data formats and structures. Use data mapping to identify how fields from one system correspond to those in the other. Implement ETL (Extract, Transform, Load) tools to clean and standardize the data into a consistent format. Collaborate with teams from both companies to set clear rules for resolving conflicts, such as duplicate or missing data. Test the merged dataset in a controlled environment to ensure accuracy before full integration. This careful planning ensures smooth alignment and reliable results.