Data Collection & ProcessingPurpose
1.2. Automator system is designed for standardized dataset preparation, reducing human error, and accelerating research cycles.
1.3. Automatedly processes, deduplicates, normalizes, and reforms data structures for statistical and geographic analysis.
1.4. Enables seamless submission to downstream automated visualization and statistical engines within the institute.
Trigger Conditions
2.2. Submission of survey results or research forms via webforms or email.
2.3. Scheduled intervals for recurring research data collection cycles.
2.4. API webhook event from external data providers indicating data availability.
Platform Variants
3.1. Google Sheets
• Feature/Setting: Use Apps Script’s `onEdit` trigger to automate script execution for cell clean-up and format normalization.
• Example: Configure automation to lowercase all entries and remove spaces on data insertion.
3.2. Microsoft Power Automate
• Feature/Setting: Deploy cloud flow with "When a File Is Created" in OneDrive, followed by "Data Operations – Compose" to clean up formatting.
• Example: Add trigger for research data folder and apply automated formula transformations.
3.3. AWS Lambda
• Feature/Setting: Set up automated functions triggered by S3 event uploads; script uses Pandas for data cleaning.
• Example: Automatedly remove duplicates and standardize date fields when new research data lands in S3.
3.4. Zapier
• Feature/Setting: Automate data processing Zap with Google Drive/Dropbox trigger and "Formatter" action.
• Example: Configure an automator to split/trim/convert values on import to master spreadsheet.
3.5. Make (Integromat)
• Feature/Setting: Build automation scenario with HTTP > Data Transformer modules.
• Example: Automated mapping and validation of research fields upon file receipt.
3.6. Talend Data Preparation
• Feature/Setting: Use auto-cleaning jobs running on detection of uploads to Talend datasets.
• Example: Automate normalization of survey columns and format all postal codes.
3.7. Alteryx Designer
• Feature/Setting: Scheduled workflows automating Cleanse, Data Parse, and Formula tools.
• Example: Automated pipeline for research data dropping into “New Submissions” folder.
3.8. Apache NiFi
• Feature/Setting: Automated data flow pipelines ingest files, run cleaning processors, and output validated CSVs.
• Example: Automate transformation on ingest from FTP server where researchers upload data.
3.9. DataRobot Paxata
• Feature/Setting: Automation projects configure Smart Suggestions on flow start.
• Example: Automate deduplication and categorical normalization for raw research data.
3.10. Databricks
• Feature/Setting: Configure scheduled jobs with Python cleaning scripts run on data lake updates.
• Example: Automate null value handling every time research CSVs are updated.
3.11. KNIME
• Feature/Setting: Batch executor automated by folder watcher node, with string manipulation, row filtering, type conversion nodes.
• Example: Automatedly transform all student survey uploads nightly.
3.12. IBM Data Refinery
• Feature/Setting: Auto-run dataflow for new assets in “Research-Intake” project.
• Example: Clean, format, profile, and store data for statistical analysis pipelines.
3.13. Tableau Prep
• Feature/Setting: Automated refresh flows leveraging "Schedule Flow" and "Data Roles".
• Example: Automate data reshape and type correction upon weekly research updates.
3.14. Microsoft Azure Data Factory
• Feature/Setting: Automated pipelines orchestrate data copy, cleaning with Data Flow mapping.
• Example: Automate row filtering and schema fixing on ingestion from SFTP.
3.15. Google Cloud Dataflow
• Feature/Setting: Data pipelines automatically apply validation/transformation steps.
• Example: Automator listens for new records and runs format correction jobs.
3.16. Python Pandas (via Cloud Function)
• Feature/Setting: Automated cloud functions react to file events; run custom scripts for reformatting.
• Example: Automatedly cast columns and drop blanks from every incoming research XLSX.
3.17. Smartsheet
• Feature/Setting: Automated workflows using "Data Shuttle" with auto-mapping/cleaning profiles.
• Example: Automate row cleanup and formula application when files are loaded.
3.18. Qlik Data Integration
• Feature/Setting: Automated task chains set to import, clean, and synchronize research data.
• Example: Automate value standardization triggered by hourly data import job.
3.19. Informatica Cloud Data Integration
• Feature/Setting: Taskflows automate cleansing logic on new file ingestion.
• Example: Automator corrects field types and removes non-printable characters from all new entries.
3.20. SQL Server Integration Services (SSIS)
• Feature/Setting: Automated packages for data import from file system with Expression Task for reformatting.
• Example: Automate trimming of whitespace and patching misaligned columns post-research campaign.
Benefits
4.2. Automates error reduction and ensures consistent research-ready datasets.
4.3. Scalable, automatable workflows support growing data volumes without more staff.
4.4. Automatedly ensures compliance with institutional and statistical standards for data processing.
4.5. Automating data transformation reduces bottlenecks in the educational research value chain.