When integrating different systems, the data details are important to understand and mitigate. An example would be integrating an industry specific SaaS application with a CRM-SFA backend which manages the relevant client data. Some key areas to consider when looking at data integration will include:
1. Data Type Mismatches:
- Different Data Representations: A SaaS and backend CRM might represent the same data type (e.g., dates, numbers, currency) in different formats. For example:
- Dates: SaaS might use “YYYY-MM-DD,” while the CRM uses “MM/DD/YYYY.”
- Numbers: SaaS might use decimal points, while the CRM uses commas as separators.
- Data Type Precision: There might be differences in the precision of data types. For example, a number field in the SaaS might allow for more decimal places than the corresponding field in the CRM, leading to data truncation or rounding issues.
2. Data Structure Differences:
- Object Structures: The way data is structured into objects or records might differ between the two systems. For instance:
- The SaaS front end might use nested JSON objects, while most backend CRMs uses a flat table structure.
- The naming conventions for fields or attributes might vary.
- Data Relationships: The way data relationships are defined might be different. For example:
- SaaS might use explicit foreign keys, while the CRM relies on implicit relationships or lookup fields.
3. Data Encoding and Character Sets:
- Character Encoding: SaaS and the CRM might use different character encodings (e.g., UTF-8, ISO-8859-1). This can lead to issues with special characters or accented letters.
- Data Serialization: The way data is serialized (e.g., JSON, XML) might differ. This can cause problems when exchanging data between the systems.
4. Data Validation and Constraints:
- Validation Rules: SaaS and the CRM might have different data validation rules. For example:
- SaaS might enforce a minimum length for a field, while the CRM does not.
- CRM might have specific data constraints (e.g., unique keys, required fields) that the SaaS does not.
- Data Constraints: The way data constraints are defined might be different. For example:
- SaaS might use regular expressions for data validation, while the CRM uses database constraints.
5. Data Transformation and Mapping:
- Data Mapping: The process of mapping data from Instanda to the CRM (or vice versa) can be complex, especially if there are significant differences in data structures or formats.
- Data Transformation: Data might need to be transformed or manipulated to fit the target system’s format. This can involve:
- Converting data types
- Formatting dates or numbers
- Concatenating or splitting strings
- Applying business logic or calculations
Mitigation Strategies:
- API Design and Documentation: Ensure that the APIs used for integration are well-designed and documented, clearly specifying the data formats and structures.
- Data Transformation Tools: Use data transformation tools or middleware to handle data format conversions and mappings.
- Data Validation and Testing: Implement thorough data validation and testing to identify and resolve data format issues.
- Data Governance: Establish data governance policies and procedures to ensure data consistency and quality.