[Draft] Acceptance Process

Welcome to Octolis' Acceptance Process Help Center page. This guide will walk you through the steps and procedures for ensuring a smooth acceptance of your datasets into Octolis. By following this process, you can ensure that your data is properly configured and meets your expectations before deployment.

How does it work?

The timeline

The Octolis Acceptance Process is designed to ensure the accuracy and reliability of the data integrated into the product. This involves thorough testing, both internally and by our clients, to guarantee that the data is correctly processed and meets predefined specifications.
Configuration Begins
Configuration of data sources initiated.
Global Testing by Product Manager
Comprehensive testing of datasets, including source integration, deduplication, and computed fields.
Internal Quality Assurance
In-house quality assurance to ensure data accuracy.
Client Quality Assurance Workshop
Collaborative workshop with client to validate data accuracy.
Data deployment with synchronization to marketing automation and incremental updates.
Client Quality Assurance
Post-deployment validation by the client.

Step 1️⃣ - Global Testing [W-4]

In this step, a thorough evaluation of the dataset configuration takes place. The primary focus is to verify the following aspects:

1. Successful import of data sources :

Ensuring a successful import of data sources is the cornerstone of the Acceptance Process. This step is pivotal as it lays the foundation for all subsequent processes. During this phase, the following validations occur:
  • Source Verification: Confirm that all specified data sources have been connected to Octolis. Whether it's an API endpoint, a database connection, or a file-based import, each source should be correctly linked.
  • Data Integrity Checks: Assess that the imported data hasn't been corrupted during the transfer. This includes checking for missing values, incorrect data types, or misaligned columns.
  • Timestamps & Sequence: Ensure that time-based data maintains its sequence, which is crucial for time-series analyses and tracking changes over a period.
  • Error Handling: In the event of failed imports, Octolis logs and reports the exact cause, allowing for a swift resolution. It's essential to review these logs to address any issues that might prevent a successful data import.
  • Volume Check: Compare the number of records in the source system with the number of records imported into Octolis to ensure no data is lost during the import process.

2. Alignment of deduplication key with specifications :

The Acceptance Process places great emphasis on verifying the seamless integration of data sources. This entails meticulous checks to ensure that all data sources are successfully imported into Octolis. The goal is to safeguard data integrity and ensure that valuable information is preserved and ready for further processing.

3. Adherence to normalization rules :

Normalization rules are pivotal in establishing data consistency and minimizing redundancy. The Acceptance Process prioritizes adherence to these rules to standardize data. This entails assessing whether data conforms to established normalization guidelines. Normalizing data facilitates accurate analysis and reporting, fostering a dependable and coherent dataset.

4. Consistency of all column formats with client expectations :

Ensuring column format consistency is crucial for meeting client expectations. In the acceptance phase, each column's format is closely examined to match intended presentation and usage. This involves evaluating factors like data types, units, and display styles. Consistent column formats enhance data usability for analysis and decision-making.
  • Column formats : The Acceptance Process evaluates column formats for consistent presentation, including data types and display styles.
  • List values : List values within columns are validated to ensure accurate representation of choices, aiding filtering and reporting.
  • Completion rate : Assessing completion rates, comparing populated entries to expectations, enhances dataset quality and usability for decision-making.

Step 2️⃣ - Internal QA [W-3]

1. Full Configuration Review

During the internal quality assurance phase, a comprehensive assessment of the dataset's configuration is conducted. This review builds upon the global testing stage, ensuring that all aspects are thoroughly scrutinized before advancing. The following points are covered:
  • Addressing any outstanding issues from the global testing phase.
  • Verifying computed fields' alignment with specifications, including the accuracy of formulas and the correct splitting of values.
πŸ’‘ For assistance in this step, you can utilize ChatGPT 4 with the Code Interpreter feature activated. You can upload the table (maximum 100 MB) and instruct GPT to provide completion rates and split values for each column.

2. Client QA Anticipation

Anticipating the client quality assurance phase is a strategic step in the process. This involves proactive measures to facilitate a smoother validation process for the client. The following actions are taken:
  • Importing statistics into Google Sheets to prepare for validation.
  • Identifying three relevant contacts for in-depth testing, ensuring thorough coverage of potential use cases.
  • Ensuring consistency and accuracy of statistics to minimize discrepancies during client validation.

Step 3️⃣ - Client QA [W-2]

1. Checklist for Key Points

To streamline the client quality assurance phase, a checklist of key points is provided. This checklist serves as a reference for clients to systematically validate crucial aspects of the dataset, ensuring a thorough assessment and minimizing oversight.

2. Statistics Review

Clients have the flexibility to review statistics either directly through the Octolis Product or by utilizing Google Sheets. This accessibility empowers them to evaluate the data in their preferred environment, enabling efficient validation.

3. Selecting Detailed Analysis Contacts

During the client quality assurance phase, customers are guided on selecting three contacts for more detailed analysis. These contacts serve as representative cases for a comprehensive assessment of data accuracy and integrity. By focusing on these contacts, clients can delve into specific scenarios, identifying potential discrepancies and addressing them effectively.


What if I want to test with more than 3 contacts?

Octolis supports clients who wish to analyze more than 3 contacts.
While we can assist in identifying additional "relevant contacts," the responsibility for analyzing these contacts and ensuring consistency lies with the client. Our standard QA process is based on analyzing three relevant contacts, which is generally sufficient to identify most issues.
Thank you for choosing Octolis. We are committed to ensuring the quality and accuracy of your integrated datasets. If you have any further questions or require assistance, please don't hesitate to contact our support team.

This Acceptance Process guide aims to provide you with a clear framework for validating your datasets within Octolis. Following these steps will contribute to a seamless integration process and a data-rich experience. If you have any questions or require additional support, please refer to our Contact Us page.