Data Extraction and Management by Logictive Solutions
This document details the structure, execution, challenges, and outcomes of the Volt AI project, focusing on the data entry and quality assurance (QA) workflows designed to extract and validate highly technical web data.
Services We Provided
Project Overview and Scope
The core of this project involved replicating and transforming complex product specification data from a primary source into a structured, client-ready CSV format. Effective workflow management and stringent quality controls were paramount to the project's success, ensuring both accuracy and timely delivery.
Extraction focused on product listings and technical documentation from the STMicroelectronics website, requiring navigation through complex technical pages.
All extracted data was consolidated into specific Google Sheets, organized to serve the client's subsequent analytical and operational needs.
The two primary data points extracted and validated were Packaging Details and detailed Pin Function specifications for each listed product.
The primary challenge in the scope was not merely data replication, but ensuring the translation of highly technical documentation into a standardized, usable digital format while adhering to specific client formatting guidelines.
Strategic Objectives & Success Metrics
The project was governed by two critical strategic objectives. These targets drove the design of our internal workflows and defined our measure of success beyond just the raw output volume. The emphasis was on reliability and utility for the client's subsequent operations.
To ensure efficient operations and clear communication across internal teams and the client, a reliable dual-tracker system was essential for monitoring progress, workload distribution, and quality metrics in real-time.
Deliver Client-Ready Data in CSV FormatThe ultimate goal was to successfully manage and migrate all required product data into a structured CSV format, ready for immediate use by the client's internal systems and applications.
Achieving these objectives required rigorous attention to detail and clear process documentation, setting the stage for the operational phase.
Operational Approach and Team Workflow
Our approach was designed for simplicity and accuracy: replicate and refine. The core task involved migrating a specific data table from the source document to a Google Sheet. Crucially, the data could not be copied directly; minor modifications were required based on client-provided transformation guidelines.
Internal Process Breakdown
Team DistributionA large, skilled team with dedicated QA and project management units at Logictive Solutions. The team was split into two functions: Workers (20 members) focused on data entry and migration, and QA Specialists (2 members) responsible for quality control.
Workers were required to update progress on two parallel systems: the Logictive Tracker (internal) and the Client's Tracker/Notion (external), ensuring transparent accountability.
Data MigrationTeam members transferred the processed data into the designated client-side sheets, adhering strictly to the required structure and formatting rules.
Quality and Compliance ReviewUpon task completion, QA specialists reviewed the migrated output, ensuring data quality and compliance. QA was also responsible for cross-checking both Logictive and Client progress trackers to maintain deadline integrity.
Key Challenges Encountered
Despite a clear operational structure, the highly specialized nature of the data led to recurring challenges, particularly related to ambiguous technical instances and maintaining client confidence in the early stages.
As work progressed, numerous unique instances were discovered within the source documents that did not fit previous guidelines, necessitating frequent requests for client clarification.
The client expressed concern regarding clients progress report not being updated accordingly. This resulted in extensive back-and-forth communication, increasing friction and slowing the overall workflow.
Challenge Mitigation: The Q&A Document Solution
To address the high communication friction and standardize responses to ambiguous data instances, a structured Q&A document was implemented. This tool became the single source of truth for all complex data entry decisions.
The Q&A document served as a dynamic knowledge base, significantly streamlining the process by replacing ad-hoc email chains with a centralized, searchable repository of guidelines. This immediately reduced the volume of direct client communication required for routine exceptions.
Team members log new, ambiguous data cases.
New instance is recorded in the Q&A Document.
Client provides official clarification/guideline.
New rule is applied to all current and future tasks.
Project Outcomes and Delivered Results
The workflow, despite early challenges, proved resilient. The implementation of strict QA protocols and the Q&A documentation system ensured a high-quality final deliverable, meeting all client specifications and timelines.
We successfully migrated all designated data into the respective sheets, delivering a complete dataset containing both Pin and Ordering Information in the required CSV format.
100%
Data Delivery SuccessAll data sets were successfully migrated and delivered according to client specifications.
0
Missed DeadlinesThe project maintained a smooth pace, meeting every specified deadline throughout the entire lifecycle.
2
Key DeliverablesPin Function Data and Ordering Information Sheets were finalized and delivered.
Lessons Learned: Successes and Areas for Improvement
The project provided valuable insights into managing complex, detail-oriented data entry workflows. While the team excelled in adherence to deadlines, the QA process highlighted recurring patterns of error that suggest opportunities for procedural enhancement.
What Went Well
The project maintained a smooth pace, consistently meeting all timeline expectations and deadlines.
The adoption of the Q&A document effectively managed instance clarification.
Areas for Improvement
High Error Volume: QA observed a recurring number of errors related to similar types of mistakes across multiple team members.
Lack of Team Awareness: Insufficient communication led to repeated errors, indicating a need for greater team-wide awareness of common pitfalls.
Lessons Learned: Successes and Areas for Improvement
Moving forward, we can enhance efficiency and accuracy through targeted improvements in communication, resource allocation, and exploring technological integration.
Ensuring Feedback
As there was back and fourth feedback, the QA specialist needs to check updates and entire team needs to focus on how they need to work according to the feedback.
Active Team Communicationk
Common errors spotted during QA must be communicated openly and immediately with the entire team, not just the individual, to foster collective learning and prevent recurrence.
Optimized QA Role Division
To address the common mistake of one of the trackers not being updated, the two QA specialists should divide the progress report checking role: one responsible for the Logictive tracker and the other for the Client's Notion tracker.
Executive Summary and Conclusion
The Volt AI Data Validation Project successfully met its core objectives, demonstrating the team's capability to deliver high-volume, high-accuracy technical data within tight deadlines. The successful implementation of the Q&A document was key to overcoming early communication bottlenecks.
Key Takeaways for Stakeholders
Stability & Delivery
Established a stable operational tempo, resulting in 100% on-time delivery of technical product data.
Process Standardization
The Q&A document proved to be a critical control mechanism for standardizing interpretation of complex data instances.
Future Efficiency
Identified clear pathways for future efficiency gains through targeted QA role specialization and potential automation integration.
By incorporating the lessons on proactive communication and task specialization, future data entry and QA projects can achieve even higher levels of both operational efficiency and data integrity.