Data assessment and profiling are critical steps in understanding the characteristics and quality of your data before migrating it to a new system like Salesforce. Here’s a sample template for a Data Assessment and Profiling document:
Sample Data Assessment and Profiling Document
1. Data Overview:
- Data Source: [Name of the existing system]
- Data Type: [Structured, unstructured, semi-structured]
- Data Volume: [Number of records, size of data]
2. Data Entities:
- Firstly, list the main data entities to be assessed. For example, customers, products, and transactions.
3. Data Quality Dimensions:
- Secondly, assess the following data quality dimensions for each data entity:
- Accuracy: How accurate is the data in reflecting the real-world scenario?
- Completeness: To what extent is the data complete?
- Consistency: How consistent is the data across different sources and records?
- Validity: Does the data conform to predefined business rules and constraints?
- Timeliness: Is the data up-to-date and relevant for business needs?
- Integrity: How well is data integrity maintained, especially in relationships between entities?
- Duplication: Are there duplicate records, and how are they managed?
4. Data Profiling:
- Attribute Profiling:
- For each attribute/field, profile the data to understand:
- Data types
- Range of values
- Distribution of values
- Presence of null values
- Cross-Attribute Profiling:
- Explore relationships between different attributes to identify patterns and dependencies.
5. Data Assessment Findings:
- Next, summarize key findings from the data assessment and profiling process.
- Highlight areas of concern or improvement needed.
6. Data Mapping:
- Create a mapping between source data entities as well as the corresponding entities in Salesforce.
- Define mappings for each attribute, including transformations and data cleansing rules.
7. Data Cleansing and Transformation Plan:
- Additionally, document the steps and processes for cleansing and transforming data before migration.
- Specify tools or scripts to be used for data cleansing.
- Define the method for extracting data from the source system.
- Also, specify extraction tools, frequency, and protocols.
9. Validation Rules:
- Establish validation rules to be applied during data migration to ensure data quality.
- Specify rules for identifying and handling exceptions.
10. Data Quality Metrics:
- Define metrics to measure data quality before and after migration.
- Establish thresholds for acceptable data quality levels.
11. Data Governance and Ownership:
- Define roles and responsibilities for data governance during and after migration.
- Identify data owners and stewards for each data entity.
12. Risk Assessment:
- Identify potential risks associated with data migration.
- Develop mitigation strategies for each identified risk.
13. Data Security:
- Outline security measures to protect sensitive data during the migration process.
- Afterwards, specify access controls and encryption methods.
14. Data Backup and Rollback Plan:
- Document the plan for data backup before migration.
- Define the rollback process in case of unforeseen issues during migration.
15. Communication Plan:
- In addition, develop a communication plan to inform stakeholders about the data assessment findings and the migration process.
- Create a timeline for the data assessment, cleansing, and migration phases.
- Lastly, include milestones and deadlines.
Overall, this Data Assessment and Profiling document provides a structured approach to understanding the existing data landscape, identifying potential challenges, and planning for a successful migration to Salesforce or any other system. Adjust the template based on your organization and data’s specific needs and nuances.
Data Cleansing and Transformation Strategy
Sample: Salesforce Needs Assessment
Sample: Data Migration Objectives