Cleaning Dirty Data in Salesforce
Data Analysis and Assessment
- Review the Salesforce data fields and identify any fields that contain inconsistent or incorrect data.
- Analyze the data quality issues by examining the frequency and severity of errors in each identified field.
- Clearly define the objectives for cleaning the data, such as improving accuracy, completeness, or consistency.
- Identify the desired outcome, such as having reliable data for reporting and analysis purposes.
- Evaluate how the dirty data affects business operations, such as customer relationship management or sales forecasting.
- Analyze the impact of the data quality issues on decision-making processes, such as inaccurate sales projections or ineffective marketing campaigns.
- Identify the specific data cleansing rules that need to be applied to the data
- Determine the thresholds for data quality that need to be met
- Create a detailed plan outlining the steps and timeline for cleaning the data
- Review the data to identify any duplicate records or overlapping data
- Identify any redundant data fields that can be consolidated into a single field
- Create a list of the areas of overlap or redundancies that need to be addressed
- Review the different data sources to determine their accuracy
- Assess the completeness of the data in each source
- Check for consistency in the data across different sources
- Use data analysis techniques to identify trends in the data
- Look for any unusual or unexpected patterns in the data
- Identify any anomalies or outliers that need to be further investigated
- Compare the different data fields to identify any discrepancies
- Look for missing information in the data fields
- Create a mapping document that shows the relationships between the data fields
- Define data quality metrics that can be used to measure the accuracy of the data
- Establish key performance indicators (KPIs) that can be used to monitor data quality
- Create a system for regularly tracking and reporting on data quality metrics and KPIs
Data Deduplication
- Review the criteria for identifying duplicate records, such as matching email addresses or names.
- Use Salesforce's built-in duplicate management feature or a third-party deduplication tool to identify duplicates.
- Review the potential duplicate records and compare them to determine which ones should be merged.
- Merge the duplicate records by selecting the primary record and merging it with the duplicate records.
- Review the duplicate entries in the contact, lead, and account records.
- Update the duplicate entries with the correct information, if applicable.
- Remove the duplicate entries that are unnecessary or duplicate the information in other records.
- Evaluate the available data deduplication tools and choose the most suitable one for your needs.
- Configure and run the data deduplication tool to identify and consolidate duplicate records.
- Review the results of the data deduplication tool and manually verify the duplicates before merging or removing them.
- Execute the necessary actions to consolidate and eliminate the identified duplicate records.
Standardization and Formatting
- Remove leading/trailing spaces and special characters from data values.
- Convert all data values to a consistent format (e.g., lowercase, uppercase, title case).
- Replace synonyms or similar terms with a standard term.
- Map different variations of the same value to a single value.
- Create a style guide that outlines the preferred naming conventions, capitalization rules, and abbreviations to be used.
- Review and update existing data values to match the established conventions.
- Perform automated checks to identify and correct inconsistencies in capitalization and abbreviations.
- Remove any special characters, spaces, or dashes from phone numbers.
- Ensure all phone numbers have a consistent format (e.g., +1 (123) 456-7890 or 123-456-7890).
- Standardize address formats (e.g., street, city, state, ZIP) and remove any inconsistencies.
- Validate and correct contact information using external data sources or APIs.
Validation and Verification
Data Enrichment
- Cross-reference existing data with external data sources to fill in missing information.
- Use data cleansing techniques to correct any inaccuracies or inconsistencies in the existing data.
- Identify reputable third-party data providers or internal sources that have relevant and accurate data.
- Integrate these data sources into the existing Salesforce system.
- Pull data from these sources and match it with existing records to enrich the data.
- Regularly review the existing data for outdated or incorrect information.
- Cross-reference this data with reliable sources to verify its accuracy.
- Update the outdated or incorrect data with the accurate and relevant information.
- Implement machine learning algorithms that can analyze the data and identify any anomalies or errors.
- Train these algorithms to clean and validate the data by comparing it with known patterns or rules.
- Automate the process of running these algorithms periodically to ensure data quality.
- Apply natural language processing techniques to unstructured data such as customer feedback or social media posts.
- Use NLP algorithms to extract relevant information, sentiments, or topics from this data.
- Organize and structure this extracted information to enrich the existing data.
- Identify the data enrichment processes that can be automated.
- Develop or implement tools or scripts that can perform these processes automatically.
- Set up schedules or triggers to run these automated processes at regular intervals.
- Utilize visualization tools or libraries to create visual representations of the data patterns and outliers.
- Analyze these visualizations to gain insights into the data and identify any unusual or significant patterns.
- Use these insights to further enhance the data enrichment process.
Data Integrity and Governance
Testing and Quality Assurance
Documentation and Reporting
- Create a document to record the steps taken during the data cleaning process.
- Include details about the tools used for cleaning the data in Salesforce.
- Create reports that track the progress of the data cleaning process.
- Measure the improvements made in data quality.
- Identify any remaining issues that need to be addressed.
- Document any data quality issues encountered during the data cleaning process.
- Provide recommendations for future data management to prevent similar issues.
- Create audit logs to track data changes made during the cleaning process.
- Ensure that the data changes are accurate and comply with relevant regulations.
- Publish the findings from the data cleaning process.
- Report the findings back to the stakeholders involved in the project.
- Create data dictionaries that standardize the definitions of different data fields.
- Include explanations and descriptions of each data field for better understanding.
- Establish a process to review and approve any data changes made during the cleaning process.
- Ensure that data changes are reviewed and approved by relevant stakeholders before implementation.
Use in Manifestly