Enhancing Data Quality with Automated QA in Unica Campaign
Enhancing Data Quality with Automated QA in Unica Campaign
Introduction
Implementing automated QA checks for flowcharts and processes in Unica Campaign can substantially enhance the accuracy and efficiency of your marketing initiatives. While Unica Campaign is primarily designed for digital marketing, it can be leveraged to automate critical tasks such as quality assurance (QA) and Extract, Transform, Load (ETL) processes. This guide focuses on automated QA within Unica Campaign.
Unica Campaign flowcharts can be configured to set up a robust QA process, ensuring uniform data quality across all campaigns. Data quality is crucial for any organization as it ensures that data is accurate, reliable, and useful for decision-making processes. Before jumping into the guide, lets explore the six dimensions of data quality: accuracy, completeness, consistency, timeliness, validity, and uniqueness. Let’s explore each dimension in detail with examples.
The Six Dimensions of Data Quality
1. Accuracy
Accuracy refers to the extent to which data correctly reflects the real-world entities it is intended to represent. Accurate data ensures that decisions based on the data are correct and reliable. For instance, inaccurate customer contact details can lead to communication failures and poor customer service.
Example: If a customer database contains a record with a customer’s ID number as “1234567890”, accuracy means that this number is indeed the correct ID number for that customer. An inaccurate entry might have the ID number as “A123456_7888”.
2. Completeness
Completeness refers to the extent to which all required data is available. Incomplete data can result in gaps in information, leading to incomplete analysis and potentially flawed decisions. For example, missing customer contact information can hinder marketing campaigns.
Example: In an employee database, completeness means having all essential fields filled out for each employee, such as name, address, phone number, and job title. If an employee’s phone number or address is missing, the record is incomplete.
3. Consistency
Consistency means that data is uniform and coherent across different datasets and systems. Consistent data avoids confusion and ensures reliable data integration and analysis. For example, inconsistent product pricing across different sales channels can lead to customer distrust and lost sales.
Example: If a customer’s address is “123 Main St” in one database and “123 Main Street” in another, this inconsistency can cause issues. Both should match exactly to ensure consistency.
4. Timeliness
Timeliness refers to how up to date and available the data is when needed. Timely data allows for prompt decision-making and actions. For example, real-time inventory data is crucial for effective supply chain management.
Example: Financial reports generated at the end of the fiscal year must reflect the most current financial status. If these reports are delayed by several months, the data becomes outdated and less useful.
5. Validity
Validity ensures that data conforms to the defined formats, rules, and requirements. Valid data maintains the integrity of the database and ensures that the data can be used effectively without errors. Invalid entries can lead to processing errors and faulty analysis.
Example: A valid email address should follow the format “example@domain.com”. An entry like “example@domain” would be invalid.
6. Uniqueness
Uniqueness means that each record is unique and not duplicated within the dataset. Unique data avoids duplication, which can lead to inflated statistics and misinformed decisions. For instance, duplicate customer records can result in sending multiple marketing messages to the same individual, which can be annoying and unprofessional.
Example: In a customer database, each customer should have a unique identifier, such as a customer ID. If the same customer appears multiple times with different IDs, it indicates a lack of uniqueness.
Automated QA Checks Using Unica Campaign Flowcharts
Before developing a QA process within Unica, determine what aspects of the flowcharts and processes you want to validate, such as data accuracy, consistency, and uniqueness.
Example Use Case: The marketing operations team at Munvo is responsible for building Unica flowcharts based on business requirements, generating a deployment file, and dropping it on an SFTP drive for deployment by an ESP.
The third-party email service provider has a specific format that needs to be followed by the campaign developers at Munvo. For example, the final file must have 10 columns in a specific order (INDIV_ID, Name, Email, etc.). The unique identifier must be a 9digit number starting with 9. The email address must be correctly formatted (@, .com, .ca, no spaces, etc.), and none of the columns should be null or blank.
To ensure the final data follows these rules, develop an automated validation system within Unica flowcharts (potentially using templates) to automatically validate and fail if one of those conditions is not met. A notification message will then be sent to the team, alerting them that the flowchart failed. The campaign developer can then fix the issue before the file is sent to the third party, avoiding ingestion failures or incorrect data deployment.
Implementing Automated QA Checks:
Configure a Derived Field before the MailList process box to validate the extracted records based on certain conditions before generating a file or recording any logs into Contact History.
Example:
- Ensure the INDIV_ID is less than 9 characters and starts with a 9.
- Ensure the Gender column has F or M.
- Ensure there are no minors in the records.
Automated Notifications: Use a custom-made script to send automated notifications to recipients, alerting them about errors within the flowchart. This is useful for scheduled flowcharts that run automatically based on a schedule. You can select the notification in the flowchart’s advanced settings in front of “Send trigger(s) on flowchart run error” section.
You can also create a customized message within the flowchart using UserVariables to be passed on to the notification script. This can include personalized metadata such as the number of records, dates, or other information such as the subject line, and recipients of the message.
Advanced QA Checks:
You can also create a QA check that runs through the data files and generates a detailed report based on specific criteria. For example, to ensure that all relevant inclusions and exclusions have been applied to the final file, create a QA template that ingests the final file, validates it, and generates a report.
Implementation Steps:
- Determine all possible inclusions and exclusions frequently used within most flowcharts. Create a standardized list and set aside corner cases.
- Categorize inclusions and exclusions as strategic segments based on lines of business or communication channels (Email, DM, SMS, Push, Call, etc.).
- Create a flowchart to perform the QA check based on the final file as its input. This could become a template that could be re-used by all team members as needed.
The report generated can highlight anomalies in the final data file, indicating that some records haven’t been properly scrubbed. This could be due to the out-of-date strategic segments or still running ETLs at the time of flowchart run or simply due to running portions of the flowchart at different times. This report is essential for ensuring correct targeting within the marketing operations team.
By following these guidelines, you can significantly enhance the data quality within Unica Campaign, ensuring more accurate, reliable, and efficient marketing operations.
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As Unica Campaign experts, we can help you achieve top-notch campaign accuracy and efficiency. Elevate your marketing strategy with automated QA in Unica Campaign! Discover more on our Unica Strategic Partnership page and get in touch today!
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