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
- For each attribute/field, profile the data to understand:
- 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.
8. Data Extraction Plan:
- 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.
16. Timeline:
- 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.
Related Content
Data Cleansing and Transformation Strategy
Data Mapping
Sample: Salesforce Needs Assessment
Data Migration
Sample: Data Migration Objectives